National Library of Energy BETA

Sample records for random number times

  1. Practical and fast quantum random number generation based on photon arrival time relative to external reference

    SciTech Connect (OSTI)

    Nie, You-Qi; Zhang, Jun Pan, Jian-Wei; Zhang, Hong-Fei; Wang, Jian; Zhang, Zhen; Ma, Xiongfeng

    2014-02-03

    We present a practical high-speed quantum random number generator, where the timing of single-photon detection relative to an external time reference is measured as the raw data. The bias of the raw data can be substantially reduced compared with the previous realizations. The raw random bit rate of our generator can reach 109 Mbps. We develop a model for the generator and evaluate the min-entropy of the raw data. Toeplitz matrix hashing is applied for randomness extraction, after which the final random bits are able to pass the standard randomness tests.

  2. Quantum Statistical Testing of a Quantum Random Number Generator

    SciTech Connect (OSTI)

    Humble, Travis S

    2014-01-01

    The unobservable elements in a quantum technology, e.g., the quantum state, complicate system verification against promised behavior. Using model-based system engineering, we present methods for verifying the opera- tion of a prototypical quantum random number generator. We begin with the algorithmic design of the QRNG followed by the synthesis of its physical design requirements. We next discuss how quantum statistical testing can be used to verify device behavior as well as detect device bias. We conclude by highlighting how system design and verification methods must influence effort to certify future quantum technologies.

  3. Statistical analysis of random duration times

    SciTech Connect (OSTI)

    Engelhardt, M.E.

    1996-04-01

    This report presents basic statistical methods for analyzing data obtained by observing random time durations. It gives nonparametric estimates of the cumulative distribution function, reliability function and cumulative hazard function. These results can be applied with either complete or censored data. Several models which are commonly used with time data are discussed, and methods for model checking and goodness-of-fit tests are discussed. Maximum likelihood estimates and confidence limits are given for the various models considered. Some results for situations where repeated durations such as repairable systems are also discussed.

  4. Multi-bit quantum random number generation by measuring positions of arrival photons

    SciTech Connect (OSTI)

    Yan, Qiurong; Zhao, Baosheng; Liao, Qinghong; Zhou, Nanrun

    2014-10-15

    We report upon the realization of a novel multi-bit optical quantum random number generator by continuously measuring the arrival positions of photon emitted from a LED using MCP-based WSA photon counting imaging detector. A spatial encoding method is proposed to extract multi-bits random number from the position coordinates of each detected photon. The randomness of bits sequence relies on the intrinsic randomness of the quantum physical processes of photonic emission and subsequent photoelectric conversion. A prototype has been built and the random bit generation rate could reach 8 Mbit/s, with random bit generation efficiency of 16 bits per detected photon. FPGA implementation of Huffman coding is proposed to reduce the bias of raw extracted random bits. The random numbers passed all tests for physical random number generator.

  5. Bad Estimates as a Function of Exceeding the MCNP Random Number Stride

    SciTech Connect (OSTI)

    Booth, Thomas E.

    2014-05-05

    Examples of bad MCNP estimates resulting from exceeding the MCNP random number stride are given for a simple infinite medium problem.

  6. Robust random number generation using steady-state emission of gain-switched laser diodes

    SciTech Connect (OSTI)

    Yuan, Z. L. Lucamarini, M.; Dynes, J. F.; Frhlich, B.; Plews, A.; Shields, A. J.

    2014-06-30

    We demonstrate robust, high-speed random number generation using interference of the steady-state emission of guaranteed random phases, obtained through gain-switching a semiconductor laser diode. Steady-state emission tolerates large temporal pulse misalignments and therefore significantly improves the interference quality. Using an 8-bit digitizer followed by a finite-impulse-response unbiasing algorithm, we achieve random number generation rates of 8 and 20?Gb/s, for laser repetition rates of 1 and 2.5?GHz, respectively, with a 20% tolerance in the interferometer differential delay. We also report a generation rate of 80?Gb/s using partially phase-correlated short pulses. In relation to the field of quantum key distribution, our results confirm the gain-switched laser diode as a suitable light source, capable of providing phase-randomized coherent pulses at a clock rate of up to 2.5?GHz.

