Sample records for wrf model output

  1. Air pollution forecasting by coupled atmosphere-fire model WRF and SFIRE with WRF-Chem

    E-Print Network [OSTI]

    Kochanski, Adam K; Mandel, Jan; Clements, Craig B

    2013-01-01T23:59:59.000Z

    Atmospheric pollution regulations have emerged as a dominant obstacle to prescribed burns. Thus, forecasting the pollution caused by wildland fires has acquired high importance. WRF and SFIRE model wildland fire spread in a two-way interaction with the atmosphere. The surface heat flux from the fire causes strong updrafts, which in turn change the winds and affect the fire spread. Fire emissions, estimated from the burning organic matter, are inserted in every time step into WRF-Chem tracers at the lowest atmospheric layer. The buoyancy caused by the fire then naturally simulates plume dynamics, and the chemical transport in WRF-Chem provides a forecast of the pollution spread. We discuss the choice of wood burning models and compatible chemical transport models in WRF-Chem, and demonstrate the results on case studies.

  2. Coupling the High Complexity Land Surface Model ACASA to the Mesoscale Model WRF

    E-Print Network [OSTI]

    Xu, L.

    In this study, the Weather Research and Forecasting Model (WRF) is coupled with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high complexity land surface model. Although WRF is a state-of-the-art regional ...

  3. Verification of the WRF model during a high ozone event over Houston, TX

    E-Print Network [OSTI]

    Ames, Douglas Seeley

    2003-01-01T23:59:59.000Z

    was more accurate at night, predicting the height of the boundary layer, cloud cover, the nocturnal low-level jet, and profiler winds in the lower levels. Multiple simulations of the WRF model were also run to test the sensitivity of the model to different...

  4. The Lagrangian particle dispersion model FLEXPART-WRF VERSION 3.1

    SciTech Connect (OSTI)

    Brioude, J.; Arnold, D.; Stohl, A.; Cassiani, M.; Morton, Don; Seibert, P.; Angevine, W. M.; Evan, S.; Dingwell, A.; Fast, Jerome D.; Easter, Richard C.; Pisso, I.; Bukhart, J.; Wotawa, G.

    2013-11-01T23:59:59.000Z

    The Lagrangian particle dispersion model FLEXPART was originally designed for cal- culating long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant. In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis at different scales. This multiscale need from the modeler community has encouraged new developments in FLEXPART. In this document, we present a version that works with the Weather Research and Forecasting (WRF) mesoscale meteoro- logical model. Simple procedures on how to run FLEXPART-WRF are presented along with special options and features that differ from its predecessor versions. In addition, test case data, the source code and visualization tools are provided to the reader as supplementary material.

  5. Simulation of Aerosol-Cloud Interactions in the WRF Model at the Southern Great Plains Site

    E-Print Network [OSTI]

    Vogel, Jonathan 1988-

    2012-08-21T23:59:59.000Z

    for his assistance early on with getting me starting using the WRF model and helping me fix and debug problems with the model as they arose. At BNL, I would like to thank Dr. Yangang Liu and Dr. Wuyin Lin for their help pertaining to my questions... with 95% confidence intervals for cloud, rain, and ice water .................................... 76 19 Average vertical velocity in cloudy regions over the entire time period for Case A with 95% confidence intervals for the strongest updraft...

  6. Surface energy partitioning over four dominant vegetation types across the United States in a coupled regional climate model

    E-Print Network [OSTI]

    Kueppers, Lara M.

    Slopes Model (PRISM) data) and to standard WRF model output. We found that WRF3-CLM3.5 can capture and Forecasting Model 3≠Community Land Model 3.5 (WRF3-CLM3.5), by comparing model output to observations (Ameri simulation of downward solar radiation could reduce the energy flux and temperature biases. After adding

  7. Regional Modeling of Dust Mass Balance and Radiative Forcing over East Asia using WRF-Chem

    SciTech Connect (OSTI)

    Chen, Siyu; Zhao, Chun; Qian, Yun; Leung, Lai-Yung R.; Huang, J.; Huang, Zhongwei; Bi, Jianrong; Zhang, Wu; Shi, Jinsen; Yang, Lei; Li, Deshuai; Li, Jinxin

    2014-12-01T23:59:59.000Z

    The Weather Research and Forecasting model with Chemistry (WRF-Chem) is used to investigate the seasonal and annual variations of mineral dust over East Asia during 2007-2011, with a focus on the dust mass balance and radiative forcing. A variety of measurements from in-stu and satellite observations have been used to evaluate simulation results. Generally, WRF-Chem reproduces not only the column variability but also the vertical profile and size distribution of mineral dust over and near the dust source regions of East Asia. We investigate the dust lifecycle and the factors that control the seasonal and spatial variations of dust mass balance and radiative forcing over the seven sub-regions of East Asia, i.e. source regions, the Tibetan Plateau, Northern China, Southern China, the ocean outflow region, and Korea-Japan regions. Results show that, over the source regions, transport and dry deposition are the two dominant sinks. Transport contributes to ~30% of the dust sink over the source regions. Dust results in a surface cooling of up to -14 and -10 W m-2, atmospheric warming of up to 20 and 15 W m-2, and TOA cooling of -5 and -8 W m-2 over the two major dust source regions of East Asia, respectively. Over the Tibetan Plateau, transport is the dominant source with a peak in summer. Over identified outflow regions, maximum dust mass loading in spring is contributed by the transport. Dry and wet depositions are the comparably dominant sinks, but wet deposition is larger than dry deposition over the Korea-Japan region, particularly in spring (70% versus 30%). The WRF-Chem simulations can generally capture the measured features of dust aerosols and its radaitve properties and dust mass balance over East Asia, which provides confidence for use in further investigation of dust impact on climate over East Asia.

  8. Development and validation of a hurricane nature run using the joint OSSE nature run and the WRF model

    E-Print Network [OSTI]

    Nolan, David S.

    model David S. Nolan,1 Robert Atlas,2 Kieran T. Bhatia,1 and Lisa R. Bucci3 Received 6 March 2013 model (WRF), embedded within the Joint OSSE global nature run previously generated by the European observations. These include the pressure-wind relationship, the kinematic and thermodynamic structure

  9. Effects of vegetation and soil moisture on the simulated land surface processes from the coupled WRF/Noah model

    E-Print Network [OSTI]

    Small, Eric

    simulations. Meso- scale models, which have been used not only for numerical weather prediction but also surface and atmosphere into numerical weather or climate prediction. This study describes coupled WRF [Chen et al., 1997; Pielke et al., 1997]. Numerical weather prediction with high spatial and tempo- ral

  10. Aerosol Indirect Effect on the Grid-scale Clouds in the Two-way Coupled WRF-CMAQ: Model Description, Development, Evaluation and Regional Analysis

    SciTech Connect (OSTI)

    Yu, Shaocai; Mathur, Rohit; Pleim, Jonathan; Wong, David; Gilliam, R.; Alapaty, Kiran; Zhao, Chun; Liu, Xiaohong

    2014-10-24T23:59:59.000Z

    This study implemented first, second and glaciations aerosol indirect effects (AIE) on resolved clouds in the two-way coupled WRF-CMAQ modeling system by including parameterizations for both cloud drop and ice number concentrations on the basis of CMAQpredicted aerosol distributions and WRF meteorological conditions. The performance of the newly-developed WRF-CMAQ model, with alternate CAM and RRTMG radiation schemes, was evaluated with the observations from the CERES satellite and surface monitoring networks (AQS, IMPROVE, CASTNet, STN, and PRISM) over the continental U.S. (CONUS) (12-km resolution) and eastern Texas (4-km resolution) during August and September of 2006. The results at the AQS surface sites show that in August, the NMB values for PM2.5 over the eastern/western U.S (EUS/WUS) and western U.S. (WUS) are 5.3% (?0.1%) and 0.4% (-5.2%) for WRF-CMAQ/CAM (WRF-CMAQ/RRTMG), respectively. The evaluation of PM2.5 chemical composition reveals that in August, WRF-CMAQ/CAM (WRF-CMAQ/RRTMG) consistently underestimated the observed SO4 2? by -23.0% (-27.7%), -12.5% (-18.9%) and -7.9% (-14.8%) over the EUS at the CASTNet, IMPROVE and STN sites, respectively. Both models (WRF-CMAQ/CAM, WRF-CMAQ/RRTMG) overestimated the observed mean OC, EC and TC concentrations over the EUS in August at the IMPROVE sites. Both models generally underestimated the cloud field (SWCF) over the CONUS in August due to the fact that the AIE on the subgrid convective clouds was not considered when the model simulations were run at the 12 km resolution. This is in agreement with the fact that both models captured SWCF and LWCF very well for the 4-km simulation over the eastern Texas when all clouds were resolved by the finer domain. Both models generally overestimated the observed precipitation by more than 40% mainly because of significant overestimation in the southern part of the CONUS in August. The simulations of WRF-CMAQ/CAM and WRF-CMAQ/RRTMG show dramatic improvements for SWCF, LWCF, COD, cloud fractions and precipitation over the ocean relative to those of WRF default cases in August. The model performance in September is similar to that in August except for greater overestimation of PM2.5 due to the overestimations of SO4 2-, NH4 +, NO3 -, and TC over the EUS, less underestimation of clouds (SWCF) over the land areas due to about 10% lower SWCF values and less convective clouds in September.

  11. Modeling Multi Output Filtering Effects in PCMOS

    E-Print Network [OSTI]

    Mooney, Vincent

    Modeling Multi Output Filtering Effects in PCMOS Anshul Singh*, Arindam Basu, Keck-Voon Ling, Nanyang Technological University (NTU), Singapore *NTU-Rice Institute of Sustainable and Applied Infodynamics (ISAID), NTU, Singapore $School of Computer Engineering, NTU, Singapore §School of ECE, Georgia

  12. Impacts of WRF Physics and Measurement Uncertainty on California Wintertime Model Wet Bias

    SciTech Connect (OSTI)

    Chin, H S; Caldwell, P M; Bader, D C

    2009-07-22T23:59:59.000Z

    The Weather and Research Forecast (WRF) model version 3.0.1 is used to explore California wintertime model wet bias. In this study, two wintertime storms are selected from each of four major types of large-scale conditions; Pineapple Express, El Nino, La Nina, and synoptic cyclones. We test the impacts of several model configurations on precipitation bias through comparison with three sets of gridded surface observations; one from the National Oceanographic and Atmospheric Administration, and two variations from the University of Washington (without and with long-term trend adjustment; UW1 and UW2, respectively). To simplify validation, California is divided into 4 regions (Coast, Central Valley, Mountains, and Southern California). Simulations are driven by North American Regional Reanalysis data to minimize large-scale forcing error. Control simulations are conducted with 12-km grid spacing (low resolution) but additional experiments are performed at 2-km (high) resolution to evaluate the robustness of microphysics and cumulus parameterizations to resolution changes. We find that the choice of validation dataset has a significant impact on the model wet bias, and the forecast skill of model precipitation depends strongly on geographic location and storm type. Simulations with right physics options agree better with UW1 observations. In 12-km resolution simulations, the Lin microphysics and the Kain-Fritsch cumulus scheme have better forecast skill in the coastal region while Goddard, Thompson, and Morrison microphysics, and the Grell-Devenyi cumulus scheme perform better in the rest of California. The effect of planetary boundary layer, soil-layer, and radiation physics on model precipitation is weaker than that of microphysics and cumulus processes for short- to medium-range low-resolution simulations. Comparison of 2-km and 12-km resolution runs suggests a need for improvement of cumulus schemes, and supports the use of microphysics schemes in coarser-grid applications.

  13. WRF-Var implementation for data assimilation experimentation at MIT

    E-Print Network [OSTI]

    Williams, John K. (John Kenneth)

    2008-01-01T23:59:59.000Z

    The goal of this Masters project is to implement the WRF model with 3D variational assimilation (3DVAR) at MIT. A working version of WRF extends the scope of experimentation to mesoscale problems in both real and idealized ...

  14. Sea Ice Enhancements to Polar WRF* Keith M. Hines1**

    E-Print Network [OSTI]

    Howat, Ian M.

    covering Europe and the Arctic Ocean demonstrate remote impacts of Arctic sea ice thickness on18 midSea Ice Enhancements to Polar WRF* Keith M. Hines1** , David H. Bromwich,1,2 , Lesheng Bai1 model (Polar WRF), a polar-optimized version of2 WRF, is developed by and available to the community

  15. Gaseous Chemistry and Aerosol Mechanism Developments for Version 3.5.1 of the Online Regional Model, WRF-Chem

    SciTech Connect (OSTI)

    Archer-Nicholls, Scott; Lowe, Douglas; Utembe, Steve; Allan, James D.; Zaveri, Rahul A.; Fast, Jerome D.; Hodnebrog, Oivind; Denier van der Gon, Hugo; McFiggans, Gordon

    2014-11-08T23:59:59.000Z

    We have made a number of developments in the regional coupled model WRF-Chem, with the aim of making the model more suitable for prediction of atmospheric composition and of interactions between air quality and weather. We have worked on the European domain, with a particular focus on making the model suitable for the study of night time chemistry and oxidation by the nitrate radical in the UK atmosphere. A reduced form of the Common Reactive Intermediates gas-phase chemical mechanism (CRIv2-R5) has been implemented to enable more explicit simulation of VOC degradation. N2O5 heterogeneous chemistry has been added to the existing sectional MOSAIC aerosol module, and coupled to both the CRIv2-R5 and existing CBM-Z gas phase scheme. Modifications have also been made to the sea-spray aerosol emission representation, allowing the inclusion of primary organic material in sea-spray aerosol. Driven by appropriate emissions, wind fields and chemical boundary conditions, implementation of the different developments is illustrated in order to demonstrate the impact that these changes have in the North-West European domain. These developments are now part of the freely available WRF-Chem distribution.

  16. Assessing the CAM5 Physics Suite in the WRF-Chem Model: Implementation, Resolution Sensitivity, and a First Evaluation for a Regional Case Study

    SciTech Connect (OSTI)

    Ma, Po-Lun; Rasch, Philip J.; Fast, Jerome D.; Easter, Richard C.; Gustafson, William I.; Liu, Xiaohong; Ghan, Steven J.; Singh, Balwinder

    2014-05-06T23:59:59.000Z

    A suite of physical parameterizations (deep and shallow convection, turbulent boundary layer, aerosols, cloud microphysics, and cloud fraction) from the global climate model Community Atmosphere Model version 5.1 (CAM5) has been implemented in the regional model Weather Research and Forecasting with chemistry (WRF-Chem). A downscaling modeling framework with consistent physics has also been established in which both global and regional simulations use the same emissions and surface fluxes. The WRF-Chem model with the CAM5 physics suite is run at multiple horizontal resolutions over a domain encompassing the northern Pacific Ocean, northeast Asia, and northwest North America for April 2008 when the ARCTAS, ARCPAC, and ISDAC field campaigns took place. These simulations are evaluated against field campaign measurements, satellite retrievals, and ground-based observations, and are compared with simulations that use a set of common WRF-Chem Parameterizations. This manuscript describes the implementation of the CAM5 physics suite in WRF-Chem provides an overview of the modeling framework and an initial evaluation of the simulated meteorology, clouds, and aerosols, and quantifies the resolution dependence of the cloud and aerosol parameterizations. We demonstrate that some of the CAM5 biases, such as high estimates of cloud susceptibility to aerosols and the underestimation of aerosol concentrations in the Arctic, can be reduced simply by increasing horizontal resolution. We also show that the CAM5 physics suite performs similarly to a set of parameterizations commonly used in WRF-Chem, but produces higher ice and liquid water condensate amounts and near-surface black carbon concentration. Further evaluations that use other mesoscale model parameterizations and perform other case studies are needed to infer whether one parameterization consistently produces results more consistent with observations.

  17. Ecosystem feedbacks to climate change in California: Development, testing, and analysis using a coupled regional atmosphere and land-surface model (WRF3-CLM3.5)

    SciTech Connect (OSTI)

    Subin, Z.M.; Riley, W.J.; Kueppers, L.M.; Jin, J.; Christianson, D.S.; Torn, M.S.

    2010-11-01T23:59:59.000Z

    A regional atmosphere model [Weather Research and Forecasting model version 3 (WRF3)] and a land surface model [Community Land Model, version 3.5 (CLM3.5)] were coupled to study the interactions between the atmosphere and possible future California land-cover changes. The impact was evaluated on California's climate of changes in natural vegetation under climate change and of intentional afforestation. The ability of WRF3 to simulate California's climate was assessed by comparing simulations by WRF3-CLM3.5 and WRF3-Noah to observations from 1982 to 1991. Using WRF3-CLM3.5, the authors performed six 13-yr experiments using historical and future large-scale climate boundary conditions from the Geophysical Fluid Dynamics Laboratory Climate Model version 2.1 (GFDL CM2.1). The land-cover scenarios included historical and future natural vegetation from the Mapped Atmosphere-Plant-Soil System-Century 1 (MC1) dynamic vegetation model, in addition to a future 8-million-ha California afforestation scenario. Natural vegetation changes alone caused summer daily-mean 2-m air temperature changes of -0.7 to +1 C in regions without persistent snow cover, depending on the location and the type of vegetation change. Vegetation temperature changes were much larger than the 2-m air temperature changes because of the finescale spatial heterogeneity of the imposed vegetation change. Up to 30% of the magnitude of the summer daily-mean 2-m air temperature increase and 70% of the magnitude of the 1600 local time (LT) vegetation temperature increase projected under future climate change were attributable to the climate-driven shift in land cover. The authors projected that afforestation could cause local 0.2-1.2 C reductions in summer daily-mean 2-m air temperature and 2.0-3.7 C reductions in 1600 LT vegetation temperature for snow-free regions, primarily because of increased evapotranspiration. Because some of these temperature changes are of comparable magnitude to those projected under climate change this century, projections of climate and vegetation change in this region need to consider these climate-vegetation interactions.

  18. Community Climate System Model (CCSM) Experiments and Output Data

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    The CCSM web makes the source code of various versions of the model freely available and provides access to experiments that have been run and the resulting output data.

  19. On-line Chemistry within WRF: Description and Evaluation of a State-of-the-Art Multiscale Air Quality and Weather Prediction Model

    SciTech Connect (OSTI)

    Grell, Georg; Fast, Jerome D.; Gustafson, William I.; Peckham, Steven E.; McKeen, Stuart A.; Salzmann, Marc; Freitas, Saulo

    2010-01-01T23:59:59.000Z

    This is a conference proceeding that is now being put together as a book. This is chapter 2 of the book: "INTEGRATED SYSTEMS OF MESO-METEOROLOGICAL AND CHEMICAL TRANSPORT MODELS" published by Springer. The chapter title is "On-line Chemistry within WRF: Description and Evaluation of a State-of-the-Art Multiscale Air Quality and Weather Prediction Model." The original conference was the COST-728/NetFAM workshop on Integrated systems of meso-meteorological and chemical transport models, Danish Meteorological Institute, Copenhagen, May 21-23, 2007.

  20. MODELING MULTI-OUTPUT FILTERING EFFECTS IN PCMOS Anshul Singh*

    E-Print Network [OSTI]

    Mooney, Vincent

    MODELING MULTI-OUTPUT FILTERING EFFECTS IN PCMOS Anshul Singh* , Arindam Basu , Keck-Voon Ling* and Vincent J. Mooney III*$§ Email: anshul.singh@research.iiit.ac.in, {arindam.basu, ekvling}@ntu, Nanyang Technological University (NTU), Singapore * NTU-Rice Institute of Sustainable and Applied

  1. Simple SPICE model for comparison of CMOS output driver circuits

    E-Print Network [OSTI]

    Hermann, John Karl

    1993-01-01T23:59:59.000Z

    to monitor the ground nodes of output driver circuits for noise. Both relative performance and noise levels are generated through the simulations. A test device was built to confirm that the model was effective in speed and noise comparisons. Values were... on CMOS technologies. Journal model is IEEE 'I?ansactions on Automatic Control. A. Literature Survey Research has been done in the past concerning noise generated by digital logic de- vices. In particular, Advanced CMOS Logic (ACL) integrated circuits...

  2. The WRF nested within the CESM: Simulations of a midlatitude cyclone over the Southern Great Plains

    E-Print Network [OSTI]

    Ohta, Shigemi

    and Forecasting (WRF) model is nested within the global Community Earth System Model (CESM). Since both models System Model (CESM). This system is validated for the simulation of a midlatitude cyclongesis event over system in which the Weather Research and Forecasting model (WRF) is nested within the Community Earth

  3. Investigating Output Accuracy for a Discrete Event Simulation Model and an Agent Based Simulation Model

    E-Print Network [OSTI]

    Majid, Mazlina Abdul; Siebers, Peer-Olaf

    2010-01-01T23:59:59.000Z

    In this paper, we investigate output accuracy for a Discrete Event Simulation (DES) model and Agent Based Simulation (ABS) model. The purpose of this investigation is to find out which of these simulation techniques is the best one for modelling human reactive behaviour in the retail sector. In order to study the output accuracy in both models, we have carried out a validation experiment in which we compared the results from our simulation models to the performance of a real system. Our experiment was carried out using a large UK department store as a case study. We had to determine an efficient implementation of management policy in the store's fitting room using DES and ABS. Overall, we have found that both simulation models were a good representation of the real system when modelling human reactive behaviour.

  4. Use of Advanced Meteorological Model Output for Coastal Ocean Modeling in Puget Sound

    SciTech Connect (OSTI)

    Yang, Zhaoqing; Khangaonkar, Tarang; Wang, Taiping

    2011-06-01T23:59:59.000Z

    It is a great challenge to specify meteorological forcing in estuarine and coastal circulation modeling using observed data because of the lack of complete datasets. As a result of this limitation, water temperature is often not simulated in estuarine and coastal modeling, with the assumption that density-induced currents are generally dominated by salinity gradients. However, in many situations, temperature gradients could be sufficiently large to influence the baroclinic motion. In this paper, we present an approach to simulate water temperature using outputs from advanced meteorological models. This modeling approach was applied to simulate annual variations of water temperatures of Puget Sound, a fjordal estuary in the Pacific Northwest of USA. Meteorological parameters from North American Region Re-analysis (NARR) model outputs were evaluated with comparisons to observed data at real-time meteorological stations. Model results demonstrated that NARR outputs can be used to drive coastal ocean models for realistic simulations of long-term water-temperature distributions in Puget Sound. Model results indicated that the net flux from NARR can be further improved with the additional information from real-time observations.

  5. NCAR WRF-based data assimilation and forecasting systems for wind energy applications power

    E-Print Network [OSTI]

    Kim, Guebuem

    NCAR WRF-based data assimilation and forecasting systems for wind energy applications power Yuewei of these modeling technologies w.r.t. wind energy applications. Then I'll discuss wind farm

  6. Probabilistic Forecasts of Wind Speed: Ensemble Model Output Statistics

    E-Print Network [OSTI]

    Washington at Seattle, University of

    . Over the past two decades, ensembles of numerical weather prediction (NWP) models have been developed and phrases: Continuous ranked probability score; Density forecast; Ensem- ble system; Numerical weather prediction; Heteroskedastic censored regression; Tobit model; Wind energy. 1 #12;1 Introduction Accurate

  7. Neural Networks for Post-processing Model Output: Caren Marzban

    E-Print Network [OSTI]

    Marzban, Caren

    variables to the neural network are: Forecast hour, model forecast temperature, relative humidity, wind direction and speed, mean sea level pressure, cloud cover, and precipitation rate and amount. The single to being able to approximate a large class of functions, they are less inclined to overfit data than some

  8. Impact of natural and anthropogenic aerosols on stratocumulus and precipitation in the Southeast Pacific: A regional modeling study using WRF-Chem

    SciTech Connect (OSTI)

    Yang, Qing; Gustafson, William I.; Fast, Jerome D.; Wang, Hailong; Easter, Richard C.; Wang, Minghuai; Ghan, Steven J.; Berg, Larry K.; Leung, Lai-Yung R.; Morrison, H.