  7. Oracle inequalities for SVMs that are based on random entropy numbers

    SciTech Connect (OSTI)

    Steinwart, Ingo

    2009-01-01

    In this paper we present a new technique for bounding local Rademacher averages of function classes induced by a loss function and a reproducing kernel Hilbert space (RKHS). At the heart of this technique lies the observation that certain expectations of random entropy numbers can be bounded by the eigenvalues of the integral operator associated to the RKHS. We then work out the details of the new technique by establishing two new oracle inequalities for SVMs, which complement and generalize orevious results.

  8. Number

    Office of Legacy Management (LM)

    ' , /v-i 2 -i 3 -A, This dow'at consists ~f--~-_,_~~~p.~,::, Number -------of.-&--copies, 1 Series.,-a-,-. ! 1 THE UNIVERSITY OF ROCHESTER 1; r-.' L INTRAMURALCORRESPONDENCE i"ks' 3 2.. September 25, 1947 Memo.tor Dr. A. H, Dovdy . From: Dr. H. E, Stokinger Be: Trip Report - Mayvood Chemical Works A trip vas made Nednesday, August 24th vith Messrs. Robert W ilson and George Sprague to the Mayvood Chemical F!orks, Mayvood, New Jersey one of 2 plants in the U.S.A. engaged in the

  9. Statistical Stability and Time-Reversal Imgaing in Random Media

    SciTech Connect (OSTI)

    Berryman, J; Borcea, L; Papanicolaou, G; Tsogka, C

    2002-02-05

    Localization of targets imbedded in a heterogeneous background medium is a common problem in seismic, ultrasonic, and electromagnetic imaging problems. The best imaging techniques make direct use of the eigenfunctions and eigenvalues of the array response matrix, as recent work on time-reversal acoustics has shown. Of the various imaging functionals studied, one that is representative of a preferred class is a time-domain generalization of MUSIC (MUltiple Signal Classification), which is a well-known linear subspace method normally applied only in the frequency domain. Since statistical stability is not characteristic of the frequency domain, a transform back to the time domain after first diagonalizing the array data in the frequency domain takes optimum advantage of both the time-domain stability and the frequency-domain orthogonality of the relevant eigenfunctions.

  10. Flow Intermittency, Dispersion, and Correlated Continuous Time Random Walks in Porous Media

    SciTech Connect (OSTI)

    de Anna, Pietro; Le Borgne, Tanguy; Dentz, Marco; Tartakovsky, Alexandre M.; Bolster, Diogo; Davy, Philippe

    2013-05-01

    We study the intermittency of fluid velocities in porous media and its relation to anomalous dispersion. Lagrangian velocities measured at equidistant points along streamlines are shown to form a spatial Markov process. As a consequence of this remarkable property, the dispersion of fluid particles can be described by a continuous time random walk with correlated temporal increments. This new dynamical picture of intermittency provides a direct link between the microscale flow, its intermittent properties, and non-Fickian dispersion.

  11. Dynamic Response of an Optomechanical System to a Stationary Random Excitation in the Time Domain

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Palmer, Jeremy A.; Paez, Thomas L.

    2011-01-01

    Modern electro-optical instruments are typically designed with assemblies of optomechanical members that support optics such that alignment is maintained in service environments that include random vibration loads. This paper presents a nonlinear numerical analysis that calculates statistics for the peak lateral response of optics in an optomechanical sub-assembly subject to random excitation of the housing. The work is unique in that the prior art does not address peak response probability distribution for stationary random vibration in the time domain for a common lens-retainer-housing system with Coulomb damping. Analytical results are validated by using displacement response data from random vibration testingmore » of representative prototype sub-assemblies. A comparison of predictions to experimental results yields reasonable agreement. The Type I Asymptotic form provides the cumulative distribution function for peak response probabilities. Probabilities are calculated for actual lens centration tolerances. The probability that peak response will not exceed the centration tolerance is greater than 80% for prototype configurations where the tolerance is high (on the order of 30 micrometers). Conversely, the probability is low for those where the tolerance is less than 20 micrometers. The analysis suggests a design paradigm based on the influence of lateral stiffness on the magnitude of the response.« less

  12. Beyond the random phase approximation: Stimulated Brillouin backscatter for finite laser coherence times

    SciTech Connect (OSTI)

    Korotkevich, Alexander O.; Lushnikov, Pavel M.; Rose, Harvey A.