    2012-09-28T23:59:59.000Z

    Cloud-system resolving simulations with the chemistry version of the Weather Research and Forecasting (WRF-Chem) model are used to quantify the impacts of regional anthropogenic and oceanic emissions on changes in aerosol properties, cloud macro- and microphysics, and cloud radiative forcing over the Southeast Pacific (SEP) during the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) (15 OctĖNov 16, 2008). The effects of oceanic aerosols on cloud properties, precipitation, and the shortwave forcing counteract those of anthropogenic aerosols. Despite the relatively small changes in Na concentrations (2-12%) from regional oceanic emissions, their net effect (direct and indirect) on the surface shortwave forcing is opposite and comparable or even larger in magnitude compared to those of regional anthropogenic emissions over the SEP. Two distinct regions are identified in the VOCALS-REx domain. The near-coast polluted region is characterized with strong droplet activation suppression of small particles by sea-salt particles, the more important role of the first than the second indirect effect, low surface precipitation rate, and low aerosol-cloud interaction strength associated with anthropogenic emissions. The relatively clean remote region is characterized with large contributions of Cloud Condensation Nuclei (CCN, number concentration denoted by NCCN) and droplet number concentrations (Nd) from non-local sources (lateral boundaries), a significant amount of surface precipitation, and high aerosol-cloud interactions under a scenario of five-fold increase in anthropogenic emissions. In the clean region, cloud properties have high sensitivity (e.g., 13% increase in cloud-top height and a 9% surface albedo increase) to the moderate increase in CCN concentration (?Nccn = 13 cm-3; 25%) produced by a five-fold increase in regional anthropogenic emissions. The increased anthropogenic aerosols reduce the precipitation amount over the relatively clean remote ocean. The reduction of precipitation (as a cloud water sink) more than doubles the wet scavenging timescale, resulting in an increased aerosol lifetime in the marine boundary layer. Therefore, the aerosol impacts on precipitation are amplified by the positive feedback of precipitation on aerosol. The positive feedback ultimately alters the cloud micro- and macro-properties, leading to strong aerosol-cloud-precipitation interactions. The higher sensitivity of clouds to anthropogenic aerosols over this region is also related to a 16% entrainment rate increase due to anthropogenic aerosols. The simulated aerosol-cloud-precipitation interactions are stronger at night over the clean marine region, while during the day, solar heating results in more frequent decoupling, thinner clouds, reduced precipitation, and reduced sensitivity to anthropogenic emissions. The simulated high sensitivity to the increased anthropogenic emissions over the clean region suggests that the perturbation of the clean marine environment with anthropogenic aerosols may have a larger effect on climate than that of already polluted marine environments.

  9. Modeling the Energy Output from an In-Stream Tidal Turbine Farm

    E-Print Network [OSTI]

    Ye Li; Barbara J. Lence; Sander M. Calisal

    AbstractóThis paper is based on a recent paper presented in the 2007 IEEE SMC conference by the same authors [1], discussing an approach to predicting energy output from an instream tidal turbine farm. An in-stream tidal turbine is a device for harnessing energy from tidal currents in channels, and functions in a manner similar to a wind turbine. A group of such turbines distributed in a site is called an in-stream tidal turbine farm which is similar to a wind farm. Approaches to estimating energy output from wind farms cannot be fully transferred to study tidal farms, however, because of the complexities involved in modeling turbines underwater. In this paper, we intend to develop an approach for predicting energy output of an in-stream tidal turbine farm. The mathematical formulation and basic procedure for predicting power output of a stand-alone turbine 1 is presented, which includes several highly nonlinear terms. In order to facilitate the computation and utilize the formulation for predicting power output from a turbine farm, a simplified relationship between turbine distribution and turbine farm energy output is derived. A case study is then conducted by applying the numerical procedure to predict the energy output of the farms. Various scenarios are implemented according to the environmental conditions in Seymour Narrows, British Columbia, Canada. Additionally, energy cost results are presented as an extension. Index Termsórenewable energy, in-stream turbine, tidal current, tidal power, vertical axis turbine, farm system modeling, in-stream tidal turbine farm 1 A stand-alone turbine refers to a turbine around which there is no other turbine that might potentially affect the performance of this turbine.

  10. Bayesian Emulation of Complex Multi-Output and Dynamic Computer Models

    E-Print Network [OSTI]

    Oakley, Jeremy

    Bayesian Emulation of Complex Multi-Output and Dynamic Computer Models Stefano Conti Anthony O the case). In particular, standard Monte Carlo-based methods of sensitivity analysis (extensively reviewed'Hagan, 2002), offering substantial efficiency gains over standard Monte Carlo-based meth- ods. These authors

  11. Probabilistic Forecasting of (Severe) Thunderstorms in the Netherlands Using Model Output Statistics

    E-Print Network [OSTI]

    Schmeits, Maurice

    Probabilistic Forecasting of (Severe) Thunderstorms in the Netherlands Using Model Output Statistics MAURICE J. SCHMEITS, KEES J. KOK, AND DAAN H. P. VOGELEZANG Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands (Manuscript received 29 April 2004, in final form 7 September 2004

  12. Using Weather Data and Climate Model Output in Economic Analyses of Climate Change

    SciTech Connect (OSTI)

    Auffhammer, Maximilian [University of California at Berkeley; Hsiang, Solomon M. [Princeton University; Schlenker, Wolfram [Columbia University; Sobel, Adam H. [Columbia University

    2013-06-28T23:59:59.000Z

    Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces a set of weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.

  13. Evaluation of a WRF dynamical downscaling simulation over California

    E-Print Network [OSTI]

    Caldwell, Peter; Chin, Hung-Neng S.; Bader, David C.; Bala, Govindasamy

    2009-01-01T23:59:59.000Z

    long-term precipitation climatology from WRF, CCSM3, NARR,? Pr Fig. 2 Annual Pr climatology from WRF, CCSM3, and NARRresults shown here are climatologies and thus donít account

  14. Cardiac output and stroke volume estimation using a hybrid of three models

    E-Print Network [OSTI]

    Arai, Tatsuya

    Cardiac output (CO) and stroke volume (SV) are the key hemodynamic parameters to be monitored and assessed in ambulatory and critically ill patients. The purpose of this study was to introduce and validate a new algorithm ...

  15. Robust UAV Coordination for Target Tracking using Output-Feedback Model Predictive Control with Moving Horizon Estimation

    E-Print Network [OSTI]

    Hespanha, Jo√£o Pedro

    Robust UAV Coordination for Target Tracking using Output-Feedback Model Predictive Control consider the control of two UAVs tracking an evasive moving ground vehicle. The UAVs are small fixed to maintain visibility. The control inputs to the UAVs are computed based on noisy measurements of the UAVs

  16. WRF Test on IBM BG/L:Toward High Performance Application to Regional Climate Research

    SciTech Connect (OSTI)

    Chin, H S

    2008-09-25T23:59:59.000Z

    The effects of climate change will mostly be felt on local to regional scales (Solomon et al., 2007). To develop better forecast skill in regional climate change, an integrated multi-scale modeling capability (i.e., a pair of global and regional climate models) becomes crucially important in understanding and preparing for the impacts of climate change on the temporal and spatial scales that are critical to California's and nation's future environmental quality and economical prosperity. Accurate knowledge of detailed local impact on the water management system from climate change requires a resolution of 1km or so. To this end, a high performance computing platform at the petascale appears to be an essential tool in providing such local scale information to formulate high quality adaptation strategies for local and regional climate change. As a key component of this modeling system at LLNL, the Weather Research and Forecast (WRF) model is implemented and tested on the IBM BG/L machine. The objective of this study is to examine the scaling feature of WRF on BG/L for the optimal performance, and to assess the numerical accuracy of WRF solution on BG/L.

  17. Scalable extraction of error models from the output of error detection circuits

    E-Print Network [OSTI]

    Austin G. Fowler; D. Sank; J. Kelly; R. Barends; John M. Martinis

    2014-05-06T23:59:59.000Z

    Accurate methods of assessing the performance of quantum gates are extremely important. Quantum process tomography and randomized benchmarking are the current favored methods. Quantum process tomography gives detailed information, but significant approximations must be made to reduce this information to a form quantum error correction simulations can use. Randomized benchmarking typically outputs just a single number, the fidelity, giving no information on the structure of errors during the gate. Neither method is optimized to assess gate performance within an error detection circuit, where gates will be actually used in a large-scale quantum computer. Specifically, the important issues of error composition and error propagation lie outside the scope of both methods. We present a fast, simple, and scalable method of obtaining exactly the information required to perform effective quantum error correction from the output of continuously running error detection circuits, enabling accurate prediction of large-scale behavior.

  18. Evaluating WRF-Chem aerosol indirect effects in Southeast Pacific marine stratocumulus during VOCALS-REx

    SciTech Connect (OSTI)

    Saide, Pablo; Spak, S. N.; Carmichael, Gregory; Mena-Carrasco, M. A.; Yang, Qing; Howell, S. G.; Leon, Dolislager; Snider, Jefferson R.; Bandy, Alan R.; Collett, Jeffrey L.; Benedict, K. B.; de Szoeke, S.; Hawkins, Lisa; Allen, Grant; Crawford, I.; Crosier, J.; Springston, S. R.

    2012-03-30T23:59:59.000Z

    We evaluate a regional-scale simulation with the WRF-Chem model for the VAMOS (Variability of the American Monsoon Systems) Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx), which sampled the Southeast Pacific's persistent stratocumulus deck. Evaluation of VOCALS-REx ship-based and aircraft observations focuses on analyzing how aerosol loading affects marine boundary layer (MBL) dynamics and cloud microphysics. We compare local time series and campaign averaged longitudinal gradients, and highlight differences in model simulations with (W) and without wet (NW) deposition processes. The higher aerosol loadings in the NW case produce considerable changes in MBL dynamics and cloud microphysics, in accordance with the established conceptual model of aerosol indirect effects. These include increase in cloud albedo, increase in MBL and cloud heights, drizzle suppression, increase in liquid water content, and increase in cloud lifetime. Moreover, better statistical representation of aerosol mass and number concentration improves model fidelity in reproducing observed spatial and temporal variability in cloud properties, including top and base height, droplet concentration, water content, rain rate, optical depth (COD) and liquid water path (LWP). Together, these help to quantify confidence in WRF-Chem's modeled aerosol-cloud interactions, while identifying structural and parametric uncertainties including: irreversibility in rain wet removal; overestimation of marine DMS and sea salt emissions and accelerated aqueous sulfate conversion. Our findings suggest that WRF-Chem simulates marine cloud-aerosol interactions at a level sufficient for applications in forecasting weather and air quality and studying aerosol climate forcing, including the reliability required for policy analysis and geo-engineering applications.

  19. Modelling Dynamic Constraints in Electricity Markets and the Costs of Uncertain Wind Output

    E-Print Network [OSTI]

    Musgens, Felix; Neuhoff, Karsten

    2006-03-14T23:59:59.000Z

    . Keywords: Electricity Markets, Energy Modelling, Optimisation Models, JEL-classification: C61, Q41 Author Affiliations: Felix MŁsgens Graduate School of Risk Manage- ment and Institute of Energy Economics University of KŲln Albertus... cycle ( maxt min,1t min,2t min maxt t 1 d d ? visualizes this structure. Figure 1: Energy Prices with Inter-temporal Constraints tmin,1 tmax P ric e tpart,2 tmin2tpart,1 tpart,3 Costs for Fuel + Start up + Part load operation Demand...

  20. A framework for interpreting climate model outputs Nadja A. Leith and Richard E. Chandler

    E-Print Network [OSTI]

    Guillas, Serge

    to illustrate the methodology. Some key words: Climate change; Climate model uncertainty; Contemporaneous ARMA acknowledged that human activities have caused changes in the Earth's climate (Solomon et al., 2007). Indeed #12;the hydrological cycle (Solomon et al., 2007). To accommodate this possibility therefore, planners

  1. Artificial neural networks for input-output dynamic modeling of nonlinear processes

    E-Print Network [OSTI]

    Sarimveis, Haralambos

    2012-06-07T23:59:59.000Z

    &at& tire behavior of n&arlinear SIS(2 and 1&IIMO pro&. esses, provi&1& d that tlrv latter operate closv vnou?h t&& dvsired operating points. In the follow'irrg &lrapters, sex&'ra) rrretlrods of non(&near modeling will be used to u&o&1&'I tl&e samv nonliu... the fnlh)&vh)r, e(tu&(t&nn: R M , V = 1 + gP, + P (1, (3) a(&cl o 11 n&1 u . . u 1?v T= (('a(v (V u st I I? tt 2 . . . n';If N is the 3I x(&1 n&atrix of parameters. ln this n&atrix u& & ls the parameter of c(u&n( ction bet&veen tth model input a...

  2. A Comparison of Simulated Cloud Radar Output from the Multiscale Modeling Framework Global Climate Model with CloudSat Cloud Radar Observations

    SciTech Connect (OSTI)

    Marchand, Roger T.; Haynes, J. M.; Mace, Gerald G.; Ackerman, Thomas P.; Stephens, Graeme L.

    2009-01-13T23:59:59.000Z

    Over the last few years a new type of global climate model (GCM) has emerged in which a cloud-resolving model is embedded into each grid cell of a GCM. This new approach is frequently called a multiscale modeling framework (MMF) or superparameterization. In this article we present a comparison of MMF output with radar observations from the NASA CloudSat mission, which uses a near-nadir-pointing millimeter-wavelength radar to probe the vertical structure of clouds and precipitation. We account for radar detection limits by simulating the 94 GHz radar reflectivity that CloudSat would observe from the high-resolution cloud-resolving model output produced by the MMF. Overall, the MMF does a good job of reproducing the broad pattern of tropical convergence zones, subtropical belts, and midlatitude storm tracks, as well as their changes in position with the annual solar cycle. Nonetheless, the comparison also reveals a number of model shortfalls including (1) excessive hydrometeor coverage at all altitudes over many convectively active regions, (2) a lack of low-level hydrometeors over all subtropical oceanic basins, (3) excessive low-level hydrometeor coverage (principally precipitating hydrometeors) in the midlatitude storm tracks of both hemispheres during the summer season (in each hemisphere), and (4) a thin band of low-level hydrometeors in the Southern Hemisphere of the central (and at times eastern and western) Pacific in the MMF, which is not observed by CloudSat. This band resembles a second much weaker ITCZ but is restricted to low levels.

  3. Study of Multi-Scale Cloud Processes Over the Tropical Western Pacific Using Cloud-Resolving Models Constrained by Satellite Data

    SciTech Connect (OSTI)

    Dudhia, Jimy

    2013-03-12T23:59:59.000Z

    Clouds in the tropical western Pacific are an integral part of the large scale environment. An improved understanding of the multi-scale structure of clouds and their interactions with the environment is critical to the ARM (Atmospheric Radiation Measurement) program for developing and evaluating cloud parameterizations, understanding the consequences of model biases, and providing a context for interpreting the observational data collected over the ARM Tropical Western Pacific (TWP) sites. Three-dimensional cloud resolving models (CRMs) are powerful tools for developing and evaluating cloud parameterizations. However, a significant challenge in using CRMs in the TWP is that the region lacks conventional data, so large uncertainty exists in defining the large-scale environment for clouds. This project links several aspects of the ARM program, from measurements to providing improved analyses, and from cloud-resolving modeling to climate-scale modeling and parameterization development, with the overall objective to improve the representations of clouds in climate models and to simulate and quantify resolved cloud effects on the large-scale environment. Our objectives will be achieved through a series of tasks focusing on the use of the Weather Research and Forecasting (WRF) model and ARM data. Our approach includes: -- Perform assimilation of COSMIC GPS radio occultation and other satellites products using the WRF Ensemble Kalman Filter assimilation system to represent the tropical large-scale environment at 36 km grid resolution. This high-resolution analysis can be used by the community to derive forcing products for single-column models or cloud-resolving models. -- Perform cloud-resolving simulations using WRF and its nesting capabilities, driven by the improved regional analysis and evaluate the simulations against ARM datasets such as from TWP-ICE to optimize the microphysics parameters for this region. A cirrus study (Mace and co-authors) already exists for TWP-ICE using satellite and ground-based observations. -- Perform numerical experiments using WRF to investigate how convection over tropical islands in the Maritime Continent interacts with large-scale circulation and affects convection in nearby regions. -- Evaluate and apply WRF as a testbed for GCM cloud parameterizations, utilizing the ability of WRF to run on multiple scales (from cloud resolving to global) to isolate resolution and physics issues from dynamical and model framework issues. Key products will be disseminated to the ARM and larger community through distribution of data archives, including model outputs from the data assimilation products and cloud resolving simulations, and publications.

  4. WRF-Chem Simulations of Aerosols and Anthropogenic Aerosol Radiative Forcing in East Asia

    SciTech Connect (OSTI)

    Gao, Yi; Zhao, Chun; Liu, Xiaohong; Zhang, Meigen; Leung, Lai-Yung R.

    2014-08-01T23:59:59.000Z

    This study aims to provide a first comprehensive evaluation of WRF-Chem for modeling aerosols and anthropogenic aerosol radiative forcing (RF) over East Asia. Several numerical experiments were conducted from November 2007 to December 2008. Comparison between model results and observations shows that the model can generally reproduce the observed spatial distributions of aerosol concentration, aerosol optical depth (AOD) and single scattering albedo (SSA) from measurements at different sites, including the relatively higher aerosol concentration and AOD over East China and the relatively lower AOD over Southeast Asia, Korean, and Japan. The model also depicts the seasonal variation and transport of pollutions over East Asia. Particulate matter of 10 um or less in the aerodynamic diameter (PM10), black carbon (BC), sulfate (SO42-), nitrate (NO3-) and ammonium (NH4+) concentrations are higher in spring than other seasons in Japan due to the pollutant transport from polluted area of East Asia. AOD is high over Southwest and Central China in winter, spring and autumn and over North China in summer while is low over South China in summer due to monsoon precipitation. SSA is lowest in winter and highest in summer. The model also captures the dust events at the Zhangye site in the semi-arid region of China. Anthropogenic aerosol RF is estimated to range from -5 to -20 W m-2 over land and -20 to -40 W m-2 over ocean at the top of atmosphere (TOA), 5 to 30 W m-2 in the atmosphere (ATM) and -15 to -40 W m-2 at the bottom (BOT). The warming effect of anthropogenic aerosol in ATM results from BC aerosol while the negative aerosol RF at TOA is caused by scattering aerosols such as SO4 2-, NO3 - and NH4+. Positive BC RF at TOA compensates 40~50% of the TOA cooling associated with anthropogenic aerosol.

  5. Comparisons of four categories of waste recycling in China's paper industry based on physical input-output life-cycle assessment model

    SciTech Connect (OSTI)

    Liang Sai [School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084 (China); Zhang, Tianzhu, E-mail: zhangtz@mail.tsinghua.edu.cn [School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084 (China); Xu Yijian [School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084 (China); China Academy of Urban Planning and Design, Beijing 100037 (China)

    2012-03-15T23:59:59.000Z

    Highlights: Black-Right-Pointing-Pointer Using crop straws and wood wastes for paper production should be promoted. Black-Right-Pointing-Pointer Bagasse and textile waste recycling should be properly limited. Black-Right-Pointing-Pointer Imports of scrap paper should be encouraged. Black-Right-Pointing-Pointer Sensitivity analysis, uncertainties and policy implications are discussed. - Abstract: Waste recycling for paper production is an important component of waste management. This study constructs a physical input-output life-cycle assessment (PIO-LCA) model. The PIO-LCA model is used to investigate environmental impacts of four categories of waste recycling in China's paper industry: crop straws, bagasse, textile wastes and scrap paper. Crop straw recycling and wood utilization for paper production have small total intensity of environmental impacts. Moreover, environmental impacts reduction of crop straw recycling and wood utilization benefits the most from technology development. Thus, using crop straws and wood (including wood wastes) for paper production should be promoted. Technology development has small effects on environmental impacts reduction of bagasse recycling, textile waste recycling and scrap paper recycling. In addition, bagasse recycling and textile waste recycling have big total intensity of environmental impacts. Thus, the development of bagasse recycling and textile waste recycling should be properly limited. Other pathways for reusing bagasse and textile wastes should be explored and evaluated. Moreover, imports of scrap paper should be encouraged to reduce large indirect impacts of scrap paper recycling on domestic environment.

  6. Data Assimilation of Lightning in WRF 3/4-D VAR Using Observation Operators

    E-Print Network [OSTI]

    Navon, Michael

    @fsu.edu Abstract Compared to other types of satellite-derived data, assimilating lightning data into operationalData Assimilation of Lightning in WRF 3/4-D VAR Using Observation Operators Razvan S¬łtefanescu1. The early stages of our research will utilize existing ground-based lightning data that can be assimilated

  7. Evaluation of WRF Forecasts of Tornadic and Nontornadic Outbreaks When Initialized with Synoptic-Scale Input

    E-Print Network [OSTI]

    Doswell III, Charles A.

    Evaluation of WRF Forecasts of Tornadic and Nontornadic Outbreaks When Initialized with Synoptic outbreak simulations are compared with 50 primarily nontornadic outbreak simulations initialized tornadoes (e.g., Stensrud et al. 1997, hereafter SCB97; Doswell and Bosart 2001; Doswell et al. 2006

  8. Comparing urban solid waste recycling from the viewpoint of urban metabolism based on physical input-output model: A case of Suzhou in China

    SciTech Connect (OSTI)

    Liang Sai, E-mail: liangsai09@gmail.com [School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084 (China); Zhang Tianzhu, E-mail: zhangtz@mail.tsinghua.edu.cn [School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084 (China)

    2012-01-15T23:59:59.000Z

    Highlights: Black-Right-Pointing-Pointer Impacts of solid waste recycling on Suzhou's urban metabolism in 2015 are analyzed. Black-Right-Pointing-Pointer Sludge recycling for biogas is regarded as an accepted method. Black-Right-Pointing-Pointer Technical levels of reusing scrap tires and food wastes should be improved. Black-Right-Pointing-Pointer Other fly ash utilization methods should be exploited. Black-Right-Pointing-Pointer Secondary wastes from reusing food wastes and sludge should be concerned. - Abstract: Investigating impacts of urban solid waste recycling on urban metabolism contributes to sustainable urban solid waste management and urban sustainability. Using a physical input-output model and scenario analysis, urban metabolism of Suzhou in 2015 is predicted and impacts of four categories of solid waste recycling on urban metabolism are illustrated: scrap tire recycling, food waste recycling, fly ash recycling and sludge recycling. Sludge recycling has positive effects on reducing all material flows. Thus, sludge recycling for biogas is regarded as an accepted method. Moreover, technical levels of scrap tire recycling and food waste recycling should be improved to produce positive effects on reducing more material flows. Fly ash recycling for cement production has negative effects on reducing all material flows except solid wastes. Thus, other fly ash utilization methods should be exploited. In addition, the utilization and treatment of secondary wastes from food waste recycling and sludge recycling should be concerned.

  9. Commissioning of output factors for uniform scanning proton beams

    SciTech Connect (OSTI)

    Zheng Yuanshui; Ramirez, Eric; Mascia, Anthony; Ding Xiaoning; Okoth, Benny; Zeidan, Omar; Hsi Wen; Harris, Ben; Schreuder, Andries N.; Keole, Sameer [ProCure Proton Therapy Center, 5901 West Memorial Road, Oklahoma City, Oklahoma 73142 (United States); ProCure Treatment Centers, 420 North Walnut Street, Bloomington, Indiana 47404 (United States); ProCure Proton Therapy Center, 5901 West Memorial Road, Oklahoma City, Oklahoma 73142 (United States)

    2011-04-15T23:59:59.000Z

    Purpose: Current commercial treatment planning systems are not able to accurately predict output factors and calculate monitor units for proton fields. Patient-specific field output factors are thus determined by either measurements or empirical modeling based on commissioning data. The objective of this study is to commission output factors for uniform scanning beams utilized at the ProCure proton therapy centers. Methods: Using water phantoms and a plane parallel ionization chamber, the authors first measured output factors with a fixed 10 cm diameter aperture as a function of proton range and modulation width for clinically available proton beams with ranges between 4 and 31.5 cm and modulation widths between 2 and 15 cm. The authors then measured the output factor as a function of collimated field size at various calibration depths for proton beams of various ranges and modulation widths. The authors further examined the dependence of the output factor on the scanning area (i.e., uncollimated proton field), snout position, and phantom material. An empirical model was developed to calculate the output factor for patient-specific fields and the model-predicted output factors were compared to measurements. Results: The output factor increased with proton range and field size, and decreased with modulation width. The scanning area and snout position have a small but non-negligible effect on the output factors. The predicted output factors based on the empirical modeling agreed within 2% of measurements for all prostate treatment fields and within 3% for 98.5% of all treatment fields. Conclusions: Comprehensive measurements at a large subset of available beam conditions are needed to commission output factors for proton therapy beams. The empirical modeling agrees well with the measured output factor data. This investigation indicates that it is possible to accurately predict output factors and thus eliminate or reduce time-consuming patient-specific output measurements for proton treatments.

  10. Assessing regional scale predictions of aerosols, marine stratocumulus, and their interactions during VOCALS-REx using WRF-Chem

    SciTech Connect (OSTI)

    Yang Q.; Lee Y.; Gustafson†Jr., W. I.; Fast, J. D.; Wang, H.; Easter, R. C.; Morrison, H.; Chapman, E. G.; Spak, S. N.; Mena-Carrasco, M. A.

    2011-12-02T23:59:59.000Z

    This study assesses the ability of the recent chemistry version (v3.3) of the Weather Research and Forecasting (WRF-Chem) model to simulate boundary layer structure, aerosols, stratocumulus clouds, and energy fluxes over the Southeast Pacific Ocean. Measurements from the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) and satellite retrievals (i.e., products from the MODerate resolution Imaging Spectroradiometer (MODIS), Clouds and Earth's Radiant Energy System (CERES), and GOES-10) are used for this assessment. The Morrison double-moment microphysics scheme is newly coupled with interactive aerosols in the model. The 31-day (15 October-16 November 2008) WRF-Chem simulation with aerosol-cloud interactions (AERO hereafter) is also compared to a simulation (MET hereafter) with fixed cloud droplet number concentrations in the microphysics scheme and simplified cloud and aerosol treatments in the radiation scheme. The well-simulated aerosol quantities (aerosol number, mass composition and optical properties), and the inclusion of full aerosol-cloud couplings lead to significant improvements in many features of the simulated stratocumulus clouds: cloud optical properties and microphysical properties such as cloud top effective radius, cloud water path, and cloud optical thickness. In addition to accounting for the aerosol direct and semi-direct effects, these improvements feed back to the simulation of boundary-layer characteristics and energy budgets. Particularly, inclusion of interactive aerosols in AERO strengthens the temperature and humidity gradients within the capping inversion layer and lowers the marine boundary layer (MBL) depth by 130 m from that of the MET simulation. These differences are associated with weaker entrainment and stronger mean subsidence at the top of the MBL in AERO. Mean top-of-atmosphere outgoing shortwave fluxes, surface latent heat, and surface downwelling longwave fluxes are in better agreement with observations in AERO, compared to the MET simulation. Nevertheless, biases in some of the simulated meteorological quantities (e.g., MBL temperature and humidity) and aerosol quantities (e.g., underestimations of accumulation mode aerosol number) might affect simulated stratocumulus and energy fluxes over the Southeastern Pacific, and require further investigation. The well-simulated timing and outflow patterns of polluted and clean episodes demonstrate the model's ability to capture daily/synoptic scale variations of aerosol and cloud properties, and suggest that the model is suitable for studying atmospheric processes associated with pollution outflow over the ocean. The overall performance of the regional model in simulating mesoscale clouds and boundary layer properties is encouraging and suggests that reproducing gradients of aerosol and cloud droplet concentrations and coupling cloud-aerosol-radiation processes are important when simulating marine stratocumulus over the Southeast Pacific.

  11. Comparison of CAISO-run Plexos output with LLNL-run Plexos output

    SciTech Connect (OSTI)

    Schmidt, A; Meyers, C; Smith, S

    2011-12-20T23:59:59.000Z

    In this report we compare the output of the California Independent System Operator (CAISO) 33% RPS Plexos model when run on various computing systems. Specifically, we compare the output resulting from running the model on CAISO's computers (Windows) and LLNL's computers (both Windows and Linux). We conclude that the differences between the three results are negligible in the context of the entire system and likely attributed to minor differences in Plexos version numbers as well as the MIP solver used in each case.

  12. Enhanced performance CCD output amplifier

    DOE Patents [OSTI]

    Dunham, Mark E. (Los Alamos, NM); Morley, David W. (Santa Fe, NM)

    1996-01-01T23:59:59.000Z

    A low-noise FET amplifier is connected to amplify output charge from a che coupled device (CCD). The FET has its gate connected to the CCD in common source configuration for receiving the output charge signal from the CCD and output an intermediate signal at a drain of the FET. An intermediate amplifier is connected to the drain of the FET for receiving the intermediate signal and outputting a low-noise signal functionally related to the output charge signal from the CCD. The amplifier is preferably connected as a virtual ground to the FET drain. The inherent shunt capacitance of the FET is selected to be at least equal to the sum of the remaining capacitances.

  13. Evaluation of and Suggested Improvements to the WSM6 Microphysics in WRF- ARW Using Synthetic and Observed GOES-13 Imagery

    SciTech Connect (OSTI)

    Grasso, Lewis; Lindsey, Daniel T.; Lim, Kyo-Sun; Clark, Adam; Bikos, Dan; Dembek, Scott R.

    2014-10-01T23:59:59.000Z

    Synthetic satellite imagery can be employed to evaluate simulated cloud fields. Past studies have revealed that the Weather Research and Forecasting (WRF) WRF Single-Moment 6-class (WSM6) microphysics in WRF-ARW produces less upper level ice clouds within synthetic images compared to observations. Synthetic Geostationary Operational Environmental Satellite (GOES)-13 imagery at 10.7 ?m of simulated cloud fields from the 4 km National Severe Storms Laboratory (NSSL) WRF-ARW is compared to observed GOES-13 imagery. Histograms suggest that too few points contain upper level simulated ice clouds. In particular, side-by-side examples are shown of synthetic and observed convective anvils. Such images illustrate the lack of anvil cloud associated with convection produced by the NSSL WRF-ARW. A vertical profile of simulated hydrometeors suggests that too much cloud water mass may be converted into graupel mass, effectively reducing the main source of ice mass in a simulated anvil. Further, excessive accretion of ice by snow removes ice from an anvil by precipitation settling. Idealized sensitivity tests reveal that a 50% reduction of the conversion of cloud water mass to graupel and a 50% reduction of the accretion rate of ice by snow results in a significant increase in anvil ice of a simulated storm. Such results provide guidance as to which conversions could be reformulated, in a more physical manner, to increase simulated ice mass in the upper troposphere.

  14. UFO - The Universal FeynRules Output

    E-Print Network [OSTI]

    Cťline Degrande; Claude Duhr; Benjamin Fuks; David Grellscheid; Olivier Mattelaer; Thomas Reiter

    2012-07-31T23:59:59.000Z

    We present a new model format for automatized matrix-element generators, the so- called Universal FeynRules Output (UFO). The format is universal in the sense that it features compatibility with more than one single generator and is designed to be flexible, modular and agnostic of any assumption such as the number of particles or the color and Lorentz structures appearing in the interaction vertices. Unlike other model formats where text files need to be parsed, the information on the model is encoded into a Python module that can easily be linked to other computer codes. We then describe an interface for the Mathematica package FeynRules that allows for an automatic output of models in the UFO format.

  15. UFO - The Universal FeynRules Output

    E-Print Network [OSTI]

    Degrande, Cťline; Fuks, Benjamin; Grellscheid, David; Mattelaer, Olivier; Reiter, Thomas

    2011-01-01T23:59:59.000Z

    We present a new model format for automatized matrix-element generators, the so- called Universal FeynRules Output (UFO). The format is universal in the sense that it features compatibility with more than one single generator and is designed to be flexible, modular and agnostic of any assumption such as the number of particles or the color and Lorentz structures appearing in the interaction vertices. Unlike other model formats where text files need to be parsed, the information on the model is encoded into a Python module that can easily be linked to other computer codes. We then describe an interface for the Mathematica package FeynRules that allows for an automatic output of models in the UFO format.

  16. Verification of hourly forecasts of wind turbine power output

    SciTech Connect (OSTI)

    Wegley, H.L.

    1984-08-01T23:59:59.000Z

    A verification of hourly average wind speed forecasts in terms of hourly average power output of a MOD-2 was performed for four sites. Site-specific probabilistic transformation models were developed to transform the forecast and observed hourly average speeds to the percent probability of exceedance of an hourly average power output. (This transformation model also appears to have value in predicting annual energy production for use in wind energy feasibility studies.) The transformed forecasts were verified in a deterministic sense (i.e., as continuous values) and in a probabilistic sense (based upon the probability of power output falling in a specified category). Since the smoothing effects of time averaging are very pronounced, the 90% probability of exceedance was built into the transformation models. Semiobjective and objective (model output statistics) forecasts were made compared for the four sites. The verification results indicate that the correct category can be forecast an average of 75% of the time over a 24-hour period. Accuracy generally decreases with projection time out to approx. 18 hours and then may increase due to the fairly regular diurnal wind patterns that occur at many sites. The ability to forecast the correct power output category increases with increasing power output because occurrences of high hourly average power output (near rated) are relatively rare and are generally not forecast. The semiobjective forecasts proved superior to model output statistics in forecasting high values of power output and in the shorter time frames (1 to 6 hours). However, model output statistics were slightly more accurate at other power output levels and times. Noticeable differences were observed between deterministic and probabilistic (categorical) forecast verification results.

  17. Effect of Terrestrial and Marine Organic Aerosol on Regional and Global Climate: Model Development, Application, and Verification with Satellite Data

    SciTech Connect (OSTI)

    Meskhidze, Nicholas; Zhang, Yang; Kamykowski, Daniel

    2012-03-28T23:59:59.000Z

    In this DOE project the improvements to parameterization of marine primary organic matter (POM) emissions, hygroscopic properties of marine POM, marine isoprene derived secondary organic aerosol (SOA) emissions, surfactant effects, new cloud droplet activation parameterization have been implemented into Community Atmosphere Model (CAM 5.0), with a seven mode aerosol module from the Pacific Northwest National Laboratory (PNNL)√?¬?√?¬Ę√?¬?√?¬?√?¬?√?¬?s Modal Aerosol Model (MAM7). The effects of marine aerosols derived from sea spray and ocean emitted biogenic volatile organic compounds (BVOCs) on microphysical properties of clouds were explored by conducting 10 year CAM5.0-MAM7 model simulations at a grid resolution 1.9√?¬?√?¬?√?¬?√?¬į√?¬?√?¬?√?¬?√?¬?2.5√?¬?√?¬?√?¬?√?¬į with 30 vertical layers. Model-predicted relationship between ocean physical and biological systems and the abundance of CCN in remote marine atmosphere was compared to data from the A-Train satellites (MODIS, CALIPSO, AMSR-E). Model simulations show that on average, primary and secondary organic aerosol emissions from the ocean can yield up to 20% increase in Cloud Condensation Nuclei (CCN) at 0.2% Supersaturation, and up to 5% increases in droplet number concentration of global maritime shallow clouds. Marine organics were treated as internally or externally mixed with sea salt. Changes associated with cloud properties reduced (absolute value) the model-predicted short wave cloud forcing from -1.35 Wm-2 to -0.25 Wm-2. By using different emission scenarios, and droplet activation parameterizations, this study suggests that addition of marine primary aerosols and biologically generated reactive gases makes an important difference in radiative forcing assessments. All baseline and sensitivity simulations for 2001 and 2050 using global-through-urban WRF/Chem (GU-WRF) were completed. The main objective of these simulations was to evaluate the capability of GU-WRF for an accurate representation of the global atmosphere by exploring the most accurate configuration of physics options in GWRF for global scale modeling in 2001 at a horizontal grid resolution of 1√?¬?√?¬?√?¬?√?¬į x 1√?¬?√?¬?√?¬?√?¬į. GU-WRF model output was evaluated using observational datasets from a variety of sources including surface based observations (NCDC and BSRN), model reanalysis (NCEP/ NCAR Reanalysis and CMAP), and remotely-sensed data (TRMM) to evaluate the ability of GU-WRF to simulate atmospheric variables at the surface as well as aloft. Explicit treatment of nanoparticles produced from new particle formation in GU-WRF/Chem-MADRID was achieved by expanding particle size sections from 8 to 12 to cover particles with the size range of 1.16 nm to 11.6 √?¬?√?¬?√?¬?√?¬Ķm. Simulations with two different nucleation parameterizations were conducted for August 2002 over a global domain at a 4√?¬?√?¬?√?¬?√?¬ļ by 5√?¬?√?¬?√?¬?√?¬ļ horizontal resolution. The results are evaluated against field measurement data from the 2002 Aerosol Nucleation and Real Time Characterization Experiment (ANARChE) in Atlanta, Georgia, as well as satellite and reanalysis data. We have also explored the relationship between √?¬?√?¬Ę√?¬?√?¬?√?¬?√?¬?clean marine√?¬?√?¬Ę√?¬?√?¬?√?¬?√?¬Ě aerosol optical properties and ocean surface wind speed using remotely sensed data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the CALIPSO satellite and the Advanced Microwave Scanning Radiometer (AMSR-E) on board the AQUA satellite. Detailed data analyses

  18. Examination of Errors in Near-Surface Temperature and Wind from WRF Numerical Simulations in Regions of Complex Terrain

    E-Print Network [OSTI]

    Pu, Zhaoxia

    -surface atmospheric conditions are very important in many applications such as wind energy, agriculture, aviationExamination of Errors in Near-Surface Temperature and Wind from WRF Numerical Simulations of Utah, Salt Lake City, Utah XUEBO ZHANG Computational Engineering and Science, University of Utah, Salt

  19. Sparse Convolved Gaussian Processes for Multi-output Regression

    E-Print Network [OSTI]

    Rattray, Magnus

    the concentration of different heavy metal pollutants [5]. Modelling multiple output variables is a challenge as we methodology for synthetic data and real world applications on pollution prediction and a sensor network. 1

  20. Using Mesoscale Weather Model Output as Boundary Conditions for Atmospheric Large-Eddy Simulations and Wind-Plant Aerodynamic Simulations (Presentation)

    SciTech Connect (OSTI)

    Churchfield, M. J.; Michalakes, J.; Vanderwende, B.; Lee, S.; Sprague, M. A.; Lundquist, J. K.; Moriarty, P. J.

    2013-10-01T23:59:59.000Z

    Wind plant aerodynamics are directly affected by the microscale weather, which is directly influenced by the mesoscale weather. Microscale weather refers to processes that occur within the atmospheric boundary layer with the largest scales being a few hundred meters to a few kilometers depending on the atmospheric stability of the boundary layer. Mesoscale weather refers to large weather patterns, such as weather fronts, with the largest scales being hundreds of kilometers wide. Sometimes microscale simulations that capture mesoscale-driven variations (changes in wind speed and direction over time or across the spatial extent of a wind plant) are important in wind plant analysis. In this paper, we present our preliminary work in coupling a mesoscale weather model with a microscale atmospheric large-eddy simulation model. The coupling is one-way beginning with the weather model and ending with a computational fluid dynamics solver using the weather model in coarse large-eddy simulation mode as an intermediary. We simulate one hour of daytime moderately convective microscale development driven by the mesoscale data, which are applied as initial and boundary conditions to the microscale domain, at a site in Iowa. We analyze the time and distance necessary for the smallest resolvable microscales to develop.

  1. A Framework to Determine the Probability Density Function for the Output Power of Wind Farms

    E-Print Network [OSTI]

    Liberzon, Daniel

    A Framework to Determine the Probability Density Function for the Output Power of Wind Farms Sairaj to the power output of a wind farm while factoring in the availability of the wind turbines in the farm availability model for the wind turbines, we propose a method to determine the wind-farm power output pdf

  2. A WRF Simulation of the Impact of 3-D Radiative Transfer on Surface Hydrology over the Rocky Mountains and Sierra Nevada

    SciTech Connect (OSTI)

    Liou, K. N.; Gu, Y.; Leung, Lai-Yung R.; Lee, W- L.; Fovell, R. G.

    2013-12-03T23:59:59.000Z

    We investigate 3-D mountains/snow effects on solar flux distributions and their impact on surface hydrology over the western United States, specifically the Rocky Mountains and Sierra Nevada. The Weather Research and Forecasting (WRF) model, applied at a 30 km grid resolution, is used in conjunction with a 3-D radiative transfer parameterization covering a time period from 1 November 2007 to 31 May 2008, during which abundant snowfall occurred. A comparison of the 3-D WRF simulation with the observed snow water equivalent (SWE) and precipitation from Snowpack Telemetry (SNOTEL) sites shows reasonable agreement in terms of spatial patterns and daily and seasonal variability, although the simulation generally has a positive precipitation bias. We show that 3-D mountain features have a profound impact on the diurnal and monthly variation of surface radiative and heat fluxes, and on the consequent elevation dependence of snowmelt and precipitation distributions. In particular, during the winter months, large deviations (3-DPP, in which PP denotes the plane-parallel approach) of the monthly mean surface solar flux are found in the morning and afternoon hours due to shading effects for elevations below 2.5 km. During spring, positive deviations shift to the earlier morning. Over mountaintops higher than 3 km, positive deviations are found throughout the day, with the largest values of 40-60Wm?2 occurring at noon during the snowmelt season of April to May. The monthly SWE deviations averaged over the entire domain show an increase in lower elevations due to reduced snowmelt, which leads to a reduction in cumulative runoff. Over higher elevation areas, positive SWE deviations are found because of increased solar radiation available at the surface. Overall, this study shows that deviations of SWE due to 3-D radiation effects range from an increase of 18%at the lowest elevation range (1.5-2 km) to a decrease of 8% at the highest elevation range (above 3 km). Since lower elevation areas occupy larger fractions of the land surface, the net effect of 3-D radiative transfer is to extend snowmelt and snowmelt-driven runoff into the warm season. Because 60-90% of water resources originate from mountains worldwide, the aforementioned differences in simulated hydrology due solely to 3-D interactions between solar radiation and mountains/snow merit further investigation in order to understand the implications of modeling mountain water resources, and these resourcesí vulnerability to climate change and air pollution.

  3. DUAL-OUTPUT HOLA FIRMWARE AND TESTS

    E-Print Network [OSTI]

    another channel (thus, "dual-output" HOLA) ∑ Another LDC+ROMB block was added to receive data from side S32PCI64 "SOLAR" mezzanine card: Provides access to S-LINK via PCI bus The first prototype of dual-outputDUAL-OUTPUT HOLA FIRMWARE AND TESTS Anton Kapliy Mel Shochet Fukun Tang Daping Weng #12;Summary

  4. Evaluated Crop Evapotranspiration over a Region of Irrigated Orchards with the Improved ACASAĖWRF Model

    E-Print Network [OSTI]

    Falk, Matthias

    Among the uncertain consequences of climate change on agriculture are changes in timing and quantity of precipitation together with predicted higher temperatures and changes in length of growing season. The understanding ...

  5. PNNL-Weather Research and Forecasting (WRF)-Chem Modeling in Mexico | Open

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home5b9fcbce19 No revision hasInformation Earth'sOklahoma/GeothermalOrange County is aOrmesa IOvonicPECO)Energy Information

  6. Control of fuel cell power output Federico Zenith, Sigurd Skogestad *

    E-Print Network [OSTI]

    Skogestad, Sigurd

    Control of fuel cell power output Federico Zenith, Sigurd Skogestad * Department of Chemical A simplified dynamic model for fuel cells is developed, based on the concept of instantaneous characteristic, which is the set of values of current and voltage that a fuel cell can reach instantaneously

  7. Bayesian Learning of unobservable output 1 Bayesian Learning of unobservable output

    E-Print Network [OSTI]

    Provence Aix-Marseille I, Universitť de

    Bayesian Learning of unobservable output 1 Bayesian Learning of unobservable output aggregating the consistency of our method and illustrate its efficiency using simulations. Although up to our knowledge there are no similar algorithms for unobservable output, we compared in our simulations to supervised approaches

  8. Anisotropic Grid Adaptation for Multiple Aerodynamic Outputs

    E-Print Network [OSTI]

    Venditti, David A.

    Anisotropic gridĖadaptive strategies are presented for viscous flow simulations in which the accurate prediction of multiple aerodynamic outputs (such as the lift, drag, and moment coefficients) is required from a single ...

  9. Optimization on Solar Panels: Finding the Optimal Output Brian Y. Lu

    E-Print Network [OSTI]

    Lavaei, Javad

    Optimization on Solar Panels: Finding the Optimal Output Brian Y. Lu January 1, 2013 1 Introduction of solar panel: Routing the configuration between solar cells with a switch matrix. However, their result models and control policies for the optimal output of solar panels. The smallest unit on a solar panel

  10. Quality assurance of solar thermal systems with the ISFH-Input/Output-Procedure

    E-Print Network [OSTI]

    Quality assurance of solar thermal systems with the ISFH- Input/Output-Procedure Peter Paerisch different solar systems. The simulation model was validated with measured data. The deviation between meas * Tel. +49 (0)5151-999503, Fax: +49 (0)5151-999500, Email: paerisch@isfh.de Abstract Input/Output

  11. ASSIMILATION OF DOPPLER RADAR DATA INTO NUMERICAL WEATHER MODELS

    SciTech Connect (OSTI)

    Chiswell, S.; Buckley, R.

    2009-01-15T23:59:59.000Z

    During the year 2008, the United States National Weather Service (NWS) completed an eight fold increase in sampling capability for weather radars to 250 m resolution. This increase is expected to improve warning lead times by detecting small scale features sooner with increased reliability; however, current NWS operational model domains utilize grid spacing an order of magnitude larger than the radar data resolution, and therefore the added resolution of radar data is not fully exploited. The assimilation of radar reflectivity and velocity data into high resolution numerical weather model forecasts where grid spacing is comparable to the radar data resolution was investigated under a Laboratory Directed Research and Development (LDRD) 'quick hit' grant to determine the impact of improved data resolution on model predictions with specific initial proof of concept application to daily Savannah River Site operations and emergency response. Development of software to process NWS radar reflectivity and radial velocity data was undertaken for assimilation of observations into numerical models. Data values within the radar data volume undergo automated quality control (QC) analysis routines developed in support of this project to eliminate empty/missing data points, decrease anomalous propagation values, and determine error thresholds by utilizing the calculated variances among data values. The Weather Research and Forecasting model (WRF) three dimensional variational data assimilation package (WRF-3DVAR) was used to incorporate the QC'ed radar data into input and boundary conditions. The lack of observational data in the vicinity of SRS available to NWS operational models signifies an important data void where radar observations can provide significant input. These observations greatly enhance the knowledge of storm structures and the environmental conditions which influence their development. As the increase in computational power and availability has made higher resolution real-time model simulations possible, the need to obtain observations to both initialize numerical models and verify their output has become increasingly important. The assimilation of high resolution radar observations therefore provides a vital component in the development and utility of numerical model forecasts for both weather forecasting and contaminant transport, including future opportunities to improve wet deposition computations explicitly.

  12. Assessing the Impacts of Different WRF Precipitation Physics in Hurricane Simulations

    E-Print Network [OSTI]

    Nasrollahi, Nasrin; AghaKouchak, Amir; Li, Jialun; Gao, Xiaogang; Hsu, Kuolin; Sorooshian, Soroosh

    2012-01-01T23:59:59.000Z

    ABSTRACT Numerical weather prediction models play a majorthat numerical weather prediction models are particularlymodel with numerical weather prediction models (Olson et al.

  13. PV output smoothing with energy storage.

    SciTech Connect (OSTI)

    Ellis, Abraham; Schoenwald, David Alan

    2012-03-01T23:59:59.000Z

    This report describes an algorithm, implemented in Matlab/Simulink, designed to reduce the variability of photovoltaic (PV) power output by using a battery. The purpose of the battery is to add power to the PV output (or subtract) to smooth out the high frequency components of the PV power that that occur during periods with transient cloud shadows on the PV array. The control system is challenged with the task of reducing short-term PV output variability while avoiding overworking the battery both in terms of capacity and ramp capability. The algorithm proposed by Sandia is purposely very simple to facilitate implementation in a real-time controller. The control structure has two additional inputs to which the battery can respond. For example, the battery could respond to PV variability, load variability or area control error (ACE) or a combination of the three.

  14. Predicting the Energy Output of Wind Farms Based on Weather Data: Important Variables and their Correlation

    E-Print Network [OSTI]

    Vladislavleva, Katya; Neumann, Frank; Wagner, Markus

    2011-01-01T23:59:59.000Z

    Wind energy plays an increasing role in the supply of energy world-wide. The energy output of a wind farm is highly dependent on the weather condition present at the wind farm. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproductions. With this paper, we take a computer science perspective on energy prediction based on weather data and analyze the important parameters as well as their correlation on the energy output. To deal with the interaction of the different parameters we use symbolic regression based on the genetic programming tool DataModeler. Our studies are carried out on publicly available weather and energy data for a wind farm in Australia. We reveal the correlation of the different variables for the energy output. The model obtained for energy prediction gives a very reliable prediction of the energy output for newly given weather data.

  15. Bioenergy technology balancing energy output with environmental

    E-Print Network [OSTI]

    Levi, Ran

    E2.3 Bioenergy technology ­ balancing energy output with environmental benefitsbenefits John standards #12;Is it right to grow bioenergy? Or How much bioenergy production is right? #12;Historical bioenergy Farmers historically used 25% land for horse feed #12;Energy crops are `solar panels' Solar energy

  16. Single Inductor Dual Output Buck Converter

    E-Print Network [OSTI]

    Eachempatti, Haritha

    2010-07-14T23:59:59.000Z

    of value 3V. The main focus areas are low cross regulation between the outputs and supply of completely independent load current levels while maintaining desired values (1.2V,1.5 V) within well controlled ripple levels. Dynamic hysteresis control is used...

  17. Porous radiant burners having increased radiant output

    DOE Patents [OSTI]

    Tong, Timothy W. (Tempe, AZ); Sathe, Sanjeev B. (Tempe, AZ); Peck, Robert E. (Tempe, AZ)

    1990-01-01T23:59:59.000Z

    Means and methods for enhancing the output of radiant energy from a porous radiant burner by minimizing the scattering and increasing the adsorption, and thus emission of such energy by the use of randomly dispersed ceramic fibers of sub-micron diameter in the fabrication of ceramic fiber matrix burners and for use therein.