    2015-01-15

    We developed a linear theory of backward stimulated Brillouin scatter (BSBS) of a spatially and temporally random laser beam relevant for laser fusion. Our analysis reveals a new collective regime of BSBS (CBSBS). Its intensity threshold is controlled by diffraction, once cT{sub c} exceeds a laser speckle length, with T{sub c} the laser coherence time. The BSBS spatial gain rate is approximately the sum of that due to CBSBS, and a part which is independent of diffraction and varies linearly with T{sub c}. The CBSBS spatial gain rate may be reduced significantly by the temporal bandwidth of KrF-based laser systems compared to the bandwidth currently available to temporally smoothed glass-based laser systems.

  13. Characterization of compounds by time-of-flight measurement utilizing random fast ions

    DOE Patents [OSTI]

    Conzemius, R.J.

    1989-04-04

    An apparatus is described for characterizing the mass of sample and daughter particles, comprising a source for providing sample ions; a fragmentation region wherein a fraction of the sample ions may fragment to produce daughter ion particles; an electrostatic field region held at a voltage level sufficient to effect ion-neutral separation and ion-ion separation of fragments from the same sample ion and to separate ions of different kinetic energy; a detector system for measuring the relative arrival times of particles; and processing means operatively connected to the detector system to receive and store the relative arrival times and operable to compare the arrival times with times detected at the detector when the electrostatic field region is held at a different voltage level and to thereafter characterize the particles. Sample and daughter particles are characterized with respect to mass and other characteristics by detecting at a particle detector the relative time of arrival for fragments of a sample ion at two different electrostatic voltage levels. The two sets of particle arrival times are used in conjunction with the known altered voltage levels to mathematically characterize the sample and daughter fragments. In an alternative embodiment the present invention may be used as a detector for a conventional mass spectrometer. In this embodiment, conventional mass spectrometry analysis is enhanced due to further mass resolving of the detected ions. 8 figs.

  14. Characterization of compounds by time-of-flight measurement utilizing random fast ions

    DOE Patents [OSTI]

    Conzemius, Robert J.

    1989-01-01

    An apparatus for characterizing the mass of sample and daughter particles, comprising a source for providing sample ions; a fragmentation region wherein a fraction of the sample ions may fragment to produce daughter ion particles; an electrostatic field region held at a voltage level sufficient to effect ion-neutral separation and ion-ion separation of fragments from the same sample ion and to separate ions of different kinetic energy; a detector system for measuring the relative arrival times of particles; and processing means operatively connected to the detector system to receive and store the relative arrival times and operable to compare the arrival times with times detected at the detector when the electrostatic field region is held at a different voltage level and to thereafter characterize the particles. Sample and daughter particles are characterized with respect to mass and other characteristics by detecting at a particle detector the relative time of arrival for fragments of a sample ion at two different electrostatic voltage levels. The two sets of particle arrival times are used in conjunction with the known altered voltage levels to mathematically characterize the sample and daughter fragments. In an alternative embodiment the present invention may be used as a detector for a conventional mass spectrometer. In this embodiment, conventional mass spectrometry analysis is enhanced due to further mass resolving of the detected ions.

  15. A Possible Approach to Inclusion of Space and Time in Frame Fields of Quantum Representations of Real and Complex Numbers

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Benioff, Paul

    2009-01-01

    This work is based on the field of reference frames based on quantum representations of real and complex numbers described in other work. Here frame domains are expanded to include space and time lattices. Strings of qukits are described as hybrid systems as they are both mathematical and physical systems. As mathematical systems they represent numbers. As physical systems in each frame the strings have a discrete Schrodinger dynamics on the lattices. The frame field has an iterative structure such that the contents of a stage j frame have images in a stage j - 1 (parent)more »frame. A discussion of parent frame images includes the proposal that points of stage j frame lattices have images as hybrid systems in parent frames. The resulting association of energy with images of lattice point locations, as hybrid systems states, is discussed. Representations and images of other physical systems in the different frames are also described. « less

  16. Discrete-Time Pricing and Optimal Exercise of American Perpetual Warrants in the Geometric Random Walk Model

    SciTech Connect (OSTI)

    Vanderbei, Robert J.; P Latin-Small-Letter-Dotless-I nar, Mustafa C.; Bozkaya, Efe B.

    2013-02-15

    An American option (or, warrant) is the right, but not the obligation, to purchase or sell an underlying equity at any time up to a predetermined expiration date for a predetermined amount. A perpetual American option differs from a plain American option in that it does not expire. In this study, we solve the optimal stopping problem of a perpetual American option (both call and put) in discrete time using linear programming duality. Under the assumption that the underlying stock price follows a discrete time and discrete state Markov process, namely a geometric random walk, we formulate the pricing problem as an infinite dimensional linear programming (LP) problem using the excessive-majorant property of the value function. This formulation allows us to solve complementary slackness conditions in closed-form, revealing an optimal stopping strategy which highlights the set of stock-prices where the option should be exercised. The analysis for the call option reveals that such a critical value exists only in some cases, depending on a combination of state-transition probabilities and the economic discount factor (i.e., the prevailing interest rate) whereas it ceases to be an issue for the put.

  17. Method and allocation device for allocating pending requests for data packet transmission at a number of inputs to a number of outputs of a packet switching device in successive time slots

    DOE Patents [OSTI]

    Abel, Francois (Rueschlikon, CH); Iliadis, Ilias (Rueschlikon, CH); Minkenberg, Cyriel J. A. (Adliswil, CH)

    2009-02-03

    A method for allocating pending requests for data packet transmission at a number of inputs to a number of outputs of a switching system in successive time slots, including a matching method including the steps of providing a first request information in a first time slot indicating data packets at the inputs requesting transmission to the outputs of the switching system, performing a first step in the first time slot depending on the first request information to obtain a first matching information, providing a last request information in a last time slot successive to the first time slot, performing a last step in the last time slot depending on the last request information and depending on the first matching information to obtain a final matching information, and assigning the pending data packets at the number of inputs to the number of outputs based on the final matching information.

  18. Lattice Boltzmann simulation of solute transport in heterogeneous porous media with conduits to estimate macroscopic continuous time random walk model parameters

    SciTech Connect (OSTI)

    Anwar, S.; Cortis, A.; Sukop, M.

    2008-10-20

    Lattice Boltzmann models simulate solute transport in porous media traversed by conduits. Resulting solute breakthrough curves are fitted with Continuous Time Random Walk models. Porous media are simulated by damping flow inertia and, when the damping is large enough, a Darcy's Law solution instead of the Navier-Stokes solution normally provided by the lattice Boltzmann model is obtained. Anisotropic dispersion is incorporated using a direction-dependent relaxation time. Our particular interest is to simulate transport processes outside the applicability of the standard Advection-Dispersion Equation (ADE) including eddy mixing in conduits. The ADE fails to adequately fit any of these breakthrough curves.

  19. SPRNG Scalable Parallel Random Number Generator LIbrary

    Energy Science and Technology Software Center (OSTI)

    2010-03-16

    This revision corrects some errors in SPRNG 1. Users of newer SPRNG versions can obtain the corrected files and build their version with it. This version also improves the scalability of some of the application-based tests in the SPRNG test suite. It also includes an interface to a parallel Mersenne Twister, so that if users install the Mersenne Twister, then they can test this generator with the SPRNG test suite and also use some SPRNGmore » features with that generator.« less

  20. Fast generation of sparse random kernel graphs

    SciTech Connect (OSTI)

    Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

    2015-09-10

    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.