  18. Anisotropic Grid Adaptation for Multiple Aerodynamic Outputs

    E-Print Network [OSTI]

    Peraire, Jaime

    Anisotropic Grid Adaptation for Multiple Aerodynamic Outputs David A. Venditti and David L Anisotropic grid≠adaptive strategies are presented for viscous flow simulations in which the accurate estimation and Hessian-based anisotropic grid adaptation. Airfoil test cases are presented to demonstrate

  19. Administrator Ready Reference Guide Customizing an Output Style

    E-Print Network [OSTI]

    University of Technology, Sydney

    may be in various sections of the instructions. Some things to look for: - line spacing Preview Utility (Tools, Preview Output Styles) or by simply opening the Output Style Editor (Bibliography, Edit button -- to the right of the output style drop- down). The Output Style Preview Utility

  20. Off-set stabilizer for comparator output

    DOE Patents [OSTI]

    Lunsford, James S. (Los Alamos, NM)

    1991-01-01T23:59:59.000Z

    A stabilized off-set voltage is input as the reference voltage to a comparator. In application to a time-interval meter, the comparator output generates a timing interval which is independent of drift in the initial voltage across the timing capacitor. A precision resistor and operational amplifier charge a capacitor to a voltage which is precisely offset from the initial voltage. The capacitance of the reference capacitor is selected so that substantially no voltage drop is obtained in the reference voltage applied to the comparator during the interval to be measured.

  1. World crude output overcomes Persian Gulf disruption

    SciTech Connect (OSTI)

    Not Available

    1992-02-01T23:59:59.000Z

    Several OPEC producers made good on their promises to replace 2.7 MMbpd of oil exports that vanished from the world market after Iraq took over Kuwait. Even more incredibly, they accomplished this while a breathtaking 1.2- MMbopd reduction in Soviet output took place during the course of 1991. After Abu Dhabi, Indonesia, Iran, Libya, Nigeria, Saudi Arabia and Venezuela turned the taps wide open, their combined output rose 2.95 MMbopd. Put together with a 282,000-bopd increase by Norway and contributions from smaller producers, this enabled world oil production to remain within 400,000 bopd of its 1990 level. The 60.5-MMbopd average was off by just 0.7%. This paper reports that improvement took place in five of eight regions. Largest increases were in Western Europe and Africa. Greatest reductions occurred in Eastern Europe and the Middle East. Fifteen nations produced 1 MMbopd or more last year, compared with 17 during 1990.

  2. SARAH 3.2: Dirac Gauginos, UFO output, and more

    E-Print Network [OSTI]

    Florian Staub

    2013-02-12T23:59:59.000Z

    SARAH is a Mathematica package optimized for the fast, efficient and precise study of supersymmetric models beyond the MSSM: a new model can be defined in a short form and all vertices are derived. This allows SARAH to create model files for FeynArts/FormCalc, CalcHep/CompHep and WHIZARD/OMEGA. The newest version of SARAH now provides the possibility to create model files in the UFO format which is supported by MadGraph 5, MadAnalysis, GoSam, and soon by Herwig++. Furthermore, SARAH also calculates the mass matrices, RGEs and one-loop corrections to the mass spectrum. This information is used to write source code for SPheno in order to create a precision spectrum generator for the given model. This spectrum-generator-generator functionality as well as the output of WHIZARD and CalcHep model files have seen further improvement in this version. Also models including Dirac Gauginos are supported with the new version of SARAH, and additional checks for the consistency of model implementations have been created.

  3. Application of computer voice input/output

    SciTech Connect (OSTI)

    Ford, W.; Shirk, D.G.

    1981-01-01T23:59:59.000Z

    The advent of microprocessors and other large-scale integration (LSI) circuits is making voice input and output for computers and instruments practical; specialized LSI chips for speech processing are appearing on the market. Voice can be used to input data or to issue instrument commands; this allows the operator to engage in other tasks, move about, and to use standard data entry systems. Voice synthesizers can generate audible, easily understood instructions. Using voice characteristics, a control system can verify speaker identity for security purposes. Two simple voice-controlled systems have been designed at Los Alamos for nuclear safeguards applicaations. Each can easily be expanded as time allows. The first system is for instrument control that accepts voice commands and issues audible operator prompts. The second system is for access control. The speaker's voice is used to verify his identity and to actuate external devices.

  4. Coordinated Output Regulation of Multiple Heterogeneous Linear Systems

    E-Print Network [OSTI]

    Dimarogonas, Dimos

    , the generalizations of coordination of multiple linear dynamic systems to the cooperative output regulation problemCoordinated Output Regulation of Multiple Heterogeneous Linear Systems Ziyang Meng, Tao Yang, Dimos V. Dimarogonas, and Karl H. Johansson Abstract-- The coordinated output regulation problem

  5. Quality assurance with the ISFH-Input/Output-Procedure 6-year-experience with 14 solar thermal systems

    E-Print Network [OSTI]

    Quality assurance with the ISFH-Input/Output-Procedure 6-year-experience with 14 solar thermal the confidence in solar thermal energy. The so called Input/Output-Procedure is controlling the solar heat systems. The simulation model was validated with measured data and a lot of failures in 11 solar thermal

  6. Simulations of Organic Aerosol Concentrations in Mexico City Using the WRF-CHEM Model during the MCMA-2006/MILAGRO Campaign

    E-Print Network [OSTI]

    Meskhidze, Nicholas

    ) Aerodyne Research Inc, Billerica, MA, USA * Correspondence to: G. Li (lgh@mce2.org) and L.T. Molina

  7. SWAT 2012 Input/Output Documentation

    E-Print Network [OSTI]

    Arnold, J.G.; Kiniry, J.R.; Srinivasan, R.; Williams, J.R.; Haney, E.B.; Neitsch, S.L.

    2013-03-04T23:59:59.000Z

    The Soil and Water Assessment Tool (SWAT) is a comprehensive model that requires a diversity of information in order to run. Novice users may feel overwhelmed by the variety and number of inputs when they first begin to use the model. This document...

  8. action potential output: Topics by E-print Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    HF efficiency, but does not necessarily yield a higher measurable power (power in the bucket). For low Carroll, David L. 376 A Spatial Analysis of Multivariate Output from...

  9. advisory capability output: Topics by E-print Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    HF efficiency, but does not necessarily yield a higher measurable power (power in the bucket). For low Carroll, David L. 453 A Spatial Analysis of Multivariate Output from...

  10. NREL: Wind Research - Boosting Wind Plant Power Output by 4%...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Boosting Wind Plant Power Output by 4%-5% through Coordinated Turbine Controls July 30, 2014 Wind plant underperformance has plagued wind plant developers for years. To address...

  11. Single-photon quantum router with multiple output ports

    E-Print Network [OSTI]

    Wei-Bin Yan; Heng Fan

    2013-11-26T23:59:59.000Z

    We study the multi-channel quantum routing of the single photons in a waveguide-emitter system. The channels are composed by the waveguides and are connected by intermediate two-level emitters. By adjusting the intermediate emitters, the output channels of the input single photons can be controlled. This is demonstrated for the cases of one output channel, two output channels and the generic N output channels. The results show that the multi-channel quantum routing of single photons can be well achieved in present system. This sheds light on the experimental realization of quantum routing of single photons.

  12. Bayesian analysis of computer code outputs

    E-Print Network [OSTI]

    Marc C Kennedy; Anthony O& apos; Hagan; Neil Higgins

    2002-01-01T23:59:59.000Z

    real-world phenomena. They are typically used to predict the corresponding real-world phenomenon, as in the following examples. Modern weather forecasting is done using enormously complex models of the atmosphere (and its interactions with land and sea). The primary intention is to predict future weather, given information about current conditions. Manufacturers of motor car engines build models to predict their behaviour. They are used to explore possible variations in engine design, and thereby to avoid the time and expense of actually building many unsuccessful variants in the search for an improved design. Water engineers build network áow models of sewer systems, in order to predict where problems of surcharging and áooding will arise under rainstorm conditions. They are then used to explore changes to the network to solve those problems. Models of atmospheric dispersion are used to predict the spread and deposition

  13. GAMS program used to estimate capacity output using a distance function with both good and bad output, variable returns to scale and weak disposability of the bad outputs.

    E-Print Network [OSTI]

    ." VIMS Marine resource Report N. 2007-6. August 2007. Author: John B. Walden NMFS/NEFSC 166 Water St(obs) weights ; POSITIVE Variable weight, lambda; EQUATIONS CONSTR1(GOUTPUT, OBS) DEA constraint for each output

  14. TRIPLE OUTPUT POWER SUPPLY Agilent MODEL E3630A

    E-Print Network [OSTI]

    Ravikumar, B.

    , manufacture, and intended use of the instrument. Agilent Technologies assumes no liability for the customer, the instrument chassis and cabinet must be connected to an electrical ground. The instrument must be connected to the ac power supply mains through a three-conductor power cable, with the third wire firmly connected

  15. FAST MULTI-CLASS IMAGE ANNOTATION WITH RANDOM SUBWINDOWS AND MULTIPLE OUTPUT RANDOMIZED TREES

    E-Print Network [OSTI]

    Wehenkel, Louis

    of Electrical Engineering and Computer Science 2Bioinformatics and Modeling - GIGA-R University of Li`ege, Sart annotation, machine learning, decision trees, extremely randomized trees, structured outputs Abstract significantly outperforms the basic method and shows good performances with respect to the state

  16. Analytical input-output and supply chain study of China's coke and steel sectors

    E-Print Network [OSTI]

    Li, Yu, 1976-

    2004-01-01T23:59:59.000Z

    I design an input-output model to investigate the energy supply chain of coal-coke-steel in China. To study the demand, supply, and energy-intensity issues for coal and coke from a macroeconomic perspective, I apply the ...

  17. Abstract: Wind Energy Conversion Systems (WECS) produce fluctuating output power, which may cause voltage fluctuations and

    E-Print Network [OSTI]

    Gross, George

    : An approach to model the solar cell system with coupled multi-physics equations (photovoltaic, electrothermalAbstract: Wind Energy Conversion Systems (WECS) produce fluctuating output power, which may cause in a network of any size can be performed. An algorithm for flicker measurement in the frequency do- main

  18. Cooling output optimization of an air handling unit Andrew Kusiak *, Mingyang Li

    E-Print Network [OSTI]

    Kusiak, Andrew

    supply temperature and supply air temperature in response to the dynamic cooling load and changingCooling output optimization of an air handling unit Andrew Kusiak *, Mingyang Li Department mining Neural network Multi-objective optimization Evolutionary computation Dynamic modeling Cooling

  19. Challenges in Predicting Power Output from Offshore Wind Farms

    E-Print Network [OSTI]

    Pryor, Sara C.

    Challenges in Predicting Power Output from Offshore Wind Farms R. J. Barthelmie1 and S. C. Pryor2 Abstract: Offshore wind energy is developing rapidly in Europe and the trend is towards large wind farms an offshore wind farm, accurate assessment of the wind resource/power output from the wind farm is a necessity

  20. ANALOG-DIGITAL INPUT OUTPUT SYSTEM FOR APPLE CO

    E-Print Network [OSTI]

    Groppi, Christopher

    ADIOS ANALOG-DIGITAL INPUT OUTPUT SYSTEM FOR APPLE CO NATIONAL RADIO ASTRONOMY OBSERVATORY TABLES ADIOS - ANALOG-DIGITAL INPUT OUTPUT SYSTEM FOR APPLE COMPUTER TABLE FOR CONTENTS Page I Module and Apple Card (Photograph) Figure 3 Complete Apple/ADIOS System (Photograph) Figure 4 Analog

  1. Most efficient quantum thermoelectric at finite power output

    E-Print Network [OSTI]

    Robert S. Whitney

    2014-03-13T23:59:59.000Z

    Machines are only Carnot efficient if they are reversible, but then their power output is vanishingly small. Here we ask, what is the maximum efficiency of an irreversible device with finite power output? We use a nonlinear scattering theory to answer this question for thermoelectric quantum systems; heat engines or refrigerators consisting of nanostructures or molecules that exhibit a Peltier effect. We find that quantum mechanics places an upper bound on both power output, and on the efficiency at any finite power. The upper bound on efficiency equals Carnot efficiency at zero power output, but decays with increasing power output. It is intrinsically quantum (wavelength dependent), unlike Carnot efficiency. This maximum efficiency occurs when the system lets through all particles in a certain energy window, but none at other energies. A physical implementation of this is discussed, as is the suppression of efficiency by a phonon heat flow.

  2. Relationship Among Efficiency and Output Power of Heat Energy Converters

    E-Print Network [OSTI]

    Alexander Luchinskiy

    2004-09-02T23:59:59.000Z

    Relationship among efficiency and output power of heat-electric energy converters as well as of any converters for transforming of heat energy into any other kind of energy is considered. It is shown, that the parameter efficiency does not determine univocally the output power of a converter. It is proposed to use another parameter for determination of working ability of heat energy converters. It is shown, that high output power can not be achieved by any kind of Stirling-type converters in spite of their high efficiency.

  3. Steady-state bumpless transfer under controller uncertainty using the state/output feedback topology

    SciTech Connect (OSTI)

    Zheng, K.; Lee, A.H.; Bentsman, J.; Taft, C.W. [University of Illinois, Urbana, IL (United States)

    2006-01-15T23:59:59.000Z

    Linear quadratic (LQ) bumpless transfer design introduced recently by Turner and Walker gives a very convenient and straightforward computational procedure for the steady-state bumpless transfer operator synthesis. It is, however, found to be incapable of providing convergence of the output of the offline controller to that of the online controller in several industrial applications, producing bumps in the plant output in the wake of controller transfer. An examination of this phenomenon reveals that the applications in question are characterized by a significant mismatch, further referred to as controller uncertainty, between the dynamics of the implemented controllers and their models used in the transfer operator computation. To address this problem, while retaining the convenience of the Turner and Walker design, a novel state/output feedback bumpless transfer topology is introduced that employs the nominal state of the offline controller and, through the use of an additional controller/model mismatch compensator, also the offline controller output. A corresponding steady-state bumpless transfer design procedure along with the supporting theory is developed for a large class of systems. Due to these features, it is demonstrated to solve a long-standing problem of high-quality steady-state bumpless transfer from the industry standard low-order nonlinear multiloop PID-based controllers to the modern multiinput-multioutput (MIMO) robust controllers in the megawatt/throttle pressure control of a typical coal-fired boiler/turbine unit.

  4. AMPS, a real-time mesoscale modeling system, has provided a decade of service for scientific and logistical needs and has helped advance polar numerical weather prediction

    E-Print Network [OSTI]

    Howat, Ian M.

    and logistical needs and has helped advance polar numerical weather prediction as well as understanding support for the USAP. The concern at the time was the numerical weather prediction (NWP) guidance-time implementation of the Weather Research and Forecasting model (WRF; Skamarock et al. 2008) to support the U

  5. Computability in Anonymous Networks: Revocable vs. Irrecovable Outputs

    E-Print Network [OSTI]

    Computability in Anonymous Networks: Revocable vs. Irrecovable Outputs Yuval Emek1 , Jochen Seidel2, and leader election. 1 Introduction We study computability in networks, referred to hereafter as distributed

  6. Development of Regional Wind Resource and Wind Plant Output Datasets...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    50-47676 March 2010 Development of Regional Wind Resource and Wind Plant Output Datasets Final Subcontract Report 15 October 2007 - 15 March 2009 3TIER Seattle, Washington National...

  7. The Effect of Signal Quality on Six Cardiac Output Estimators

    E-Print Network [OSTI]

    Mark, Roger Greenwood

    The effect of signal quality on the accuracy of cardiac output (CO) estimation from arterial blood pressure (ABP) was evaluated using data from the MIMIC II database. Thermodilution CO (TCO) was the gold standard. A total ...

  8. Corticospinal Output to Hindlimb Muscles in the Primate

    E-Print Network [OSTI]

    Hudson, Heather M

    2011-05-31T23:59:59.000Z

    The overall goal of this study was to investigate the properties of corticospinal output to a wide range of hindlimb muscles in the primate and to map the representation of individual muscles in hindlimb motor cortex. ...

  9. Grid adaptation for functional outputs of compressible flow simulations

    E-Print Network [OSTI]

    Venditti, David Anthony, 1973-

    2002-01-01T23:59:59.000Z

    An error correction and grid adaptive method is presented for improving the accuracy of functional outputs of compressible flow simulations. The procedure is based on an adjoint formulation in which the estimated error in ...

  10. Development and application of WRF3.3-CLM4crop to study of agriculture - climate interaction

    E-Print Network [OSTI]

    Lu, Yaqiong

    2013-01-01T23:59:59.000Z

    Global Climate Change and United-States Agriculture, Nature,climate modeling Land surface modeling Agriculture and climate interaction Land use change

  11. Collaborative Research: Towards Advanced Understanding and Predictive Capability of Climate Change in the Arctic Using a High-Resolution Regional Arctic Climate Model

    SciTech Connect (OSTI)

    Cassano, John [Principal Investigator

    2013-06-30T23:59:59.000Z

    The primary research task completed for this project was the development of the Regional Arctic Climate Model (RACM). This involved coupling existing atmosphere, ocean, sea ice, and land models using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM) coupler (CPL7). RACM is based on the Weather Research and Forecasting (WRF) atmospheric model, the Parallel Ocean Program (POP) ocean model, the CICE sea ice model, and the Variable Infiltration Capacity (VIC) land model. A secondary research task for this project was testing and evaluation of WRF for climate-scale simulations on the large pan-Arctic model domain used in RACM. This involved identification of a preferred set of model physical parameterizations for use in our coupled RACM simulations and documenting any atmospheric biases present in RACM.

  12. The world of quantum noise and the fundamental output process

    E-Print Network [OSTI]

    V. P. Belavkin; O. Hirota; R. Hudson

    2005-10-04T23:59:59.000Z

    A stationary theory of quantum stochastic processes of second order is outlined. It includes KMS processes in wide sense like the equilibrium finite temperature quantum noise given by the Planck's spectral formula. It is shown that for each stationary noise there exists a natural output process output process which is identical to the noise in the infinite temperature limit, and flipping with the noise if the time is reversed at finite temperature. A canonical Hilbert space representation of the quantum noise and the fundamental output process is established and a decomposition of their spectra is found. A brief explanation of quantum stochastic integration with respect to the input-output processes is given using only correlation functions. This provides a mathematical foundation for linear stationary filtering transformations of quantum stochastic processes. It is proved that the colored quantum stationary noise and its time-reversed version can be obtained in the second order theory by a linear nonadapted filtering of the standard vacuum noise uniquely defined by the canonical creation and annihilation operators on the spectrum of the input-output pair.

  13. Microwave generated electrodeless lamp for producing bright output

    SciTech Connect (OSTI)

    Wood, Ch. H.; Ury, M. G.

    1985-03-26T23:59:59.000Z

    A microwave generated electrodeless light source for producing a bright output comprising a lamp structure including a microwave chamber and a plasma medium-containing lamp envelope having a maximum dimension which is substantially less than a wavelength disposed therein. To provide the desired radiation output the interior of the chamber is coated with a UV-reflective material and the chamber has an opening for allowing UV radiation to exit, which is covered with a metallic mesh. The chamber is arranged to be near-resonant at a single wavelength, and the lamp envelope has a fill including mercury at an operating pressure of 1-2 atmospheres, while a power density of at least 250-300 (watts/cm/sup 3/) is coupled to the envelope to result in a relatively high deep UV output at a relatively high brightness.

  14. Self-consistent input-output formulation of quantum feedback

    SciTech Connect (OSTI)

    Yanagisawa, M. [Department of Engineering, The Australian National University, Canberra, ACT 0200 (Australia); Hope, J. J. [Department of Quantum Science, The Australian National University, Canberra, ACT 0200 (Australia)

    2010-12-15T23:59:59.000Z

    A simple method of analyzing quantum feedback circuits is presented. The classical analysis of feedback circuits can be generalized to apply to quantum systems by mapping the field operators of various outputs to other inputs via the standard input-output formalism. Unfortunately, this has led to unphysical results such as the violation of the Heisenberg uncertainty principle for in-loop fields. This paper shows that this general approach can be redeemed by ensuring a self-consistently Hermitian Hamiltonian. The calculations are based on a noncommutative calculus of operator derivatives. A full description of several examples of quantum linear and nonlinear feedback for optical systems is presented.

  15. Output power characteristics and performance of TOPAZ II Thermionic Fuel Element No. 24

    SciTech Connect (OSTI)

    Luchau, D.W.; Bruns, D.R. [Team Specialty Services, Inc., TOPAZ International Program, 901 University Blvd., SE, Albuquerque, New Mexico 87106 (United States); Izhvanov, O.; Androsov, V. [JV INERTEK, Scientific Industrial Association ``Luch``, 24 Zheleznodorozhnaya, Podolsk, (Russia) 142100

    1996-03-01T23:59:59.000Z

    A final report on the output power characteristics and capabilities of single cell TOPAZ II Thermionic Fuel Element (TFE) No. 24 is presented. Thermal power tests were conducted for over 3000 hours to investigate converter performance under normal and adverse operating conditions. Experiments conducted include low power testing, high power testing, air introduction to the interelectrode gap, collector temperature optimization, thermal modeling, and output power characteristic measurements. During testing, no unexpected degradation in converter performance was observed. The TFE has been removed from the test stand and returned to Scientific Industrial Association {open_quote}{open_quote}LUCH{close_quote}{close_quote} for materials analysis and report. This research was conducted at the Thermionic System Evaluation Test (TSET) Facility at the New Mexico Engineering Research Institute (NMERI) as a part of the Topaz International Program (TIP) by the Air Force Phillips Laboratory (PL). {copyright} {ital 1996 American Institute of Physics.}

  16. Development of a 402.5 MHz 140 kW Inductive Output Tube

    SciTech Connect (OSTI)

    R. Lawrence Ives; Michael Read, Robert Jackson

    2012-05-09T23:59:59.000Z

    This report contains the results of Phase I of an SBIR to develop a Pulsed Inductive Output Tube (IOT) with 140 kW at 400 MHz for powering H-proton beams. A number of sources, including single beam and multiple beam klystrons, can provide this power, but the IOT provides higher efficiency. Efficiencies exceeding 70% are routinely achieved. The gain is typically limited to approximately 24 dB; however, the availability of highly efficient, solid state drivers reduces the significance of this limitation, particularly at lower frequencies. This program initially focused on developing a 402 MHz IOT; however, the DOE requirement for this device was terminated during the program. The SBIR effort was refocused on improving the IOT design codes to more accurately simulate the time dependent behavior of the input cavity, electron gun, output cavity, and collector. Significant improvement was achieved in modeling capability and simulation accuracy.

  17. Convergent relaxations of polynomial matrix inequalities and static output feedback

    E-Print Network [OSTI]

    Henrion, Didier

    (LMI) relaxations to solve non-convex polynomial matrix in- equality (PMI) optimization problems minimizers that satisfy the PMI. The approach is successfully applied to PMIs arising from static output- mulated as polynomial matrix inequality (PMI) optimization problems in the controller parameters

  18. Output-Sensitive Algorithms for Tukey Depth and Related Problems

    E-Print Network [OSTI]

    Morin, Pat

    Output-Sensitive Algorithms for Tukey Depth and Related Problems David Bremner University of New de Bruxelles Pat Morin Carleton University Abstract The Tukey depth (Tukey 1975) of a point p halfspace that contains p. Algorithms for computing the Tukey depth of a point in various dimensions

  19. TRICOLOR LIGHT EMITTING DIODE DOT MATRIX DISPLAY SYSTEM WITHAUDIO OUTPUT

    E-Print Network [OSTI]

    Pang, Grantham

    1 TRICOLOR LIGHT EMITTING DIODE DOT MATRIX DISPLAY SYSTEM WITHAUDIO OUTPUT Grantham Pang, Chi emitting diodes; tricolor display; audio communication. I. Introduction This paper relates to a tricolor broadcasting through the visible light rays transmitted by the display panel or assembly. Keywords: light

  20. Soft-Input Soft-Output Sphere Decoding Christoph Studer

    E-Print Network [OSTI]

    Soft-Input Soft-Output Sphere Decoding Christoph Studer Integrated Systems Laboratory ETH Zurich Laboratory ETH Zurich, 8092 Zurich, Switzerland Email: boelcskei@nari.ee.ethz.ch Abstract--Soft-input soft, 8092 Zurich, Switzerland Email: studer@iis.ee.ethz.ch Helmut Bölcskei Communication Technology

  1. The effects of output transformers on distortion in audio amplifiers

    E-Print Network [OSTI]

    Lanier, Ross Edwin

    1949-01-01T23:59:59.000Z

    Introduction ~. . . . . . . . , . . . . . . ~. . . . . 7 Frequency Discrimination. . . . . . . . . . . . . . . . 9 Harmonic Distortion. ~ ~. . . . ~ 21 Distortion by the Intermodulationmethod. . . . . . . . 47 Comparison of Harmonic and Intermodulation... current in the primary as a function of frequency . 19 Output voltage of transformer 3 without direct current in the primary as a function of frequency 20 Block diagram for measuring distortion by the harmonic method 26 Per cent harmonic distortion...