  1. Fast generation of sparse random kernel graphs

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

    2015-09-10

    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

  2. Request Number:

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

    3023307 Name: Madeleine Brown Organization: nJa Address: --- -------- -------- -- Country: Phone Number: United States Fax Number: n/a E-mail: --- -------- --------_._------ --- Reasonably Describe Records Description: Please send me a copy of the emails and records relating to the decision to allow the underage son of Bill Gates to tour Hanford in June 2010. Please also send the emails and records that justify the Department of Energy to prevent other minors from visiting B Reactor. Optional

  3. Request Number:

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

    1074438 Name: Gayle Cooper Organization: nla Address: _ Country: United States Phone Number: Fax Number: nla E-mail: . ~===--------- Reasonably Describe Records Description: Information pertaining to the Department of Energy's cost estimate for reinstating pension benefit service years to the Enterprise Company (ENCO) employees who are active plan participants in the Hanford Site Pension Plan. This cost estimate was an outcome of the DOE's Worker Town Hall Meetings held on September 17-18, 2009.

  4. Random one-of-N selector

    DOE Patents [OSTI]

    Kronberg, J.W.

    1993-04-20

    An apparatus for selecting at random one item of N items on the average comprising counter and reset elements for counting repeatedly between zero and N, a number selected by the user, a circuit for activating and deactivating the counter, a comparator to determine if the counter stopped at a count of zero, an output to indicate an item has been selected when the count is zero or not selected if the count is not zero. Randomness is provided by having the counter cycle very often while varying the relatively longer duration between activation and deactivation of the count. The passive circuit components of the activating/deactivating circuit and those of the counter are selected for the sensitivity of their response to variations in temperature and other physical characteristics of the environment so that the response time of the circuitry varies. Additionally, the items themselves, which may be people, may vary in shape or the time they press a pushbutton, so that, for example, an ultrasonic beam broken by the item or person passing through it will add to the duration of the count and thus to the randomness of the selection.

  5. Random one-of-N selector

    DOE Patents [OSTI]

    Kronberg, James W. (353 Church Rd., Beech Island, SC 29841)

    1993-01-01

    An apparatus for selecting at random one item of N items on the average comprising counter and reset elements for counting repeatedly between zero and N, a number selected by the user, a circuit for activating and deactivating the counter, a comparator to determine if the counter stopped at a count of zero, an output to indicate an item has been selected when the count is zero or not selected if the count is not zero. Randomness is provided by having the counter cycle very often while varying the relatively longer duration between activation and deactivation of the count. The passive circuit components of the activating/deactivating circuit and those of the counter are selected for the sensitivity of their response to variations in temperature and other physical characteristics of the environment so that the response time of the circuitry varies. Additionally, the items themselves, which may be people, may vary in shape or the time they press a pushbutton, so that, for example, an ultrasonic beam broken by the item or person passing through it will add to the duration of the count and thus to the randomness of the selection.

  6. Report number codes

    SciTech Connect (OSTI)

    Nelson, R.N.

    1985-05-01

    This publication lists all report number codes processed by the Office of Scientific and Technical Information. The report codes are substantially based on the American National Standards Institute, Standard Technical Report Number (STRN)-Format and Creation Z39.23-1983. The Standard Technical Report Number (STRN) provides one of the primary methods of identifying a specific technical report. The STRN consists of two parts: The report code and the sequential number. The report code identifies the issuing organization, a specific program, or a type of document. The sequential number, which is assigned in sequence by each report issuing entity, is not included in this publication. Part I of this compilation is alphabetized by report codes followed by issuing installations. Part II lists the issuing organization followed by the assigned report code(s). In both Parts I and II, the names of issuing organizations appear for the most part in the form used at the time the reports were issued. However, for some of the more prolific installations which have had name changes, all entries have been merged under the current name.

  7. Number | Open Energy Information

    Open Energy Info (EERE)

    Property:NumOfPlants Property:NumProdWells Property:NumRepWells Property:Number of Color Cameras Property:Number of Devices Deployed Property:Number of Plants included in...