  2. The continuity of the output entropy of positive maps

    SciTech Connect (OSTI)

    Shirokov, Maxim E [Steklov Mathematical Institute, Russian Academy of Sciences, Moscow (Russian Federation)

    2011-10-31T23:59:59.000Z

    Global and local continuity conditions for the output von Neumann entropy for positive maps between Banach spaces of trace-class operators in separable Hilbert spaces are obtained. Special attention is paid to completely positive maps: infinite dimensional quantum channels and operations. It is shown that as a result of some specific properties of the von Neumann entropy (as a function on the set of density operators) several results on the output entropy of positive maps can be obtained, which cannot be derived from the general properties of entropy type functions. In particular, it is proved that global continuity of the output entropy of a positive map follows from its finiteness. A characterization of positive linear maps preserving continuity of the entropy (in the following sense: continuity of the entropy on an arbitrary subset of input operators implies continuity of the output entropy on this subset) is obtained. A connection between the local continuity properties of two completely positive complementary maps is considered. Bibliography: 21 titles.

  3. Maximizing output from oil reservoirs without water breakthrough

    E-Print Network [OSTI]

    Lucas, Stephen

    Maximizing output from oil reservoirs without water breakthrough S.K. Lucas School of Mathematics, revised May 2003, published 45(3), 2004, 401­422 Abstract Often in oil reservoirs a layer of water lies, for example, Muskat [8], Bear [1]). When oil is removed from the reservoir by an oil well, it will generate

  4. Review of Wind Energy Forecasting Methods for Modeling Ramping Events

    SciTech Connect (OSTI)

    Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R

    2011-03-28T23:59:59.000Z

    Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.

  5. Model Development Development of a system emulating the global carbon cycle in Earth system models

    E-Print Network [OSTI]

    K. Tachiiri; J. C. Hargreaves; J. D. Annan; A. Oka; A. Abe-ouchi; M. Kawamiya

    2010-01-01T23:59:59.000Z

    developed a loosely coupled model (LCM) which can represent the outputs of a GCMbased Earth system model

  6. Simulation of one-minute power output from utility-scale photovoltaic generation systems.

    SciTech Connect (OSTI)

    Stein, Joshua S.; Ellis, Abraham; Hansen, Clifford W.

    2011-08-01T23:59:59.000Z

    We present an approach to simulate time-synchronized, one-minute power output from large photovoltaic (PV) generation plants in locations where only hourly irradiance estimates are available from satellite sources. The approach uses one-minute irradiance measurements from ground sensors in a climatically and geographically similar area. Irradiance is translated to power using the Sandia Array Performance Model. Power output is generated for 2007 in southern Nevada are being used for a Solar PV Grid Integration Study to estimate the integration costs associated with various utility-scale PV generation levels. Plant designs considered include both fixed-tilt thin-film, and single-axis-tracked polycrystalline Si systems ranging in size from 5 to 300 MW{sub AC}. Simulated power output profiles at one-minute intervals were generated for five scenarios defined by total PV capacity (149.5 MW, 222 WM, 292 MW, 492 MW, and 892 MW) each comprising as many as 10 geographically separated PV plants.

  7. Optical device with conical input and output prism faces

    DOE Patents [OSTI]

    Brunsden, Barry S. (Chicago, IL)

    1981-01-01T23:59:59.000Z

    A device for radially translating radiation in which a right circular cylinder is provided at each end thereof with conical prism faces. The faces are oppositely extending and the device may be severed in the middle and separated to allow access to the central part of the beam. Radiation entering the input end of the device is radially translated such that radiation entering the input end at the perimeter is concentrated toward the output central axis and radiation at the input central axis is dispersed toward the output perimeter. Devices are disclosed for compressing beam energy to enhance drilling techniques, for beam manipulation of optical spatial frequencies in the Fourier plane and for simplification of dark field and color contrast microscopy. Both refracting and reflecting devices are disclosed.

  8. Reliable Gas Turbine Output: Attaining Temperature Independent Performance

    E-Print Network [OSTI]

    Neeley, J. E.; Patton, S.; Holder, F.

    RELIABLE GAS TURBINE OUTPUT; ATTAINING TEMPERATURE INDEPENDENT PERFORMANCE James E. Neeley, P.E. Power Plant Engineer Public Utility Commission of Texas Austin, Texas ABSTRACT Improvements in gas turbine efficiency, coupled... with dropping gas prices, has made gas turbines a popular choice of utilities to supply peaking as well as base load power in the form of combined cycle power plants. Today, because of the gas turbine's compactness, low maintenance, and high levels...

  9. Development of output user interface software to support analysis

    SciTech Connect (OSTI)

    Wahanani, Nursinta Adi, E-mail: sintaadi@batan.go.id; Natsir, Khairina, E-mail: sintaadi@batan.go.id; Hartini, Entin, E-mail: sintaadi@batan.go.id [Center for Development of Nuclear Informatics - National Nuclear Energy Agency, PUSPIPTEK, Serpong, Tangerang, Banten (Indonesia)

    2014-09-30T23:59:59.000Z

    Data processing software packages such as VSOP and MCNPX are softwares that has been scientifically proven and complete. The result of VSOP and MCNPX are huge and complex text files. In the analyze process, user need additional processing like Microsoft Excel to show informative result. This research develop an user interface software for output of VSOP and MCNPX. VSOP program output is used to support neutronic analysis and MCNPX program output is used to support burn-up analysis. Software development using iterative development methods which allow for revision and addition of features according to user needs. Processing time with this software 500 times faster than with conventional methods using Microsoft Excel. PYTHON is used as a programming language, because Python is available for all major operating systems: Windows, Linux/Unix, OS/2, Mac, Amiga, among others. Values that support neutronic analysis are k-eff, burn-up and mass Pu{sup 239} and Pu{sup 241}. Burn-up analysis used the mass inventory values of actinide (Thorium, Plutonium, Neptunium and Uranium). Values are visualized in graphical shape to support analysis.

  10. NCPART: management of ICEMDDN output for numerical control users

    SciTech Connect (OSTI)

    Rossini, B.F.

    1986-04-01T23:59:59.000Z

    NCPART is a procedure developed by the Numerical Control Department at Bendix Kansas City Division to handle the entry to and exit from ICEMDDN, and process all of the local files output by ICEMDDN. The NCPART procedure is menu driven, and provides automatic access to ICEMDDN and any files necessary to process information with ICEM for numerical Control users. Basically, the procedure handles all of the ICEMDDN operations that involve operating system commands, and frees the NC programmer to concentrate on his/her work as a programmer.

  11. Waveguide submillimetre laser with a uniform output beam

    SciTech Connect (OSTI)

    Volodenko, A V; Gurin, O V; Degtyarev, A V; Maslov, Vyacheslav A; Svich, V A; Topkov, A N [V.N. Karazin Kharkiv National University, Kharkiv (Ukraine)

    2007-01-31T23:59:59.000Z

    A method for producing non-Gaussian light beams with a uniform intensity profile is described. The method is based on the use of a combined waveguide quasi-optical resonator containing a generalised confocal resonator with an inhomogeneous mirror with absorbing inhomogeneities discretely located on its surface and a hollow dielectric waveguide whose size satisfies the conditions of self-imaging of a uniform field in it. The existence of quasi-homogeneous beams at the output of an optically pumped 0.1188-mm waveguide CH{sub 3}OH laser with a amplitude-stepped mirror is confirmed theoretically and experimentally. (lasers)

  12. Output-Based Error Estimation and Adaptation for Uncertainty Quantification

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsrucLasDelivered¬ČPNG IHDR¬Ä√ćSolar Energy SystemsFebruary 7-8,March 8,8)Normal 27 1 54InOutput-Based

  13. Method and system for managing an electrical output of a turbogenerator

    DOE Patents [OSTI]

    Stahlhut, Ronnie Dean (Bettendorf, IA); Vuk, Carl Thomas (Denver, IA)

    2010-08-24T23:59:59.000Z

    The system and method manages an electrical output of a turbogenerator in accordance with multiple modes. In a first mode, a direct current (DC) bus receives power from a turbogenerator output via a rectifier where turbogenerator revolutions per unit time (e.g., revolutions per minute (RPM)) or an electrical output level of a turbogenerator output meet or exceed a minimum threshold. In a second mode, if the turbogenerator revolutions per unit time or electrical output level of a turbogenerator output are less than the minimum threshold, the electric drive motor or a generator mechanically powered by the engine provides electrical energy to the direct current bus.

  14. Enhancing e-waste estimates: Improving data quality by multivariate InputĖOutput Analysis

    SciTech Connect (OSTI)

    Wang, Feng, E-mail: fwang@unu.edu [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Huisman, Jaco [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Stevels, Ab [Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Baldť, Cornelis Peter [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Statistics Netherlands, Henri Faasdreef 312, 2492 JP Den Haag (Netherlands)

    2013-11-15T23:59:59.000Z

    Highlights: ē A multivariate InputĖOutput Analysis method for e-waste estimates is proposed. ē Applying multivariate analysis to consolidate data can enhance e-waste estimates. ē We examine the influence of model selection and data quality on e-waste estimates. ē Datasets of all e-waste related variables in a Dutch case study have been provided. ē Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate InputĖOutput Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e-waste estimation studies.

  15. A Wavelet-Based Variability Model (WVM) for Solar PV Power Plants

    E-Print Network [OSTI]

    Lave, Matthew; Kleissl, Jan; Stein, Joshua S

    2013-01-01T23:59:59.000Z

    Model (WVM) for Solar PV Power Plants Matthew Lave, Jansolar photovoltaic (PV) power plant output given a singleproduce a simulated power plant output. The WVM is validated

  16. Design of a 3.3 V analog video line driver with controlled output impedance

    E-Print Network [OSTI]

    Ramachandran, Narayan Prasad

    2004-09-30T23:59:59.000Z

    impedance of the line. The main requirements for design are high output swing, high linearity, matched impedance to the line and power efficiency. These requirements are addressed by a class AB amplifier whose output impedance can be controlled through...

  17. Predicting the Power Output of Distributed Renewable Energy Resources within a Broad Geographical Region

    E-Print Network [OSTI]

    Chalkiadakis, Georgios

    Predicting the Power Output of Distributed Renewable Energy Resources within a Broad Geographical potentially dis- tributed renewable energy resources (su years, estimating the power output of in- herently intermittent and potentially distributed renewable

  18. Spatial Interference Mitigation for Multiple Input Multiple Output Ad Hoc Networks: MISO Gains

    E-Print Network [OSTI]

    Paris-Sud XI, Universitť de

    Spatial Interference Mitigation for Multiple Input Multiple Output Ad Hoc Networks: MISO Gains beamforming for a multiple input single output (MISO) ad hoc network to increase the density of successful

  19. Regulatory Reform to Promote Clean Energy: The Potential of Output-Based Emissions Standards

    SciTech Connect (OSTI)

    Cox, Matthew [Georgia Institute of Technology] [Georgia Institute of Technology; Brown, Dr. Marilyn Ann [Georgia Institute of Technology] [Georgia Institute of Technology; Jackson, Roderick K [ORNL] [ORNL

    2011-01-01T23:59:59.000Z

    Barriers to industrial energy-efficient technologies hinder their use. A number of EPA analyses and industrial experts have found that the utilization of input-based emissions standards (measured in parts-per-million or pounds/MMBtu) in the Clean Air Act creates a regulatory barrier to the installation and deployment of technologies that emit fewer criteria pollutants and use energy more efficiently. Changing emission management strategies to an output-based emissions standard (measured in tons of pollutant emitted) is a way to ameliorate some of these barriers. Combined heat and power (CHP) is one of the key technologies that would see increased industrial application if the emissions standards were modified. Many states have made this change since the EPA first approved it in 2000, although direction from the Federal government could speed implementation modifications. To analyze the national impact of accelerated state adoption of output-based standards on CHP technologies, this paper uses detailed National Energy Modeling System (NEMS) and spreadsheet analysis illustrating two phased-in adoption scenarios for output-based emissions standards in the industrial sector. Benefit/cost metrics are calculated from a private and public perspective, and also a social perspective that considers the criteria and carbon air pollution emissions. These scenarios are compared to the reference case of AEO 2010 and are quite favorable, with a social benefit-cost ratio of 16.0 for a five-year phase-in scenario. In addition, the appropriateness of the Federal role, applicability, technology readiness, and administrative feasibility are discussed.

  20. The electrical and lumen output characteristics of an RF lamp

    SciTech Connect (OSTI)

    Alexandrovich, B.M.; Godyak, V.A.; Piejak, R.B. [Osram Sylvania Inc., Beverly, MA (United States)

    1996-12-31T23:59:59.000Z

    Low pressure rf discharges have been studied for over a century. Their first practical application for lighting was proposed by Tesla in 1891. Since then hundreds of patents have been published attempting to implement rf lighting. However, progress in understanding rf discharge phenomena (mostly driven by plasma processing needs) and dramatic improvement in the performance/cost ratio of rf power sources have recently opened the door for development of rf light sources. Today commercial inductively coupled electrodeless lamps are offered by Matsuhita, Philips and GE. In this work the authors present measurements of the electrical characteristics and lumen output from a 2.65 MHz driven inductively coupled light source. Measurements were made on a spherical lamp of 3.125 inch diameter with a re-entrant cavity that houses a cylindrical ferrite core around which is wrapped the primary coil.

  1. Quantum teleportation scheme by selecting one of multiple output ports

    E-Print Network [OSTI]

    Satoshi Ishizaka; Tohya Hiroshima

    2009-04-06T23:59:59.000Z

    The scheme of quantum teleportation, where Bob has multiple (N) output ports and obtains the teleported state by simply selecting one of the N ports, is thoroughly studied. We consider both deterministic version and probabilistic version of the teleportation scheme aiming to teleport an unknown state of a qubit. Moreover, we consider two cases for each version: (i) the state employed for the teleportation is fixed to a maximally entangled state, and (ii) the state is also optimized as well as Alice's measurement. We analytically determine the optimal protocols for all the four cases, and show the corresponding optimal fidelity or optimal success probability. All these protocols can achieve the perfect teleportation in the asymptotic limit of $N\\to\\infty$. The entanglement properties of the teleportation scheme are also discussed.

  2. GAMS program used to estimate capacity output using a distance function with both desirable and undesirable outputs, and weak disposability for the undesirable outputs.

    E-Print Network [OSTI]

    ." VIMS Marine resource Report N. 2007-6. August 2007. Author: John B. Walden NMFS/NEFSC 166 Water St(obs,var) variuable input utilization rate weight(obs) weights ; POSITIVE Variable weight, lambda; EQUATIONS CONSTR1 /dd_res_crs.txt/ MODEL CAP /ALL/; /*Use all the equations. Alternatively, the model could be solved

  3. Dosimetric characterization and output verification for conical brachytherapy surface applicators. Part I. Electronic brachytherapy source

    SciTech Connect (OSTI)

    Fulkerson, Regina K., E-mail: rmkenned@gmail.com; Micka, John A.; DeWerd, Larry A. [Department of Medical Physics, University of WisconsinĖMadison, Madison, Wisconsin 53705 (United States)] [Department of Medical Physics, University of WisconsinĖMadison, Madison, Wisconsin 53705 (United States)

    2014-02-15T23:59:59.000Z

    Purpose: Historically, treatment of malignant surface lesions has been achieved with linear accelerator based electron beams or superficial x-ray beams. Recent developments in the field of brachytherapy now allow for the treatment of surface lesions with specialized conical applicators placed directly on the lesion. Applicators are available for use with high dose rate (HDR){sup 192}Ir sources, as well as electronic brachytherapy sources. Part I of this paper will discuss the applicators used with electronic brachytherapy sources; Part II will discuss those used with HDR {sup 192}Ir sources. Although the use of these applicators has gained in popularity, the dosimetric characteristics including depth dose and surface dose distributions have not been independently verified. Additionally, there is no recognized method of output verification for quality assurance procedures with applicators like these. Existing dosimetry protocols available from the AAPM bookend the cross-over characteristics of a traditional brachytherapy source (as described by Task Group 43) being implemented as a low-energy superficial x-ray beam (as described by Task Group 61) as observed with the surface applicators of interest. Methods: This work aims to create a cohesive method of output verification that can be used to determine the dose at the treatment surface as part of a quality assurance/commissioning process for surface applicators used with HDR electronic brachytherapy sources (Part I) and{sup 192}Ir sources (Part II). Air-kerma rate measurements for the electronic brachytherapy sources were completed with an Attix Free-Air Chamber, as well as several models of small-volume ionization chambers to obtain an air-kerma rate at the treatment surface for each applicator. Correction factors were calculated using MCNP5 and EGSnrc Monte Carlo codes in order to determine an applicator-specific absorbed dose to water at the treatment surface from the measured air-kerma rate. Additionally, relative dose measurements of the surface dose distributions and characteristic depth dose curves were completed in-phantom. Results: Theoretical dose distributions and depth dose curves were generated for each applicator and agreed well with the measured values. A method of output verification was created that allows users to determine the applicator-specific dose to water at the treatment surface based on a measured air-kerma rate. Conclusions: The novel output verification methods described in this work will reduce uncertainties in dose delivery for treatments with these kinds of surface applicators, ultimately improving patient care.

  4. A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation

    SciTech Connect (OSTI)

    Zhang Yumin; Lum, Kai-Yew [Temasek Laboratories, National University of Singapore, Singapore 117508 (Singapore); Wang Qingguo [Depa. Electrical and Computer Engineering, National University of Singapore, Singapore 117576 (Singapore)

    2009-03-05T23:59:59.000Z

    In this paper, a H-infinity fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus, the classical nonlinear filter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive fault diagnosis algorithm is further investigated to estimate the fault. Simulation example is given to demonstrate the effectiveness of the proposed approaches.

  5. Fail safe controllable output improved version of the electromechanical battery

    DOE Patents [OSTI]

    Post, R.F.

    1999-01-19T23:59:59.000Z

    Mechanical means are provided to control the voltages induced in the windings of a generator/motor. In one embodiment, a lever is used to withdraw or insert the entire stator windings from the cavity where the rotating field exists. In another embodiment, voltage control and/or switching off of the output is achievable with a variable-coupling generator/motor. A stator is made up of two concentric layers of windings, with a larger number of turns on the inner layer of windings than the outer layer of windings. The windings are to be connected in series electrically, that is, their voltages add vectorially. The mechanical arrangement is such that one or both of the windings can be rotated with respect to the other winding about their common central axis. Another improved design for the stator assembly of electromechanical batteries provides knife switch contacts that are in electrical contact with the stator windings. The operation of this embodiment depends on the fact that an abnormally large torque will be exerted on the stator structure during any short-circuit condition. 4 figs.

  6. Updated Eastern Interconnect Wind Power Output and Forecasts for ERGIS: July 2012

    SciTech Connect (OSTI)

    Pennock, K.

    2012-10-01T23:59:59.000Z

    AWS Truepower, LLC (AWST) was retained by the National Renewable Energy Laboratory (NREL) to update wind resource, plant output, and wind power forecasts originally produced by the Eastern Wind Integration and Transmission Study (EWITS). The new data set was to incorporate AWST's updated 200-m wind speed map, additional tall towers that were not included in the original study, and new turbine power curves. Additionally, a primary objective of this new study was to employ new data synthesis techniques developed for the PJM Renewable Integration Study (PRIS) to eliminate diurnal discontinuities resulting from the assimilation of observations into mesoscale model runs. The updated data set covers the same geographic area, 10-minute time resolution, and 2004?2006 study period for the same onshore and offshore (Great Lakes and Atlantic coast) sites as the original EWITS data set.

  7. Landis/Byrd Polar Research Center The Ohio State University/2012

    E-Print Network [OSTI]

    Howat, Ian M.

    and Aquarium (Zoo) opened their new Polar Frontier exhibit on May 6, 2010 Ice Bear Outpost - the interpretive center for the Polar Frontier exhibit.) Graphics from Polar WRF Maps generated as outputs from Polar WRF extend

  8. Process and Intermediate Calculations User AccessInputs Outputs

    E-Print Network [OSTI]

    density, canopy base height, fuel moisture) · Weather · Fire History · Ignition History Analytic Models Behavior · DEM (Elevation, slope, aspect) · Vegetation (Fuel models, crown cover, stand height, bulk Smoke Analysis Management of Unplanned Ignitions: Each cell is evaluated using a probabilistic footprint

  9. INTRODUCTION The power output of insect flight muscles is proportional to muscle

    E-Print Network [OSTI]

    Nieh, James

    #12;2239 INTRODUCTION The power output of insect flight muscles is proportional to muscle polaris) to forage in suboptimal thermal conditions (Heinrich, 1993). Recently, bumble bee (Bombus

  10. Combining frequency and time domain approaches to systems with multiple spike train input and output

    E-Print Network [OSTI]

    Brillinger, D. R.; Lindsay, K. A.; Rosenberg, J. R.

    2009-01-01T23:59:59.000Z

    between neuronal spike trains. Prog Biophys Mol Biol Vapnikto systems with multiple spike train input and output D. R.Keywords Multiple spike trains ∑ Neural coding ∑ Maximum

  11. Abstract--The behavior of Solar Photo-Voltaic Generation (SPVG) in the grid is defined by the way its output active and

    E-Print Network [OSTI]

    Ca√Īizares, Claudio A.

    its output active and reactive power are controlled; the reactive power can be controlled directly). Therefore, two basic modeling approaches are considered in the present work: constant reactive power of electricity. The Ontario Power Authority (OPA), as per the Green Energy Act [1], considers renewable energy

  12. HSPICE and WaveView Tutorial Hspice is used for circuit simulation and WaveView is used to view output waveforms.

    E-Print Network [OSTI]

    Mahmoodi, Hamid

    NNano-E Hs Electron Sc San Fr S spice Q Mich Hamid nics & C chool of rancisco San Fra Spr Quick By be downloaded from the following website: http://ptm.asu.edu/ Click on the latest models and download 16nm PTM "hspice job aborted". In that case, please open the output file (inv.out) and search for error to see

  13. ARM: ARSCL: multiple outputs from first Clothiaux algorithms on Vaisala or Belfort ceilometers, Micropulse lidar, and MMCR

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Coulter, Richard; Widener, Kevin; Bharadwaj, Nitin; Johnson, Karen; Martin, Timothy

    ARSCL: multiple outputs from first Clothiaux algorithms on Vaisala or Belfort ceilometers, Micropulse lidar, and MMCR

  14. PWM Inverter Output Filter Cost to Losses Trade Off and Optimal Design

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    PWM Inverter Output Filter Cost to Losses Trade Off and Optimal Design Robert J. Pasterczyk Jean--This paper describes how to design the output filter of a PWM inverter used in a Uninterruptible Power SupplyVA 3-ph. PWM inverter is taken as example. B. Design Constraints Uninterruptible Power Supply (UPS

  15. Estimating Solar PV Output Using Modern Space/Time Geostatistics (Presentation)

    SciTech Connect (OSTI)

    Lee, S. J.; George, R.; Bush, B.

    2009-04-29T23:59:59.000Z

    This presentation describes a project that uses mapping techniques to predict solar output at subhourly resolution at any spatial point, develop a methodology that is applicable to natural resources in general, and demonstrate capability of geostatistical techniques to predict the output of a potential solar plant.