  8. NSR Key Number Retrieval

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

    NSR Key Number Retrieval Pease enter key in the box Submit

  9. Compendium of Experimental Cetane Numbers

    SciTech Connect (OSTI)

    Yanowitz, J.; Ratcliff, M. A.; McCormick, R. L.; Taylor, J. D.; Murphy, M. J.

    2014-08-01

    This report is an updated version of the 2004 Compendium of Experimental Cetane Number Data and presents a compilation of measured cetane numbers for pure chemical compounds. It includes all available single compound cetane number data found in the scientific literature up until March 2014 as well as a number of unpublished values, most measured over the past decade at the National Renewable Energy Laboratory. This Compendium contains cetane values for 389 pure compounds, including 189 hydrocarbons and 201 oxygenates. More than 250 individual measurements are new to this version of the Compendium. For many compounds, numerous measurements are included, often collected by different researchers using different methods. Cetane number is a relative ranking of a fuel's autoignition characteristics for use in compression ignition engines; it is based on the amount of time between fuel injection and ignition, also known as ignition delay. The cetane number is typically measured either in a single-cylinder engine or a constant volume combustion chamber. Values in the previous Compendium derived from octane numbers have been removed, and replaced with a brief analysis of the correlation between cetane numbers and octane numbers. The discussion on the accuracy and precision of the most commonly used methods for measuring cetane has been expanded and the data has been annotated extensively to provide additional information that will help the reader judge the relative reliability of individual results.

  10. Document ID Number: RL-721

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

    ---------------------------------------------------------- Document ID Number: RL-721 REV 4 NEPA REVIEW SCREENING FORM DOE/CX-00066 I. Project Title: Nesting Bird Deterrent Study at the 241-C Tank Farm CX B3.8, "Outdoor Terrestrial Ecological and Environmental Research" II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions - e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings,

  11. Random array grid collimator

    DOE Patents [OSTI]

    Fenimore, E.E.

    1980-08-22

    A hexagonally shaped quasi-random no-two-holes touching grid collimator. The quasi-random array grid collimator eliminates contamination from small angle off-axis rays by using a no-two-holes-touching pattern which simultaneously provides for a self-supporting array increasng throughput by elimination of a substrate. The presentation invention also provides maximum throughput using hexagonally shaped holes in a hexagonal lattice pattern for diffraction limited applications. Mosaicking is also disclosed for reducing fabrication effort.

  12. Big Numbers | Jefferson Lab

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

    Big Numbers May 16, 2011 This article has some numbers in it. In principle, numbers are just language, like English or Japanese. Nevertheless, it is true that not everyone is comfortable or facile with numbers and may be turned off by too many of them. To those people, I apologize that this article pays less attention to maximizing the readership than some I do. But sometimes it's just appropriate to indulge one's self, so here goes. When we discuss the performance of some piece of equipment, we

  13. Sublinear scaling for time-dependent stochastic density functional theory

    SciTech Connect (OSTI)

    Gao, Yi; Neuhauser, Daniel; Baer, Roi; Rabani, Eran

    2015-01-21

    A stochastic approach to time-dependent density functional theory is developed for computing the absorption cross section and the random phase approximation (RPA) correlation energy. The core idea of the approach involves time-propagation of a small set of stochastic orbitals which are first projected on the occupied space and then propagated in time according to the time-dependent Kohn-Sham equations. The evolving electron density is exactly represented when the number of random orbitals is infinite, but even a small number (≈16) of such orbitals is enough to obtain meaningful results for absorption spectrum and the RPA correlation energy per electron. We implement the approach for silicon nanocrystals using real-space grids and find that the overall scaling of the algorithm is sublinear with computational time and memory.

  14. Electrokinetic transport in microchannels with random roughness

    SciTech Connect (OSTI)

    Wang, Moran [Los Alamos National Laboratory; Kang, Qinjun [Los Alamos National Laboratory

    2008-01-01

    We present a numerical framework to model the electrokinetic transport in microchannels with random roughness. The three-dimensional microstructure of the rough channel is generated by a random generation-growth method with three statistical parameters to control the number density, the total volume fraction, and the anisotropy characteristics of roughness elements. The governing equations for the electrokinetic transport are solved by a high-efficiency lattice Poisson?Boltzmann method in complex geometries. The effects from the geometric characteristics of roughness on the electrokinetic transport in microchannels are therefore modeled and analyzed. For a given total roughness volume fraction, a higher number density leads to a lower fluctuation because of the random factors. The electroosmotic flow rate increases with the roughness number density nearly logarithmically for a given volume fraction of roughness but decreases with the volume fraction for a given roughness number density. When both the volume fraction and the number density of roughness are given, the electroosmotic flow rate is enhanced by the increase of the characteristic length along the external electric field direction but is reduced by that in the direction across the channel. For a given microstructure of the rough microchannel, the electroosmotic flow rate decreases with the Debye length. It is found that the shape resistance of roughness is responsible for the flow rate reduction in the rough channel compared to the smooth channel even for very thin double layers, and hence plays an important role in microchannel electroosmotic flows.

  15. Document Details Document Number

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

    Document Details Document Number Date of Document Document Title/Description [Links below to each document] D195066340 Not listed. N/A REVISIONS IN STRATIGRAPHIC NOMENCLATURE OF COLUMBIA RIVER BASALT GROUP D196000240 Not listed. N/A EPA DENIAL OF LINER LEACHATE COLLECTION SYSTEM REQUIREMENTS D196005916 Not listed. N/A LATE CENOZOIC STRATIGRAPHY AND TECTONIC EVOLUTION WITHIN SUBSIDING BASIN SOUTH CENTRAL WASHINGTON D196025993 RHO-BWI-ST-14 N/A SUPRABASALT SEDIMENTS OF COLD CREEK SYNCLINE AREA

  16. Recombination of polynucleotide sequences using random or defined primers

    DOE Patents [OSTI]

    Arnold, Frances H. (Pasadena, CA); Shao, Zhixin (Pasadena, CA); Affholter, Joseph A. (Midland, MI); Zhao, Huimin H (San Diego, CA); Giver, Lorraine J. (Sunnyvale, CA)

    2000-01-01

    A method for in vitro mutagenesis and recombination of polynucleotide sequences based on polymerase-catalyzed extension of primer oligonucleotides is disclosed. The method involves priming template polynucleotide(s) with random-sequences or defined-sequence primers to generate a pool of short DNA fragments with a low level of point mutations. The DNA fragments are subjected to denaturization followed by annealing and further enzyme-catalyzed DNA polymerization. This procedure is repeated a sufficient number of times to produce full-length genes which comprise mutants of the original template polynucleotides. These genes can be further amplified by the polymerase chain reaction and cloned into a vector for expression of the encoded proteins.

  17. Recombination of polynucleotide sequences using random or defined primers

    DOE Patents [OSTI]

    Arnold, Frances H. (Pasadena, CA); Shao, Zhixin (Pasadena, CA); Affholter, Joseph A. (Midland, MI); Zhao, Huimin (Pasadena, CA); Giver, Lorraine J. (Pasadena, CA)

    2001-01-01

    A method for in vitro mutagenesis and recombination of polynucleotide sequences based on polymerase-catalyzed extension of primer oligonucleotides is disclosed. The method involves priming template polynucleotide(s) with random-sequences or defined-sequence primers to generate a pool of short DNA fragments with a low level of point mutations. The DNA fragments are subjected to denaturization followed by annealing and further enzyme-catalyzed DNA polymerization. This procedure is repeated a sufficient number of times to produce full-length genes which comprise mutants of the original template polynucleotides. These genes can be further amplified by the polymerase chain reaction and cloned into a vector for expression of the encoded proteins.

  18. DOE/ID-Number

    Office of Environmental Management (EM)

    Public Preferences Related to Consent-Based Siting of Radioactive Waste Management Facilities for Storage and Disposal: Analyzing Variations over Time, Events, and Program Designs Prepared for US Department of Energy Nuclear Fuel Storage and Transportation Planning Project Hank C. Jenkins-Smith Carol L. Silva Kerry G. Herron Kuhika G. Ripberger Matthew Nowlin Joseph Ripberger Center for Risk and Crisis Management, University of Oklahoma Evaristo "Tito" Bonano Rob P. Rechard Sandia

  19. Organization of growing random networks

    SciTech Connect (OSTI)

    Krapivsky, P. L.; Redner, S.