  16. Generating Isolated Outputs in a Multilevel Modular Capacitor Clamped DC-DC Converter

    E-Print Network [OSTI]

    Tolbert, Leon M.

    balance between the fuel cell and any energy storage inside the vehicle, and provides continuous power) for Hybrid Electric and Fuel Cell Vehicles Faisal H. Khan1 , Leon M. Tolbert2 1 Electric Power Research transformers to generate isolated ac outputs. These isolated outputs can be rectified and filtered to obtain

  17. Experimental Results on Multiple-Input Single-Output (MISO) Time Reversal for UWB

    E-Print Network [OSTI]

    Qiu, Robert Caiming

    Experimental Results on Multiple-Input Single-Output (MISO) Time Reversal for UWB Systems with multiple-input single- output (MISO) antennas over ultra-wideband (UWB) channels. In particular, temporal and spatial focusing as well as array gain are studied based on a (4 ◊ 1) MISO scheme in an office environment

  18. Optimizing the Output of a Human-Powered Energy Harvesting System with Miniaturization and Integrated Control

    E-Print Network [OSTI]

    Potkonjak, Miodrag

    1 Optimizing the Output of a Human-Powered Energy Harvesting System with Miniaturization mechanical energy from human foot-strikes and explore its configuration and control towards optimized energy output. Dielectric Elastomers (DEs) are high-energy density, soft, rubber-like material

  19. Fine-grained Photovoltaic Output Prediction using a Bayesian Ensemble Prithwish Chakraborty1,2

    E-Print Network [OSTI]

    Ramakrishnan, Naren

    generation is increasingly reliant on renewable power sources, e.g., solar (pho- tovoltaic or PV) and wind Increasingly, local and distributed power generation e.g., through solar (photovoltaic or PV), wind, fuel cells and intermittent in their energy output, which makes integration with the power grid challenging. PV output

  20. High-Efficiency Multiple-Output DC-DC Conversion for Low-Voltage Systems

    E-Print Network [OSTI]

    Abram P. Dancy; Rajeevan Amirtharajah; Anantha P. Chandrakasan

    2000-01-01T23:59:59.000Z

    This versatile power converter controller provides dual outputs at a fixed switching frequency and can regulate either output voltage or target system delay (using an external -- filter). In the voltage regulation mode, the output voltage is monitored with an analog--digital (A/D) converter, and the feedback compensation network is implemented digitally. The generation of the pulsewidth modulation (PWM) signal is done with a hybrid delay line/counter approach, which saves power and area relative to previous implementations. Power devices are included on chip to create the two independently regulated output PWM signals. The key features of this design are its low-power dissipation, reconfigurability, use of either delay or voltage feedback, and multiple outputs.

  1. Method for leveling the power output of an electromechanical battery as a function of speed

    DOE Patents [OSTI]

    Post, R.F.

    1999-03-16T23:59:59.000Z

    The invention is a method of leveling the power output of an electromechanical battery during its discharge, while at the same time maximizing its power output into a given load. The method employs the concept of series resonance, employing a capacitor the parameters of which are chosen optimally to achieve the desired near-flatness of power output over any chosen charged-discharged speed ratio. Capacitors are inserted in series with each phase of the windings to introduce capacitative reactances that act to compensate the inductive reactance of these windings. This compensating effect both increases the power that can be drawn from the generator before inductive voltage drops in the windings become dominant and acts to flatten the power output over a chosen speed range. The values of the capacitors are chosen so as to optimally flatten the output of the generator over the chosen speed range. 3 figs.

  2. Finding the quantum thermoelectric with maximal efficiency and minimal entropy production at given power output

    E-Print Network [OSTI]

    Robert S. Whitney

    2015-01-28T23:59:59.000Z

    We investigate the nonlinear scattering theory for quantum systems with strong Seebeck and Peltier effects, and consider their use as heat-engines and refrigerators with finite power outputs. This article gives detailed derivations of the results summarized in Phys. Rev. Lett. 112, 130601 (2014). It shows how to use the scattering theory to find (i) the quantum thermoelectric with maximum possible power output, and (ii) the quantum thermoelectric with maximum efficiency at given power output. The latter corresponds to a minimal entropy production at that power output. These quantities are of quantum origin since they depend on system size over electronic wavelength, and so have no analogue in classical thermodynamics. The maximal efficiency coincides with Carnot efficiency at zero power output, but decreases with increasing power output. This gives a fundamental lower bound on entropy production, which means that reversibility (in the thermodynamic sense) is impossible for finite power output. The suppression of efficiency by (nonlinear) phonon and photon effects is addressed in detail; when these effects are strong, maximum efficiency coincides with maximum power. Finally, we show in particular limits (typically without magnetic fields) that relaxation within the quantum system does not allow the system to exceed the bounds derived for relaxation-free systems, however a general proof of this remains elusive.

  3. Finding the quantum thermoelectric with maximal efficiency and minimal entropy production at given power output

    E-Print Network [OSTI]

    Robert S. Whitney

    2015-03-16T23:59:59.000Z

    We investigate the nonlinear scattering theory for quantum systems with strong Seebeck and Peltier effects, and consider their use as heat-engines and refrigerators with finite power outputs. This article gives detailed derivations of the results summarized in Phys. Rev. Lett. 112, 130601 (2014). It shows how to use the scattering theory to find (i) the quantum thermoelectric with maximum possible power output, and (ii) the quantum thermoelectric with maximum efficiency at given power output. The latter corresponds to a minimal entropy production at that power output. These quantities are of quantum origin since they depend on system size over electronic wavelength, and so have no analogue in classical thermodynamics. The maximal efficiency coincides with Carnot efficiency at zero power output, but decreases with increasing power output. This gives a fundamental lower bound on entropy production, which means that reversibility (in the thermodynamic sense) is impossible for finite power output. The suppression of efficiency by (nonlinear) phonon and photon effects is addressed in detail; when these effects are strong, maximum efficiency coincides with maximum power. Finally, we show in particular limits (typically without magnetic fields) that relaxation within the quantum system does not allow the system to exceed the bounds derived for relaxation-free systems, however, a general proof of this remains elusive.

  4. Numerical simulations of output pulse extraction from a high-power microwave compressor with a plasma switch

    SciTech Connect (OSTI)

    Shlapakovski, Anatoli; Beilin, Leonid; Bliokh, Yuri; Donskoy, Moshe; Krasik, Yakov E. [Physics Department, Technion, Haifa 32000 (Israel); Hadas, Yoav [Department of Applied Physics, Rafael, PO Box 2250, Haifa 31021 (Israel); Schamiloglu, Edl [Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico 87131 (United States)

    2014-05-07T23:59:59.000Z

    Numerical simulations of the process of electromagnetic energy release from a high-power microwave pulse compressor comprising a gas-filled cavity and interference switch were carried out. A microwave plasma discharge in a rectangular waveguide H-plane tee was modeled with the use of the fully electromagnetic particle-in-cell code MAGIC. The gas ionization, plasma evolution, and interaction with RF fields accumulated within the compressor were simulated using different approaches provided by the MAGIC code: particle-in-cell approach accounting for electron-neutral collisions, gas conductivity model based on the concept of mobility, and hybrid modeling. The dependences of the microwave output pulse peak power and waveform on parameters that can be controlled in experiments, such as an external ionization rate, RF field amplitude, and background gas pressure, were investigated.

  5. X-ray source assembly having enhanced output stability, and fluid stream analysis applications thereof

    DOE Patents [OSTI]

    Radley, Ian (Glenmont, NY); Bievenue, Thomas J. (Delmar, NY); Burdett, John H. (Charlton, NY); Gallagher, Brian W. (Guilderland, NY); Shakshober, Stuart M. (Hudson, NY); Chen, Zewu (Schenectady, NY); Moore, Michael D. (Alplaus, NY)

    2008-06-08T23:59:59.000Z

    An x-ray source assembly and method of operation are provided having enhanced output stability. The assembly includes an anode having a source spot upon which electrons impinge and a control system for controlling position of the anode source spot relative to an output structure. The control system can maintain the anode source spot location relative to the output structure notwithstanding a change in one or more operating conditions of the x-ray source assembly. One aspect of the disclosed invention is most amenable to the analysis of sulfur in petroleum-based fuels.

  6. Output dominance as a predictor of humor content in verbal productions

    E-Print Network [OSTI]

    Hull, Rachel Gayle

    2000-01-01T23:59:59.000Z

    -dominance-ordered feature lists generated for each of the concepts. It was hypothesized that juxtapositions judged funny would rely more often on properties with significantly different output dominance scores per concept, while those judged not funny would involve fewer...

  7. Examining the Variability of Wind Power Output in the Regulation Time Frame: Preprint

    SciTech Connect (OSTI)

    Hodge, B. M.; Shedd, S.; Florita, A.

    2012-08-01T23:59:59.000Z

    This work examines the distribution of changes in wind power for different time scales in the regulation time frame as well as the correlation of changes in power output for individual wind turbines in a wind plant.

  8. Augmentation of Power Output of Axisymmetric Ducted Wind Turbines by Porous Trailing Edge Disks

    E-Print Network [OSTI]

    widnall, sheila

    2014-06-30T23:59:59.000Z

    This paper presents analytical and experimental results that demonstrated that the power output from a ducted wind turbine can be dramatically increased by the addition of a trailing edge device such as a porous disk. In ...

  9. A Hardware Implementation of the Soft Output Viterbi Algorithm for Serially Concatenated Convolutional Codes

    E-Print Network [OSTI]

    Werling, Brett William

    2010-06-28T23:59:59.000Z

    This thesis outlines the hardware design of a soft output Viterbi algorithm decoder for use in a serially concatenated convolutional code system. Convolutional codes and their related structures are described, as well as the algorithms used...

  10. Primate Motor Cortex: Individual and Ensemble Neuron-Muscle Output Relationships

    E-Print Network [OSTI]

    Griffin, Darcy Michelle

    2008-07-30T23:59:59.000Z

    The specific aims of this study were to: 1) investigate the encoding of forelimb muscle activity timing and magnitude by corticomotoneuronal (CM) cells, 2) test the stability of primary motor cortex (M1) output to forelimb ...

  11. Multilevel Cascade H-bridge Inverter DC Voltage Estimation Through Output Voltage Sensing

    E-Print Network [OSTI]

    Tolbert, Leon M.

    system as the inverter power supply may vary. For example, interface of solar panels or fuel cell. The output voltage is then processed by a DSP unit that uses the signals that command the switches

  12. Input-Output as a Method of Evaluahon of the Economic Impact of Water Resources Development

    E-Print Network [OSTI]

    Canion, R. L.; Trock, W. L.

    In this report the results of a study of the use of input-output analysis to evaluate the economic impact of water resources development are presented. Blackburn Crossing reservoir on the Upper Neches river was the subject development...

  13. Code design for multiple-input multiple-output broadcast channels

    E-Print Network [OSTI]

    Uppal, Momin Ayub

    2009-06-02T23:59:59.000Z

    Recent information theoretical results indicate that dirty-paper coding (DPC) achieves the entire capacity region of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC). This thesis presents practical code designs for Gaussian...

  14. Water Power Calculator Temperature and Analog Input/Output Module Ambient Temperature Testing

    SciTech Connect (OSTI)

    Mark D. McKay

    2011-02-01T23:59:59.000Z

    Water Power Calculator Temperature and Analog input/output Module Ambient Temperature Testing A series of three ambient temperature tests were conducted for the Water Power Calculator development using the INL Calibration Laboratoryís Tenney Environmental Chamber. The ambient temperature test results demonstrate that the Moore Industries Temperature Input Modules, Analog Input Module and Analog Output Module, ambient temperature response meet or exceed the manufactures specifications

  15. Exploring the circadian outputs and function of HPT-1 in Neurospora crassa

    E-Print Network [OSTI]

    Vickery, Justin Wayde

    2013-09-28T23:59:59.000Z

    EXPLORING THE CIRCADIAN OUTPUTS AND FUNCTIONS OF HTP-1 IN NEUROSPORA CRASSA An Undergraduate Research Scholars Thesis by JUSTIN WAYDE VICKERY Submitted to Honors and Undergraduate Research Texas A&M University in partial fulfillment... ......................................................................................................................... 25 1 ABSTRACT Exploring the circadian outputs and functions of HPT-1 in N. crassa. (May 2014) Justin Wayde Vickery Department of Biology Texas A&M University Research Advisor: Dr. Deborah Bell-Pedersen Department of Biology...

  16. Output Harmonic Termination Techniques for AlGaN/GaN HEMT Power Amplifiers Using Active Integrated Antenna Approach

    E-Print Network [OSTI]

    Itoh, Tatsuo

    Output Harmonic Termination Techniques for AlGaN/GaN HEMT Power Amplifiers Using Active Integrated 1200, Los Angeles, CA 90045 Abstract -- In this paper, effects of output harmonic terminations on PAE termination, we observe a substantial increase in PAE and output power. Further, we demonstrate the high

  17. Distributed quantitative precipitation forecasts combining information from radar and numerical weather prediction model outputs

    E-Print Network [OSTI]

    Ganguly, Auroop Ratan

    2002-01-01T23:59:59.000Z

    Applications of distributed Quantitative Precipitation Forecasts (QPF) range from flood forecasting to transportation. Obtaining QPF is acknowledged to be one of the most challenging areas in hydrology and meteorology. ...

  18. Scaling analyses of forcings and outputs of a simplified Last1 Millennium climate model2

    E-Print Network [OSTI]

    Lovejoy, Shaun

    its responses with those of multiproxies and the NASA GISS-ER2 GCM. 22 became too weak at 27 longer scales. At small scales, the GISS ER2

  19. Dissemination of Climate Model Output to the Public and Commercial Sector

    SciTech Connect (OSTI)

    Robert Stockwell, PhD

    2010-09-23T23:59:59.000Z

    Climate is defined by the Glossary of Meteorology as the mean of atmospheric variables over a period of time ranging from as short as a few months to multiple years and longer. Although the term climate is often used to refer to long-term weather statistics, the broader definition of climate is the time evolution of a system consisting of the atmosphere, hydrosphere, lithosphere, and biosphere. Physical, chemical, and biological processes are involved in interactions among the components of the climate system. Vegetation, soil moisture, and glaciers are part of the climate system in addition to the usually considered temperature and precipitation (Pielke, 2008). Climate change refers to any systematic change in the long-term statistics of climate elements (such as temperature, pressure, or winds) sustained over several decades or longer. Climate change can be initiated by external forces, such as cyclical variations in the Earth's solar orbit that are thought to have caused glacial and interglacial periods within the last 2 million years (Milankovitch, 1941). However, a linear response to astronomical forcing does not explain many other observed glacial and interglacial cycles (Petit et al., 1999). It is now understood that climate is influenced by the interaction of solar radiation with atmospheric greenhouse gasses (e.g., carbon dioxide, chlorofluorocarbons, methane, nitrous oxide, etc.), aerosols (airborne particles), and Earth's surface. A significant aspect of climate are the interannual cycles, such as the El Nino La Nina cycle which profoundly affects the weather in North America but is outside the scope of weather forecasts. Some of the most significant advances in understanding climate change have evolved from the recognition of the influence of ocean circulations upon the atmosphere (IPCC, 2007). Human activity can affect the climate system through increasing concentrations of atmospheric greenhouse gases, air pollution, increasing concentrations of aerosol, and land alteration. A particular concern is that atmospheric levels of CO{sub 2} may be rising faster than at any time in Earth's history, except possibly following rare events like impacts from large extraterrestrial objects (AMS, 2007). Atmospheric CO{sub 2} concentrations have increased since the mid-1700s through fossil fuel burning and changes in land use, with more than 80% of this increase occurring since 1900. The increased levels of CO{sub 2} will remain in the atmosphere for hundreds to thousands of years. The complexity of the climate system makes it difficult to predict specific aspects of human-induced climate change, such as exactly how and where changes will occur, and their magnitude. The Intergovernmental Panel for Climate Change (IPCC) was established by World Meteorological Organization (WMO) and the United Nations in 1988. The IPCC was tasked with assessing the scientific, technical and socioeconomic information needed to understand the risk of human-induced climate change, its observed and projected impacts, and options for adaptation and mitigation. The IPCC concluded in its Fourth Assessment Report (AR4) that warming of the climate system is unequivocal, and that most of the observed increase in globally averaged temperatures since the mid-20th century is very likely due to the observed increased in anthropogenic greenhouse gas concentrations (IPCC, 2007).

  20. A Comparison of Simulated Cloud Radar Output from the Multiscale Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary)morphinanInformation Desert SouthwestTechnologies |November 2011 Mon,Electrocatalysis |Framework Global Climate

  1. Dosimetric characterization and output verification for conical brachytherapy surface applicators. Part II. High dose rate {sup 192}Ir sources

    SciTech Connect (OSTI)

    Fulkerson, Regina K., E-mail: rmkenned@gmail.com; Micka, John A.; DeWerd, Larry A. [Department of Medical Physics, University of WisconsinĖMadison, Madison, Wisconsin 53705 (United States)] [Department of Medical Physics, University of WisconsinĖMadison, Madison, Wisconsin 53705 (United States)

    2014-02-15T23:59:59.000Z

    Purpose: Historically, treatment of malignant surface lesions has been achieved with linear accelerator based electron beams or superficial x-ray beams. Recent developments in the field of brachytherapy now allow for the treatment of surface lesions with specialized conical applicators placed directly on the lesion. Applicators are available for use with high dose rate (HDR){sup 192}Ir sources, as well as electronic brachytherapy sources. Part I of this paper discussed the applicators used with electronic brachytherapy sources. Part II will discuss those used with HDR {sup 192}Ir sources. Although the use of these applicators has gained in popularity, the dosimetric characteristics have not been independently verified. Additionally, there is no recognized method of output verification for quality assurance procedures with applicators like these. Methods: This work aims to create a cohesive method of output verification that can be used to determine the dose at the treatment surface as part of a quality assurance/commissioning process for surface applicators used with HDR electronic brachytherapy sources (Part I) and{sup 192}Ir sources (Part II). Air-kerma rate measurements for the {sup 192}Ir sources were completed with several models of small-volume ionization chambers to obtain an air-kerma rate at the treatment surface for each applicator. Correction factors were calculated using MCNP5 and EGSnrc Monte Carlo codes in order to determine an applicator-specific absorbed dose to water at the treatment surface from the measured air-kerma rate. Additionally, relative dose measurements of the surface dose distributions and characteristic depth dose curves were completed in-phantom. Results: Theoretical dose distributions and depth dose curves were generated for each applicator and agreed well with the measured values. A method of output verification was created that allows users to determine the applicator-specific dose to water at the treatment surface based on a measured air-kerma rate. Conclusions: The novel output verification methods described in this work will reduce uncertainties in dose delivery for treatments with these kinds of surface applicators, ultimately improving patient care.

  2. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    3. Summary Statistics for Coal Refining Plants, 2012 - 2014" "(thousand short tons)" "Year and","Coal Receipts","Average Price of Coal Receipts","Coal Used","Coal Stocks1"...

  3. SAS Output

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    4. Nitrogen Oxides Control Technology Emissions Reduction Factors Nitrogen Oxides Control Technology EIA-Code(s) Reduction Factor Advanced Overfire Air AA 30% Alternate Burners BF...

  4. SAS Output

    Gasoline and Diesel Fuel Update (EIA)

    Boiler Spreader Stoker Boiler Tangential Boiler All Other Boiler Types Combustion Turbine Internal Combustion Engine Agricultural Byproducts AB Source: 1 Lbs per ton 0.08 0.01...

  5. SAS Output

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    System Type Firing Configuration Tangential Boiler All Other Boiler Types Combustion Turbine Internal Combustion Engine Fuel EIA Fuel Code Source and Tables (As Appropriate)...

  6. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    "(thousand short tons)" "Census Division","June 30 2014","March 31 2014","June 30 2013","Percent Change" "and State",,,,"(June 30)" ,,,,"2014 versus 2013" "Middle...

  7. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    to Date" "Census Division","April - June","January - March","April - June",2014,2013,"Percent" "and State1",2014,2014,2013,,,"Change" "Middle Atlantic" "...

  8. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    to Date" "Census Division","April - June","January - March","April - June",2014,2013,"Percent" ,2014,2014,2013,,,"Change" "Middle Atlantic",1222,1214,1247,2435,2460,-1...

  9. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    ,,,,"Year to Date" "Commodity","April - June","January - March","April - June",2014,2013,"Percent" ,2014,2014,2013,,,"Change" "Coke" " Sales",1969,1865,1969,3834,3905,-1.8 "...

  10. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    to Date" "Census Division","April - June","January - March","April - June",2014,2013,"Percent" ,2014,2014,2013,,,"Change" "Middle Atlantic",1599,1503,1622,3102,3178,-2.4...

  11. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    to Date" "Census Division","April - June","January - March","April - June",2014,2013,"Percent" "and State",2014,2014,2013,,,"Change" "Middle Atlantic",113.65,114.55,139.64,...

  12. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    ,,,,"Year to Date" "NAICS Code","April - June","January - March","April - June",2014,2013,"Percent" ,2014,2014,2013,,,"Change" "311 Food Manufacturing",2085,2575,2256,4660,4817,...

  13. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    Code" "(thousand short tons)" "NAICS Code","June 30 2014","March 31 2014","June 30 2013","Percent Change" ,,,,"(June 30)" ,,,,"2014 versus 2013" "311 Food...

  14. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    to Date" "Census Division","April - June","January - March","April - June",2014,2013,"Percent" "and State",2014,2014,2013,,,"Change" "New England",20,30,21,51,48,5.5 "...

  15. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    to Date" "Census Division","April - June","January - March","April - June",2014,2013,"Percent" "and State",2014,2014,2013,,,"Change" "Middle Atlantic",19,58,25,77,79,-2.7 "...

  16. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    to Date" "Census Division","April - June","January - March","April - June",2014,2013,"Percent" "and State",2014,2014,2013,,,"Change" "New England","w","w","w","w","w","w" "...

  17. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    "(thousand short tons)" "Census Division","June 30 2014","March 31 2014","June 30 2013","Percent Change" "and State",,,,"(June 30)" ,,,,"2014 versus 2013" "New...

  18. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    to Date" "Census Division","April - June","January - March","April - June",2014,2013,"Percent" "and State1",2014,2014,2013,,,"Change" "New England" " Btu",13306,12964,13323...

  19. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    ,"Sector1",,,"Institutional Users",,"Distributors" 2008 " March 31",146497,1462,4818,448,153225,34876,188101 " June 30",152542,1756,4983,478,159760,32086,191846 "...

  20. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    ,,,,"Year to Date" "NAICS Code","April - June","January - March","April - June",2014,2013,"Percent" ,2014,2014,2013,,,"Change" "311 Food Manufacturing",2111,2386,2214,4497,4570,...

  1. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    to Date" "Census Division","April - June","January - March","April - June",2014,2013,"Percent" "and State",2014,2014,2013,,,"Change" "Middle Atlantic",21,59,20,80,73,10.4 "...

  2. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    to Date" "Census Division","April - June","January - March","April - June",2014,2013,"Percent" "and State",2014,2014,2013,,,"Change" "New England",21,29,22,50,48,3.1 "...

  3. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) Working GroupB..3.3. Revenue

  4. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) Working GroupB..3.3. Revenue4.

  5. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) Working GroupB..3.3.

  6. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) Working GroupB..3.3.A.

  7. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) Working GroupB..3.3.A.B.

  8. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) Working GroupB..3.3.A.B.A. Net

  9. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) Working GroupB..3.3.A.B.A.

  10. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) Working GroupB..3.3.A.B.A.A.

  11. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) Working GroupB..3.3.A.B.A.A.B.

  12. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) Working

  13. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) WorkingB. Winter Net Internal

  14. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) WorkingB. Winter Net

  15. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) WorkingB. Winter NetB.

  16. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 ResourceAwards SAGE Awards A(SAPC) WorkingB. Winter NetB.4.5.

  17. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline Blend.1.

  18. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline Blend.1.2.

  19. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline

  20. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline. Number of

  1. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline. Number

  2. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline. Number3.

  3. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline. Number3.5.

  4. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline.

  5. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline.7. Average

  6. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline.7.

  7. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline.7.9.

  8. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline.7.9.0.

  9. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline.7.9.0.1.

  10. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional Gasoline.7.9.0.1.2.

  11. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional

  12. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. Green Pricing

  13. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. Green PricingA.

  14. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. Green PricingA.B.

  15. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. Green

  16. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB. Net

  17. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB. Net3.A.

  18. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB. Net3.A.B.

  19. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB.

  20. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB.B. Net

  1. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB.B. NetA.

  2. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB.B. NetA.B.

  3. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB.B.

  4. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB.B.7. Net

  5. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB.B.7. Net8.

  6. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB.B.7.

  7. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB.B.7.0. Net

  8. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB.B.7.0.

  9. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB.B.7.0.2.

  10. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4. GreenB.B.7.0.2.3.

  11. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.

  12. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net Generation

  13. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net Generation6.

  14. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net

  15. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net8. Net

  16. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net8. Net9. Net

  17. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net8. Net9.

  18. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net8. Net9.1.

  19. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net8. Net9.1.2.

  20. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net8.

  1. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net8.4. Useful

  2. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net8.4. Useful.

  3. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net8.4.

  4. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net8.4.B.

  5. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net8.4.B.3.

  6. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net8.4.B.3.4.

  7. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5. Net8.4.B.3.4.5.

  8. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.

  9. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A. Net Summer

  10. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A. Net SummerB.

  11. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A. Net SummerB.C.

  12. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A. Net

  13. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A. Net0. Net

  14. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A. Net0. Net1.

  15. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A. Net0. Net1.2.

  16. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A. Net0.

  17. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A. Net0.4.

  18. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A. Net0.4.A.

  19. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A. Net0.4.A.B.

  20. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A. Net0.4.A.B.C.

  1. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A.

  2. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A.E. Coal:

  3. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A.E. Coal:F.

  4. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A.E. Coal:F.A.

  5. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A.E. Coal:F.A.B.

  6. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A.E.

  7. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A.E.D. Petroleum

  8. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A.E.D.

  9. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A.E.D.F.

  10. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A.E.D.F.A.

  11. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A.E.D.F.A.B.

  12. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A.E.D.F.A.B.C.

  13. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on CokersA2.Conventional4.5.A.E.D.F.A.B.C.D.

  14. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity on

  15. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: Consumption for

  16. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: Consumption forA.

  17. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: Consumption forA.B.

  18. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: Consumption forA.B.C.

  19. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: Consumption forA.B.C.D.

  20. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: Consumption

  1. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: ConsumptionF. Natural

  2. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: ConsumptionF. NaturalD.

  3. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: ConsumptionF.

  4. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: ConsumptionF.F. Wood /

  5. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: ConsumptionF.F. Wood /A.

  6. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: ConsumptionF.F. Wood

  7. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: ConsumptionF.F. WoodC.

  8. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: ConsumptionF.F. WoodC.D.

  9. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: ConsumptionF.F.

  10. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: ConsumptionF.F.F.

  11. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: ConsumptionF.F.F.A.

  12. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: ConsumptionF.F.F.A.B.

  13. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke: ConsumptionF.F.F.A.B.C.

  14. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:

  15. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. Biogenic Municipal

  16. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. Biogenic MunicipalF.

  17. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. Biogenic MunicipalF.D.

  18. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. Biogenic

  19. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF. Other Waste

  20. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF. Other

  1. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF. Other0.

  2. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF. Other0.1.

  3. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF. Other0.1.2.

  4. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF.

  5. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF.4.

  6. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF.4.1. Stocks

  7. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF.4.1. Stocks2

  8. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF.4.1.

  9. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF.4.1.4.

  10. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF.4.1.4..

  11. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF.4.1.4..3.

  12. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E. BiogenicF.4.1.4..3.4.

  13. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.

  14. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6. Receipts, Average

  15. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6. Receipts, Average7

  16. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6. Receipts, Average78.

  17. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6. Receipts,

  18. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6. Receipts,0.

  19. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6. Receipts,0.1.

  20. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6. Receipts,0.1.2.

  1. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6. Receipts,0.1.2.3.

  2. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6. Receipts,0.1.2.3.4.

  3. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.

  4. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6. Receipts of

  5. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6. Receipts of7.

  6. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6. Receipts of7.8.

  7. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6. Receipts of7.8.9.

  8. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6. Receipts

  9. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6. Receipts1.

  10. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6. Receipts1.2.

  11. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6. Receipts1.2.3.

  12. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6. Receipts1.2.3.4.

  13. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6. Receipts1.2.3.4.5.

  14. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.

  15. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.2. Average Tested

  16. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.2. Average Tested3.

  17. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.2. Average

  18. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.2. Average.

  19. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.2. Average.2.

  20. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.2. Average.2.3.

  1. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.2. Average.2.3.4.

  2. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.2. Average.2.3.4.5.

  3. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.2.

  4. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.2.7. Energy

  5. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.2.7. Energy8.

  6. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.2.7. Energy8.9.

  7. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia,(Million Barrels) Crude Oil Reserves in NonproducingAdditions to Capacity onF. Petroleum Coke:E.6.6.2.7. Energy8.9.A.5.

  8. SAS Output

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    Bituminous Coal BIT Source: 1 205.30000 Distillate Fuel Oil DFO Source: 1 161.38600 Geothermal GEO Estimate from EIA, Office of Integrated Analysis and Forecasting 16.59983 Jet...

  9. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    1. U.S. Coal Summary Statistics, 2008 - 2014" "(thousand short tons)" "Year and","Production1","Imports","Waste Coal","Producer and","Consumption","Exports","Consumer","Losses and"...

  10. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsrucLas ConchasPassive Solar HomePromisingStoriesSANDIA REPORT SAND 2011-39584. Average Retail Price

  11. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsrucLas ConchasPassive Solar HomePromisingStoriesSANDIA REPORT SAND 2011-39584. Average Retail

  12. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsrucLas ConchasPassive Solar HomePromisingStoriesSANDIA REPORT SAND 2011-39584. Average Retail1.

  13. SAS Output

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsrucLas ConchasPassive Solar HomePromisingStoriesSANDIA REPORT SAND 2011-39584. Average Retail1.2.

  14. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)Percent of U.S.Percent of U.S.Coal

  15. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)Percent of U.S.Percent of

  16. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)Percent of U.S.Percent ofProductive

  17. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)Percent of U.S.Percent

  18. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)Percent of U.S.PercentProductive

  19. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)Percent of

  20. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)Percent ofRecoverable Coal Reserves

  1. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)Percent ofRecoverable Coal

  2. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)Percent ofRecoverable

  3. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)Percent ofRecoverableAverage Number

  4. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)Percent ofRecoverableAverage

  5. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)Percent ofRecoverableAverageand

  6. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)Percent

  7. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal Productivity by State

  8. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal Productivity by State2.

  9. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal Productivity by

  10. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal Productivity by4. Coal

  11. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal Productivity by4.

  12. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal Productivity by4.6.

  13. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal Productivity by4.6.7.

  14. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal Productivity by4.6.7.8.

  15. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal Productivity

  16. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal ProductivityUnderground

  17. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal

  18. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal1. Average Sales Price

  19. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal1. Average Sales Price2.

  20. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal1. Average Sales

  1. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal1. Average Sales4.

  2. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal1. Average Sales4.Coal

  3. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal1. Average

  4. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal1. AverageCoal

  5. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal1. AverageCoalCoal

  6. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal1. AverageCoalCoalCoal

  7. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal1.

  8. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal1.Report No.: DOE/EIA

  9. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ <Information Administration (EIA) 10 MECS Survey Data 2010Feet)PercentCoal1.Report No.: DOE/EIA0.

  10. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreases The448

  11. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreases The448U.S.

  12. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreases The448U.S.Average

  13. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreases

  14. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreasesSteam Coal Exports by

  15. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreasesSteam Coal Exports

  16. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreasesSteam Coal

  17. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreasesSteam CoalAverage

  18. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreasesSteam CoalAverageU.S.

  19. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreasesSteam

  20. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreasesSteamCoal Production,

  1. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreasesSteamCoal

  2. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreasesSteamCoalU.S. Coke

  3. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreasesSteamCoalU.S.

  4. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreasesSteamCoalU.S.by

  5. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price decreasesSteamCoalU.S.byU.S.

  6. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price

  7. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price Quantity and Average Price of

  8. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price Quantity and Average Price

  9. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price Quantity and Average

  10. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data CenterFranconia, Virginia:FAQ < RAPID Jump to:SeadovCooperativeA2. World9, 2014 Residential propane price Quantity and Average U.S.

  11. SAS Output

    Gasoline and Diesel Fuel Update (EIA)

    B. U.S. Transformer Sustained Automatic Outage Counts and Hours by High-Voltage Size and NERC Region, 2012 Sustained Automatic Outage Counts High-Side Voltage (kV) Eastern...

  12. SAS Output

    Gasoline and Diesel Fuel Update (EIA)

    B. U.S. Transformer Outages by Type and NERC region, 2012 Outage Type Eastern Interconnection TRE WECC Contiguous U.S. Circuit Outage Counts Automatic Outages (Sustained) 16.00 --...

  13. SAS Output

    Gasoline and Diesel Fuel Update (EIA)

    B. U.S. Transformer Sustained Automatic Outage Counts and Hours by Cause Code and by NERC Region, 2012 Transformer Outage Counts Sustained Outage Causes FRCC MRO NPCC RFC SERC SPP...

  14. SAS Output

    U.S. Energy Information Administration (EIA) Indexed Site

    ,109.81,115.95,110.07,117.4,-6.2 "315 Apparel Manufacturing","w","w","w","w","w","w" "321 Wood Product Manufacturing","w","-","w","w","w","w" "322 Paper Manufacturing",87.55,88.68,...

  15. A combined compensation method for the output voltage of an insulated core transformer power supply

    SciTech Connect (OSTI)

    Yang, L.; Yang, J., E-mail: jyang@mail.hust.edu.cn; Liu, K. F.; Qin, B.; Chen, D. Z. [State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China)

    2014-06-15T23:59:59.000Z

    An insulated core transformer (ICT) power supply is an ideal high-voltage generator for irradiation accelerators with energy lower than 3 MeV. However, there is a significant problem that the structure of the segmented cores leads to an increase in the leakage flux and voltage differences between rectifier disks. A high level of consistency in the output of the disks helps to achieve a compact structure by improving the utilization of both the rectifier components and the insulation distances, and consequently increase the output voltage of the power supply. The output voltages of the disks which are far away from the primary coils need to be improved to reduce their inhomogeneity. In this study, by investigating and comparing the existing compensation methods, a new combined compensation method is proposed, which increases the turns on the secondary coils and employs parallel capacitors to improve the consistency of the disks, while covering the entire operating range of the power supply. This method turns out to be both feasible and effective during the development of an ICT power supply. The non-uniformity of the output voltages of the disks is less than 3.5% from no-load to full-load, and the power supply reaches an output specification of 350 kV/60 mA.

  16. System and method for cancelling the effects of stray magnetic fields from the output of a variable reluctance sensor

    DOE Patents [OSTI]

    Chen, Chingchi (Ann Arbor, MI); Degner, Michael W. (Farmington Hills, MI)

    2002-11-19T23:59:59.000Z

    A sensor system for sensing a rotation of a sensing wheel is disclosed. The sensor system has a sensing coil in juxtaposition with the sensing wheel. Moreover, the sensing coil has a sensing coil output signal indicative of the rotational speed of the sensing wheel. Further, a cancellation coil is located remotely from the sensing coil and connected in series therewith. Additionally, the cancellation coil has a cancellation coil output signal indicative of an environmental disturbance which is effecting the sensing coil output signal. The cancellation coil output signal operates to cancel the effects of the environmental disturbance on the sensing coil output signal.

  17. Note: Synchronous energy extraction through four output ports of microwave compressor

    SciTech Connect (OSTI)

    Avgustinovich, V. A.; Artemenko, S. N.; Novikov, S. A.; Yushkov, Yu. G. [Tomsk Polytechnic University, 2-a Lenina, Tomsk 634050 (Russian Federation)

    2013-06-15T23:59:59.000Z

    The energy stored in a resonant cavity was extracted through four output ports and added in phase in a common line. Operation of a single switch provided synchronism and the power portions transmitted through the ports were combined in a waveguide turnstile junction. Estimation shows that the compressor peak power can reach a value eight times as much as the switched wave power, provided the output pulsewidth is shortened by the same factor with reference to the cavity double transit time. The performance of the X-band compressor prototype was investigated. Signals radiated through each of four output ports had identical envelope shapes and equal peak power values. The reflected wave did not accompany the power combining. The pulses of 1.2 MW peak power and 1.6 ns pulse width were obtained when the compressor was driven by the 50 kW pulse power magnetron generator.

  18. Handling Ambiguity via Input-Output Kernel Learning Xinxing Xu Ivor W. Tsang Dong Xu

    E-Print Network [OSTI]

    Tsang Wai Hung "Ivor"

    of Computer Engineering, Nanyang Technological University, Singapore xuxi0006@ntu.edu.sg IvorTsang@ntu.edu.sg dongxu@ntu.edu.sg Abstract--Data ambiguities exist in many data mining and machine learning applications the effectiveness of our proposed IOKL framework. Keywords-Group Multiple Kernel Learning; Input-Output Kernel

  19. SOLAR ENERGY (conditionally accepted 1/2010) QUANTIFYING PV POWER OUTPUT VARIABILITY

    E-Print Network [OSTI]

    Perez, Richard R.

    SOLAR ENERGY (conditionally accepted 1/2010) QUANTIFYING PV POWER OUTPUT VARIABILITY Thomas E create major problems that will require major mitigation efforts. #12;SOLAR ENERGY (conditionally industry believe it could constrain the penetration of gridconnected PV. The U.S. Department of Energy

  20. Improved thermoelectric power output from multilayered polyethylenimine doped carbon nanotube based organic composites

    SciTech Connect (OSTI)

    Hewitt, Corey A.; Montgomery, David S.; Barbalace, Ryan L.; Carlson, Rowland D.; Carroll, David L., E-mail: carroldl@wfu.edu [Center for Nanotechnology and Molecular Materials, Wake Forest University, 501 Deacon Blvd., Winston Salem, North Carolina 27105 (United States)

    2014-05-14T23:59:59.000Z

    By appropriately selecting the carbon nanotube type and n-type dopant for the conduction layers in a multilayered carbon nanotube composite, the total device thermoelectric power output can be increased significantly. The particular materials chosen in this study were raw single walled carbon nanotubes for the p-type layers and polyethylenimine doped single walled carbon nanotubes for the n-type layers. The combination of these two conduction layers leads to a single thermocouple Seebeck coefficient of 96 Ī 4??VK{sup ?1}, which is 6.3 times higher than that previously reported. This improved Seebeck coefficient leads to a total power output of 14.7 nW per thermocouple at the maximum temperature difference of 50?K, which is 44 times the power output per thermocouple for the previously reported results. Ultimately, these thermoelectric power output improvements help to increase the potential use of these lightweight, flexible, and durable organic multilayered carbon nanotube based thermoelectric modules in low powered electronics applications, where waste heat is available.

  1. The effects of energy policies in China on GDP, industrial output and new energy profits1

    E-Print Network [OSTI]

    Lin, C.-Y. Cynthia

    1 The effects of energy policies in China on GDP, industrial output and new energy profits1 Ming-Jie Lu, C.-Y. Cynthia Lin and Song Chen Abstract This paper examines the effects of energy policies and the profits of new energy companies using instruments to address the potential endogeneity of the policies

  2. Uni-Traveling-Carrier Photodiodes with Increased Output Response and Low Intermodulation

    E-Print Network [OSTI]

    Bowers, John

    Uni-Traveling-Carrier Photodiodes with Increased Output Response and Low Intermodulation Distortion-traveling-carrier photodiodes have been fabricated and tested to investigate the influence of the doping profile in several of the device layers on saturation characteristics and linearity. Two particular photodiode (PD) structures

  3. THE EFFECTS OF NET ENTANGLEMENT ON THE DRAG AND POWER OUTPUT OF

    E-Print Network [OSTI]

    THE EFFECTS OF NET ENTANGLEMENT ON THE DRAG AND POWER OUTPUT OF A CALIFORNIA SEA LION, ZAWPHUS of entangled northern fur 'Scordino. J., and R. Fisher. 1983. Invelltigations on fur seal entanglement in net of plastic litter on beaches of several Alaskan islands. Using the number of net fragments found on shore

  4. Development of Regional Wind Resource and Wind Plant Output Datasets for the Hawaiian Islands

    SciTech Connect (OSTI)

    Manobianco, J.; Alonge, C.; Frank, J.; Brower, M.

    2010-07-01T23:59:59.000Z

    In March 2009, AWS Truepower was engaged by the National Renewable Energy Laboratory (NREL) to develop a set of wind resource and plant output data for the Hawaiian Islands. The objective of this project was to expand the methods and techniques employed in the Eastern Wind Integration and Transmission Study (EWITS) to include the state of Hawaii.

  5. On the Impact of Partial Shading on PV Output Power DEZSO SERA YAHIA BAGHZOUZ

    E-Print Network [OSTI]

    Sera, Dezso

    On the Impact of Partial Shading on PV Output Power DEZSO SERA YAHIA BAGHZOUZ Institute of Energy the inflation adjusted cost of PV energy has declined by roughly by a factor of 2 over the same time period [3 power capability. However, the relative amount of such degradation in energy production cannot

  6. A comparison between raw EPS output, (modied) BMA and extended LR using ECMWF EPS precipitation reforecasts

    E-Print Network [OSTI]

    Schmeits, Maurice

    A comparison between raw EPS output, (modied) BMA and extended LR using ECMWF EPS precipitation (EPS). 2. Data sets, statistical methods and predictand denitions The data sets used in this study [1 and precipitation data from a reforecasting exper- iment with the ECMWF EPS system. Figure 1: BMA-tted pdf of 24-h

  7. Increasing the output of a Littman-type laser by use of an intracavity Faraday rotator

    E-Print Network [OSTI]

    Hart, Gus

    are readily available at a variety of wavelengths from the red to the near infrared. They require their advantages. The sim- pler of the two designs is the Littrow scheme. In this arrangement a reflection grating. The zeroth- order grating reflection is used as the output coupler to extract light from the cavity. Light

  8. Work output of planetary atmospheric engines: dissipation in clouds and rain

    E-Print Network [OSTI]

    Lorenz, Ralph D.

    not provide enough work to lift the condensate against gravity. INDEX TERMS: 0343 Atmospheric Composition as the original function of steam engines was to lift water, a principal output of the atmospheric heat engine (the average surface temperature of 288K) and √ĀT $38K, the difference between T and the effective

  9. SHORT TERM PREDICTIONS FOR THE POWER OUTPUT OF ENSEMBLES OF WIND TURBINES AND PV-GENERATORS

    E-Print Network [OSTI]

    Heinemann, Detlev

    SHORT TERM PREDICTIONS FOR THE POWER OUTPUT OF ENSEMBLES OF WIND TURBINES AND PV-GENERATORS Hans. For the conventional power park, the power production of the wind turbines presents a fluctuating 'negative load PRODUCTION OF WIND TURBINES For the forecast of the power production of wind turbines two approaches may

  10. The sound power output of a monopole source in a cylindrical pipe containing area discontinuities

    E-Print Network [OSTI]

    Boyer, Edmond

    The sound power output of a monopole source in a cylindrical pipe containing area discontinuities W placed close to a pipe discontinuity. It is found that while the sound power of a monopole source in free pipe is constant in the plane wave region. A sharp increase in sound power is then seen to occur when

  11. Nuclear norm system identification with missing inputs and outputs Zhang Liua,

    E-Print Network [OSTI]

    Vandenberghe, Lieven

    Nuclear norm system identification with missing inputs and outputs Zhang Liua, , Anders Hanssonb,1 formulation and uses the nuclear norm heuristic for structured low-rank matrix approximation, with the missing of the alternating direc- tion method of multipliers (ADMM) to solve regularized or non-regularized nuclear norm

  12. Closed-loop identification via output fast sampling Jiandong Wang a

    E-Print Network [OSTI]

    Wang, Jiandong

    Closed-loop identification via output fast sampling Jiandong Wang a , Tongwen Chen a,*, Biao Huang6G 2V4 b Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB identifiable? The so-called fast-sampling direct approach provides a positive answer. It removes a traditional

  13. Greenland ice sheet surface mass balance variability (1988-2004) from calibrated Polar MM5 output*

    E-Print Network [OSTI]

    Howat, Ian M.

    1 Greenland ice sheet surface mass balance variability (1988-2004) from calibrated Polar MM5 output in Environmental Sciences, University of Colorado, Boulder, CO, USA 4 National Snow and Ice Data Center, University coherent regional patterns of Greenland ice sheet surface mass balance (SMB) change over a 17-year period

  14. Sensorless Adaptive Output Feedback Control of Wind Energy Systems with PMS Generators

    E-Print Network [OSTI]

    Boyer, Edmond

    1 Sensorless Adaptive Output Feedback Control of Wind Energy Systems with PMS Generators A. El the problem of controlling wind energy conversion (WEC) systems involving permanent magnet synchronous is to maximize wind energy extraction which cannot be achieved without letting the wind turbine rotor operate

  15. Using input-output techniques to address economic and energy issues in Malaysia

    E-Print Network [OSTI]

    activities. Expand the basic activity: manufacturing into two activities: 1) high energy intensity 2) low energy intensity Assume they have equal share of output and their input structure is similar: Then assume? Assume electricity intensity: · high energy intensity 1.4 · low energy intensity 0.4 Now calculate

  16. Assessment of Inlet Cooling to Enhance Output of a Fleet of Gas Turbines

    E-Print Network [OSTI]

    Wang, T.; Braquet, L.

    2008-01-01T23:59:59.000Z

    An analysis was made to assess the potential enhancement of a fleet of 14 small gas turbines' power output by employing an inlet air cooling scheme at a gas process plant. Various gas turbine (GT) inlet air cooling schemes were reviewed. The inlet...

  17. Generating short-output digest functions L.H. Nguyen and A.W. Roscoe

    E-Print Network [OSTI]

    Jeavons, Peter

    Generating short-output digest functions L.H. Nguyen and A.W. Roscoe Oxford University Computing and secure digest functions". This paper has also been submitted to AFRICACRYPT 2010 in January 2010 digest which has similarities to -balanced and almost universal hash func- tions. Digest functions

  18. Generalized Mercury/Waterfilling for Multiple-Input Multiple-Output Channels

    E-Print Network [OSTI]

    Verd√ļ, Sergio

    Generalized Mercury/Waterfilling for Multiple-Input Multiple-Output Channels Fernando P procedure that generalizes the mercury/waterfilling algorithm, previously proposed for parallel non-interfering chan- nels. In this generalization the mercury level accounts for the sub- optimal (non-Gaussian) input

  19. High-Output Nanogenerator by Rational Unipolar Assembly of Conical Nanowires and

    E-Print Network [OSTI]

    Wang, Zhong L.

    strain of 0.11% at a straining rate of 3.67% s-1 produces an output voltage up to 2 V (equivalent open circuit voltage of 3.3 V). This is a practical and versatile technology with the potential for powering, including vibration, air flow, and human physical motion, is available almost everywhere and at all times

  20. Shaping the output pulse of a linear-transformer-driver module W. A. Stygar,1

    E-Print Network [OSTI]

    Shaping the output pulse of a linear-transformer-driver module W. A. Stygar,1 W. E. Fowler,1 K. R a linear-transformer- driver (LTD) module that drives an internal water-insulated transmission line-insulated radial-transmission-line impedance transformers [Phys. Rev. ST Accel. Beams 11, 030401 (2008)]. DOI: 10

  1. Maximizing Power Output in Homogeneous Charge Compression Ignition (HCCI) Engines and Enabling Effective Control of Combustion Timing

    E-Print Network [OSTI]

    Saxena, Samveg

    2011-01-01T23:59:59.000Z

    Ford Motor Company, ďDiesel Engine Aftertreatment: How FordNational Laboratory, ďEngine Combustion NetworkĒ, http://High Power Output without Engine Knock and with Ultra-Low

  2. Operational forecasting based on a modified Weather Research and Forecasting model

    SciTech Connect (OSTI)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18T23:59:59.000Z

    Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

  3. Quantifying the Impact of Wind Turbine Wakes on Power Output at Offshore R. J. BARTHELMIE,*,1 S. C. PRYOR,*,1 S. T. FRANDSEN,1 K. S. HANSEN,# J. G. SCHEPERS,@

    E-Print Network [OSTI]

    Pryor, Sara C.

    Quantifying the Impact of Wind Turbine Wakes on Power Output at Offshore Wind Farms R. J. This research is focused on improving the understanding of, and modeling of, wind turbine wakes in order to make. Differences in turbine spacing (10.5 versus 7 rotor diameters) are not differentiable in wake-related power

  4. Variable gas spring for matching power output from FPSE to load of refrigerant compressor

    DOE Patents [OSTI]

    Chen, Gong (Athens, OH); Beale, William T. (Athens, OH)

    1990-01-01T23:59:59.000Z

    The power output of a free piston Stirling engine is matched to a gas compressor which it drives and its stroke amplitude is made relatively constant as a function of power by connecting a gas spring to the drive linkage from the engine to the compressor. The gas spring is connected to the compressor through a passageway in which a valve is interposed. The valve is linked to the drive linkage so it is opened when the stroke amplitude exceeds a selected limit. This allows compressed gas to enter the spring, increase its spring constant, thus opposing stroke increase and reducing the phase lead of the displacer ahead of the piston to reduce power output and match it to a reduced load power demand.

  5. Input-output Analysis of Quantum Finite-level Systems in Response to Single Photon States

    E-Print Network [OSTI]

    Yu Pan; Guofeng Zhang; Matthew R. James

    2015-01-01T23:59:59.000Z

    Single photon states, which carry quantum information and coherently interact with quantum systems, are vital to the realization of all-optical quantum networks and quantum memory. In this paper we derive the conditions that enable an exact analysis of the response of passive quantum finite-level systems under the weak driving of single photon input. We show that when a class of finite level systems is driven by single photon inputs, expressions for the output states may be derived exactly using linear systems transfer functions. This removes the need for physical approximations such as weak excitation limit in the analysis of quantum nonlinear systems under single photon driving. We apply this theory to the analysis of a single photon switch. The input-output relations are consistent with the existing results in the study of few photon transport through finite-level systems.

  6. Spin-on-doping for output power improvement of silicon nanowire array based thermoelectric power generators

    SciTech Connect (OSTI)

    Xu, B., E-mail: bin.xu09@imperial.ac.uk; Fobelets, K. [Department of Electrical and Electronic Engineering, Imperial College London, Exhibition Road, SW7 2BT London (United Kingdom)

    2014-06-07T23:59:59.000Z

    The output power of a silicon nanowire array (NWA)-bulk thermoelectric power generator (TEG) with Cu contacts is improved by spin-on-doping (SOD). The Si NWAs used in this work are fabricated via metal assisted chemical etching (MACE) of 0.01Ė0.02 ? cm resistivity n- and p-type bulk, converting ?4% of the bulk thickness into NWs. The MACE process is adapted to ensure crystalline NWs. Current-voltage and Seebeck voltage-temperature measurements show that while SOD mainly influences the contact resistance in bulk, it influences both contact resistance and power factor in NWA-bulk based TEGs. According to our experiments, using Si NWAs in combination with SOD increases the output power by an order of 3 under the same heating power due to an increased power factor, decreased thermal conductivity of the NWA and reduced Si-Cu contact resistance.

  7. Variable gas spring for matching power output from FPSE to load of refrigerant compressor

    DOE Patents [OSTI]

    Chen, G.; Beale, W.T.

    1990-04-03T23:59:59.000Z

    The power output of a free piston Stirling engine is matched to a gas compressor which it drives and its stroke amplitude is made relatively constant as a function of power by connecting a gas spring to the drive linkage from the engine to the compressor. The gas spring is connected to the compressor through a passageway in which a valve is interposed. The valve is linked to the drive linkage so it is opened when the stroke amplitude exceeds a selected limit. This allows compressed gas to enter the spring, increase its spring constant, thus opposing stroke increase and reducing the phase lead of the displacer ahead of the piston to reduce power output and match it to a reduced load power demand. 6 figs.

  8. Coupling of lateral grating displacement to the output ports of a diffractive Fabry-Perot cavity

    E-Print Network [OSTI]

    J. Hallam; S. Chelkowski; A. Freise; S. Hild; B. Barr; K. A. Strain; O. Burmeister; R. Schnabel

    2009-05-05T23:59:59.000Z

    Diffraction gratings have been proposed as core elements in future laser-interferometric gravitational-wave detectors. In this paper, we use a steady-state technique to derive coupling of lateral grating displacement to the output ports of a diffractive Fabry-Perot cavity. By introducing a signal to noise ratio (SNR) for each of the three cavity output ports the magnitude of the noise sidebands originating from lateral grating displacement are compared to the magnitude of a potential gravitational wave signal. For the example of a 3km long Fabry-Perot cavity featuring parameters similar to the planned Advanced Virgo instrument, we found that the forward-reflecting grating port offers the highest SNR at low frequencies. Furthermore, for this example suspension requirements for lateral isolation were computed, and a factor of twenty relaxation at a frequency of 10Hz can be gained over the transmitted port by observing the forward-reflected port.

  9. E-Print Network 3.0 - average model parameter Sample Search Results

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    also the shape... P) that was calculated using the mean of the output distribution, by ranking model parameters according to their impact... the effect of parameters on model...

  10. Input/Output of ab-initio nuclear structure calculations for improved performance and portability

    SciTech Connect (OSTI)

    Laghave, Nikhil

    2010-12-15T23:59:59.000Z

    Many modern scientific applications rely on highly computation intensive calculations. However, most applications do not concentrate as much on the role that input/output operations can play for improved performance and portability. Parallelizing input/output operations of large files can significantly improve the performance of parallel applications where sequential I/O is a bottleneck. A proper choice of I/O library also offers a scope for making input/output operations portable across different architectures. Thus, use of parallel I/O libraries for organizing I/O of large data files offers great scope in improving performance and portability of applications. In particular, sequential I/O has been identified as a bottleneck for the highly scalable MFDn (Many Fermion Dynamics for nuclear structure) code performing ab-initio nuclear structure calculations. We develop interfaces and parallel I/O procedures to use a well-known parallel I/O library in MFDn. As a result, we gain efficient I/O of large datasets along with their portability and ease of use in the down-stream processing. Even situations where the amount of data to be written is not huge, proper use of input/output operations can boost the performance of scientific applications. Application checkpointing offers enormous performance improvement and flexibility by doing a negligible amount of I/O to disk. Checkpointing saves and resumes application state in such a manner that in most cases the application is unaware that there has been an interruption to its execution. This helps in saving large amount of work that has been previously done and continue application execution. This small amount of I/O provides substantial time saving by offering restart/resume capability to applications. The need for checkpointing in optimization code NEWUOA has been identified and checkpoint/restart capability has been implemented in NEWUOA by using simple file I/O.

  11. ARM - Campaign Instrument - wrf-chem

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary)morphinanInformationbudapest Comments? We would love to heartotdngovInstrumentswrf-chem Comments? We would love to

  12. IR DIAL performance modeling

    SciTech Connect (OSTI)

    Sharlemann, E.T.

    1994-07-01T23:59:59.000Z

    We are developing a DIAL performance model for CALIOPE at LLNL. The intent of the model is to provide quick and interactive parameter sensitivity calculations with immediate graphical output. A brief overview of the features of the performance model is given, along with an example of performance calculations for a non-CALIOPE application.

  13. Weak values and weak coupling maximizing the output of weak measurements

    SciTech Connect (OSTI)

    Di Lorenzo, Antonio, E-mail: dilorenzo.antonio@gmail.com

    2014-06-15T23:59:59.000Z

    In a weak measurement, the average output ?o? of a probe that measures an observable A{sup -hat} of a quantum system undergoing both a preparation in a state ?{sub i} and a postselection in a state E{sub f} is, to a good approximation, a function of the weak value A{sub w}=Tr[E{sub f}A{sup -hat} ?{sub i}]/Tr[E{sub f}?{sub i}], a complex number. For a fixed coupling ?, when the overlap Tr[E{sub f}?{sub i}] is very small, A{sub w} diverges, but ?o? stays finite, often tending to zero for symmetry reasons. This paper answers the questions: what is the weak value that maximizes the output for a fixed coupling? What is the coupling that maximizes the output for a fixed weak value? We derive equations for the optimal values of A{sub w} and ?, and provide the solutions. The results are independent of the dimensionality of the system, and they apply to a probe having a Hilbert space of arbitrary dimension. Using the SchrŲdingerĖRobertson uncertainty relation, we demonstrate that, in an important case, the amplification ?o? cannot exceed the initial uncertainty ?{sub o} in the observable o{sup -hat}, we provide an upper limit for the more general case, and a strategy to obtain ?o???{sub o}. - Highlights: ēWe have provided a general framework to find the extremal values of a weak measurement. ēWe have derived the location of the extremal values in terms of preparation and postselection. ēWe have devised a maximization strategy going beyond the limit of the SchrŲdingerĖRobertson relation.

  14. All-optical routing of single photons with multiple input and output ports by interferences

    E-Print Network [OSTI]

    Wei-Bin Yan; Bao Liu; Ling Zhou; Heng Fan

    2014-09-23T23:59:59.000Z

    We propose a waveguide-cavity coupled system to achieve the routing of photons by the phases of other photons. Our router has four input ports and four output ports. The transport of the coherent-state photons injected through any input port can be controlled by the phases of the coherent-state photons injected through other input ports. This control can be achieved when the mean numbers of the routed and control photons are small enough and require no additional control fields. Therefore, the all-optical routing of photons can be achieved at the single-photon level.

  15. Improving the Thermal Output Availability of Reciprocating Engine Cogeneration Systems by Mechanical Vapor Compression

    E-Print Network [OSTI]

    Becker, F. E.; DiBella, F. A.; Lamphere, F.

    LOW?PRESSURE I WASTE STEAM r ... IMPROVING THE THERMAL OUTPUT AVAILABILITY OF RECIPROCATING ENGINE COGENERATION SYSTEMS BY MECHANICAL VAPOR COMPRESSION F.E. Becker and F.A. DiBella Tecogen, Inc., a Subsidiary of Thermo El~ctron Corporation...-user with electric power and process heat that is totally in the form of high-pressure steam. Current recipro cating engine systems can now provide only low pressure steam or hot water from the engine jacket, and this often is not needed or not the most appro...

  16. Cascade design of single input single output systems using H? and quantitative feedback theory methodologies

    E-Print Network [OSTI]

    Lal, Mayank

    2005-02-17T23:59:59.000Z

    . It is shown using QFT methodology that there aren?t any advantages gained in the low frequencies with the use of cascaded design. In effect it is concluded that if the design is properly executed a single loop controller closed from the output to the input... In the fourth part the H? methodology was used to design a two loop control structure. The idea was to compare this design to the QFT design. It was seen that H? generated redundant controllers and pre filters...

  17. Investigation of various ways of obtaining output waveforms of CMOS digital circuits by explicit methods

    E-Print Network [OSTI]

    Ong, Lian Wah

    1989-01-01T23:59:59.000Z

    Local and Total Error. 18 5. 1 Full Adder, FADD. 30 5. 2 Waveform Comparison with Spice: JKFP. 5. 3 Waveform Comparison with Spice: FADD. 32 5. 4 Effect of AV on Window Method. 5. 5 4-Bits Counter, CB41. 5. 6 4-Bits Counter, CM14. 34 38 5. 7 4... already know its input wave (ie. the output waveform of S4). We can then adjust the step size according to this input wave. The step size is varied as follows: (1) Initially, the whole circuit is simulated based on a specified AV (eg 0. 5V...

  18. Output-power fluctuations of flowing-gas CO/sub 2/ lasers with unstable resonators

    SciTech Connect (OSTI)

    Artamonov, A.V.; Konev, V.A.; Likhanskii, V.V.; Napartovich, A.P.

    1984-06-01T23:59:59.000Z

    An experimental study was made of the influence of different factors on the stability of the output intensity of a flowing-gas CO/sub 2/ laser with an unstable resonator. The measured amplitude--frequency characteristics of the intensity fluctuation spectrum had resonance peaks at multiples of the frequency corresponding to the transit time of the gas to the optic axis of the resonator. A rise in the efficiency of the laser system was found to be accompanied by an increase in the amplitude of the fluctuations of the radiation intensity.

  19. On using transputers to design the header and output processors for the PSi architecture

    E-Print Network [OSTI]

    Manickam, Muralidhar

    1991-01-01T23:59:59.000Z

    -Amara (Chair of Committee) Karan Watson (M mb ) ~~) rkady K evsky (Mem er) 0 ~ ~g/8 dgar Sanchez-Sinencio (Member) Jo W. Howze (Head of Department) December 1991 ABSTRACT On Using Transputers to Design the IIeacler an&1 Output Processors... . . APPENDIX E . . 55 CHAPTER Page VITA 95 LIST OF TABLES TABLE Page l. l. Results Table I. 4. 1. Speerl Settings for Transputer Links. 4. 2. T-0000. '2:I '&. I. DIS T-g00 External I(le&nore Perfuruuune. . '&. 2. DIS T-g00 Itlelnor~ Refresll...

  20. Polarization requirements for ensemble implementations of quantum algorithms with a single-bit output

    SciTech Connect (OSTI)

    Anderson, Brandon M.; Collins, David [Department of Physics, University of Texas at Dallas, P.O. Box 830688, Richardson, Texas 75083 (United States); Department of Physics, Bucknell University, Lewisburg, Pennsylvania 17837 (United States)

    2005-10-15T23:59:59.000Z

    We compare the failure probabilities of ensemble implementations of quantum algorithms which use pseudopure initial states, quantified by their polarization, to those of competing classical probabilistic algorithms. Specifically we consider a class algorithms which require only one bit to output the solution to problems. For large ensemble sizes, we present a general scheme to determine a critical polarization beneath which the quantum algorithm fails with greater probability than its classical competitor. We apply this to the Deutsch-Jozsa algorithm and show that the critical polarization is 86.6%.

  1. Techniques for increasing output power from mode-locked semiconductor lasers

    SciTech Connect (OSTI)

    Mar, A.; Vawter, G.A.

    1996-02-01T23:59:59.000Z

    Mode-locked semiconductor lasers have drawn considerable attention as compact, reliable, and relatively inexpensive sources of short optical pulses. Advances in the design of such lasers have resulted in vast improvements in pulsewidth and noise performance, at a very wide range of repetition rates. An attractive application for these lasers would be to serve as alternatives for large benchtop laser systems such as dye lasers and solid-state lasers. However, mode-locked semiconductor lasers have not yet approached the performance of such systems in terms of output power. Different techniques for overcoming the problem of low output power from mode-locked semiconductor lasers will be discussed. Flared and arrayed lasers have been used successfully to increase the pulse saturation energy limit by increasing the gain cross section. Further improvements have been achieved by use of the MOPA configuration, which utilizes a flared semiconductor amplifier s amplify pulses to energies of 120 pJ and peak powers of nearly 30W.

  2. Multidimensional simulation studies of the SELENE FEL oscillator/buncher followed by a radiator/amplifier output scheme

    SciTech Connect (OSTI)

    Hahn, S.J. [Stanford Linear Accelerator Center, Menlo Park, CA (United States); Fawley, W.M. [Lawrence Berkeley Lab., CA (United States)

    1995-02-01T23:59:59.000Z

    We analyze and present numerical simulations of the so-called electron output scheme [G. I. Erg et al., 15th Int. FEL Conf., The Hague, The Netherlands, 1993, Book of Abstracts p. 50; Preprint Budker INP 93-75] applied to the SELENE proposal of using a high power FEL to illuminate satellite solar cells. In this scheme, a first stage FEL oscillator bunches the electron beam while a second stage ``radiator`` extracts high power radiation. Our analysis suggests only in the case where the radiator employs a long, tapered undulator will the electron output scheme produce a significant increase in extraction efficiency over what is obtainable from a simple, single-stage oscillator. 1- and 2-D numerical simulations of a 1.7{mu}m FEL employing the electron output scheme show reasonably large bunching fractions ({approximately} 0.3--0.4) at the output of the oscillator stage but only {le}2% extraction efficiency from the radiator stage.

  3. GAMS program used to estimate capacity with the hyperbolic graph efficiency measure, with constant returns to scale and undesirable outputs.

    E-Print Network [OSTI]

    . "Estimating Capacity and Efficiency in Fisheries with Undesirable Outputs." VIMS Marine resource Report N(obs) weights gamma(obs,var) ; POSITIVE Variable weight, gamma; EQUATIONS CONSTR1(GOUTPUT, OBS) DEA constraint

  4. GAMS program used to estimate capacity with the hyperbolic graph efficiency measure, with variable returns to scale and undesirable outputs.

    E-Print Network [OSTI]

    . "Estimating Capacity and Efficiency in Fisheries with Undesirable Outputs." VIMS Marine resource Report N(obs) weights gamma(obs,var) ; POSITIVE Variable weight, gamma; EQUATIONS CONSTR1(GOUTPUT, OBS) DEA constraint

  5. Maximizing Power Output in Homogeneous Charge Compression Ignition (HCCI) Engines and Enabling Effective Control of Combustion Timing

    E-Print Network [OSTI]

    Saxena, Samveg

    2011-01-01T23:59:59.000Z

    and Yang, Y. , ďBoosted HCCI for High Power Output withoutand emissions in a HCCI engine for power generationĒ, Energythe synergy with a gasoline HCCI engineĒ, SAE Paper 2011-01-

  6. Reflecting upon vision: The limited effects of visual feedback upon motor output in a bimanual tapping task.†

    E-Print Network [OSTI]

    Iveson, Matthew

    2009-07-03T23:59:59.000Z

    Whilst previous studies have focused on the importance of other sensory information, the use of mirrors has been instrumental in investigating the contribution of visual feedback to motor output. Franz and Packman (2004) ...

  7. Long-term stable timing distribution of an ultrafast optical pulse train over multiple fiber links with polarization maintaining output

    E-Print Network [OSTI]

    Cox, Jonathan A.

    The distribution of an ultrafast optical pulse train over multiple fiber links with long-term stable timing precision within 2 femtoseconds rms is accomplished by integrating a polarization maintaining output with 300 meter ...

  8. The CO2 Content of Consumption Across US Regions: A Multi-Regional Input-Output (MRIO) Approach

    E-Print Network [OSTI]

    Caron, J.

    We improve on existing estimates of the carbon dioxide (CO2) content of consumption across regions of the United States. Using a multi-regional input-output (MRIO) framework, we estimate the direct and indirect CO2 emissions ...

  9. High-Resolution Modeling to Assess Tropical Cyclone Activity in Future Climate Regimes

    SciTech Connect (OSTI)

    Lackmann, Gary

    2013-06-10T23:59:59.000Z

    Applied research is proposed with the following objectives: (i) to determine the most likely level of tropical cyclone intensity and frequency in future climate regimes, (ii) to provide a quantitative measure of uncertainty in these predictions, and (iii) to improve understanding of the linkage between tropical cyclones and the planetary-scale circulation. Current mesoscale weather forecasting models, such as the Weather Research and Forecasting (WRF) model, are capable of simulating the full intensity of tropical cyclones (TC) with realistic structures. However, in order to accurately represent both the primary and secondary circulations in these systems, model simulations must be configured with sufficient resolution to explicitly represent convection (omitting the convective parameterization scheme). Most previous numerical studies of TC activity at seasonal and longer time scales have not utilized such explicit convection (EC) model runs. Here, we propose to employ the moving nest capability of WRF to optimally represent TC activity on a seasonal scale using a downscaling approach. The statistical results of a suite of these high-resolution TC simulations will yield a realistic representation of TC intensity on a seasonal basis, while at the same time allowing analysis of the feedback that TCs exert on the larger-scale climate system. Experiments will be driven with analyzed lateral boundary conditions for several recent Atlantic seasons, spanning a range of activity levels and TC track patterns. Results of the ensemble of WRF simulations will then be compared to analyzed TC data in order to determine the extent to which this modeling setup can reproduce recent levels of TC activity. Next, the boundary conditions (sea-surface temperature, tropopause height, and thermal/moisture profiles) from the recent seasons will be altered in a manner consistent with various future GCM/RCM scenarios, but that preserves the large-scale shear and incipient disturbance activity. This will allow (i) a direct comparison of future TC activity that could be expected for an active or inactive season in an altered climate regime, and (ii) a measure of the level of uncertainty and variability in TC activity resulting from different carbon emission scenarios.

  10. Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    an implementation of a single-fluid inter- face model in the ALE-AMR code to simulate surface tension effects. The model does not require explicit information on the physical...

  11. Gain, directionality and noise in microwave SQUID amplifiers: Input-output approach

    E-Print Network [OSTI]

    Archana Kamal; John Clarke; Michel Devoret

    2012-06-20T23:59:59.000Z

    We present a new theoretical framework to analyze microwave amplifiers based on the dc SQUID. Our analysis applies input-output theory generalized for Josephson junction devices biased in the running state. Using this approach we express the high frequency dynamics of the SQUID as a scattering between the participating modes. This enables us to elucidate the inherently nonreciprocal nature of gain as a function of bias current and input frequency. This method can, in principle, accommodate an arbitrary number of Josephson harmonics generated in the running state of the junction. We report detailed calculations taking into account the first few harmonics that provide simple semi-quantitative results showing a degradation of gain, directionality and noise of the device as a function of increasing signal frequency. We also discuss the fundamental limits on device performance and applications of this formalism to real devices.

  12. A 350 MHz, 200 kW CW, Multiple Beam Inductive Output Tube - Final Report

    SciTech Connect (OSTI)

    R.Lawrece Ives; George Collins; David Marsden Michael Read; Edward Eisen; Takuchi Kamura, Philipp Borchard

    2012-11-28T23:59:59.000Z

    This program developed a 200 kW CW, 350 MHz, multiple beam inductive output tube (MBIOT) for driving accelerator cavities. The MBIOT operates at 30 kV with a gain of 23 dB. The estimated efficiency is 70%. The device uses seven electron beams, each transmitting 1.4 A of current. The tube is approximately six feet long and weighs approximately 400 lbs. The prototype device will be evaluated as a potential RF source for the Advanced Photon Source at Argonne National Laboratory (ANL). Because of issues related to delivery of the electron guns, it was not possible to complete assembly and test of the MBIOT during the Phase II program. The device is being completed with support from Calabazas Creek Research, Inc., Communications & Power Industries, LLC. and the Naval Surface Weapons Center (NSWC) in Dahlgren, VA. The MBIOT will be initially tested at NSWC before delivery to ANL. The testing at NSWC is scheduled for February 2013.

  13. Next generation input-output data format for HEP using Google's protocol buffers

    E-Print Network [OSTI]

    Chekanov, S V

    2013-01-01T23:59:59.000Z

    We propose a data format for Monte Carlo (MC) events, or any structural data, including experimental data, in a compact binary form using variable-size integer encoding as implemented in the Google's Protocol Buffers package. This approach is implemented in the so-called ProMC library which produces smaller file sizes for MC records compared to the existing input-output libraries used in high-energy physics (HEP). Other important features are a separation of abstract data layouts from concrete programming implementations, self-description and random access. Data stored in ProMC files can be written, read and manipulated in a number of programming languages, such C++, Java and Python.

  14. ProMC: Input-output data format for HEP applications using varint encoding

    E-Print Network [OSTI]

    Chekanov, S V; Van Gemmeren, P

    2013-01-01T23:59:59.000Z

    A new data format for Monte Carlo (MC) events, or any structural data, including experimental data, is discussed. The format is designed to store data in a compact binary form using variable-size integer encoding as implemented in the Google's Protocol Buffers package. This approach is implemented in the ProMC library which produces smaller file sizes for MC records compared to the existing input-output libraries used in high-energy physics (HEP). Other important features of the proposed format are a separation of abstract data layouts from concrete programming implementations, self-description and random access. Data stored in ProMC files can be written, read and manipulated in a number of programming languages, such C++, JAVA and PYTHON.

  15. ProMC: Input-output data format for HEP applications using varint encoding

    E-Print Network [OSTI]

    S. V. Chekanov; E. May; K. Strand; P. Van Gemmeren

    2014-04-03T23:59:59.000Z

    A new data format for Monte Carlo (MC) events, or any structural data, including experimental data, is discussed. The format is designed to store data in a compact binary form using variable-size integer encoding as implemented in the Google's Protocol Buffers package. This approach is implemented in the ProMC library which produces smaller file sizes for MC records compared to the existing input-output libraries used in high-energy physics (HEP). Other important features of the proposed format are a separation of abstract data layouts from concrete programming implementations, self-description and random access. Data stored in ProMC files can be written, read and manipulated in a number of programming languages, such C++, JAVA, FORTRAN and PYTHON.

  16. Next generation input-output data format for HEP using Google's protocol buffers

    E-Print Network [OSTI]

    S. V. Chekanov

    2013-06-27T23:59:59.000Z

    We propose a data format for Monte Carlo (MC) events, or any structural data, including experimental data, in a compact binary form using variable-size integer encoding as implemented in the Google's Protocol Buffers package. This approach is implemented in the so-called ProMC library which produces smaller file sizes for MC records compared to the existing input-output libraries used in high-energy physics (HEP). Other important features are a separation of abstract data layouts from concrete programming implementations, self-description and random access. Data stored in ProMC files can be written, read and manipulated in a number of programming languages, such C++, Java and Python.

  17. Impact of subgrid-scale radiative heating variability on the stratocumulus-to-trade cumulus transition in climate models

    SciTech Connect (OSTI)

    Xiao, Heng; Gustafson, William I.; Wang, Hailong

    2014-04-29T23:59:59.000Z

    Subgrid-scale interactions between turbulence and radiation are potentially important for accurately reproducing marine low clouds in climate models. To better understand the impact of these interactions, the Weather Research and Forecasting (WRF) model is configured for large eddy simulation (LES) to study the stratocumulus-to-trade cumulus (Sc-to-Cu) transition. Using the GEWEX Atmospheric System Studies (GASS) composite Lagrangian transition case and the Atlantic Trade Wind Experiment (ATEX) case, it is shown that the lack of subgrid-scale turbulence-radiation interaction, as is the case in current generation climate models, accelerates the Sc-to-Cu transition. Our analysis suggests that in cloud-topped boundary layers subgrid-scale turbulence-radiation interactions contribute to stronger production of temperature variance, which in turn leads to stronger buoyancy production of turbulent kinetic energy and helps to maintain the Sc cover.

  18. Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    a single-fluid diffuse interface model in the ALE-AMR hydrodynamics code to simulate surface tension effects. We show simula- tions and compare them to other surface tension...

  19. Modeling

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    sion effects. We show the result of a test case, and compare it to the result without surface tension. The model describes droplet formation nicely. Application The ARRA-funded...

  20. On the generalization of linear least mean squares estimation to quantum systems with non-commutative outputs

    E-Print Network [OSTI]

    Nina H. Amini; Zibo Miao; Yu Pan; Matthew R. James; Hideo Mabuchi

    2015-01-19T23:59:59.000Z

    The purpose of this paper is to study the problem of generalizing the Belavkin-Kalman filter to the case where the classical measurement signal is replaced by a fully quantum non-commutative output signal. We formulate a least mean squares estimation problem that involves a non-commutative system as the filter processing the non-commutative output signal. We solve this estimation problem within the framework of non-commutative probability. Also, we find the necessary and sufficient conditions which make these non-commutative estimators physically realizable.