    2001-06-01

    The organizational development of growing random networks is investigated. These growing networks are built by adding nodes successively, and linking each to an earlier node of degree k with an attachment probability A{sub k}. When A{sub k} grows more slowly than linearly with k, the number of nodes with k links, N{sub k}(t), decays faster than a power law in k, while for A{sub k} growing faster than linearly in k, a single node emerges which connects to nearly all other nodes. When A{sub k} is asymptotically linear, N{sub k}(t){similar_to}tk{sup {minus}{nu}}, with {nu} dependent on details of the attachment probability, but in the range 2{lt}{nu}{lt}{infinity}. The combined age and degree distribution of nodes shows that old nodes typically have a large degree. There is also a significant correlation in the degrees of neighboring nodes, so that nodes of similar degree are more likely to be connected. The size distributions of the in and out components of the network with respect to a given node{emdash}namely, its {open_quotes}descendants{close_quotes} and {open_quotes}ancestors{close_quotes}{emdash}are also determined. The in component exhibits a robust s{sup {minus}2} power-law tail, where s is the component size. The out component has a typical size of order lnt, and it provides basic insights into the genealogy of the network.

  20. Texas Natural Gas Number of Residential Consumers (Number of...

    Gasoline and Diesel Fuel Update (EIA)

    Residential Consumers (Number of Elements) Texas Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  1. Texas Natural Gas Number of Commercial Consumers (Number of Elements...

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

    Commercial Consumers (Number of Elements) Texas Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  2. Connecticut Natural Gas Number of Commercial Consumers (Number...

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

    Commercial Consumers (Number of Elements) Connecticut Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  3. Connecticut Natural Gas Number of Residential Consumers (Number...

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

    Residential Consumers (Number of Elements) Connecticut Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6...

  4. North Carolina Natural Gas Number of Commercial Consumers (Number...

    Gasoline and Diesel Fuel Update (EIA)

    Commercial Consumers (Number of Elements) North Carolina Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6...

  5. New York Natural Gas Number of Commercial Consumers (Number of...

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

    Commercial Consumers (Number of Elements) New York Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  6. New York Natural Gas Number of Residential Consumers (Number...

    Gasoline and Diesel Fuel Update (EIA)

    Residential Consumers (Number of Elements) New York Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  7. Indiana Natural Gas Number of Industrial Consumers (Number of...

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

    Industrial Consumers (Number of Elements) Indiana Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  8. Study Reveals Fuel Injection Timing Impact on Particle Number...

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

    expected to account for 60% of the U.S. market by 2016, and the technology offers fuel economy and CO 2 reduction benefits, it can have higher particulate matter (PM) mass and...

  9. Combinatorial theory of the semiclassical evaluation of transport moments. I. Equivalence with the random matrix approach

    SciTech Connect (OSTI)

    Berkolaiko, G.; Kuipers, J.

    2013-11-15

    To study electronic transport through chaotic quantum dots, there are two main theoretical approaches. One involves substituting the quantum system with a random scattering matrix and performing appropriate ensemble averaging. The other treats the transport in the semiclassical approximation and studies correlations among sets of classical trajectories. There are established evaluation procedures within the semiclassical evaluation that, for several linear and nonlinear transport moments to which they were applied, have always resulted in the agreement with random matrix predictions. We prove that this agreement is universal: any semiclassical evaluation within the accepted procedures is equivalent to the evaluation within random matrix theory. The equivalence is shown by developing a combinatorial interpretation of the trajectory sets as ribbon graphs (maps) with certain properties and exhibiting systematic cancellations among their contributions. Remaining trajectory sets can be identified with primitive (palindromic) factorisations whose number gives the coefficients in the corresponding expansion of the moments of random matrices. The equivalence is proved for systems with and without time reversal symmetry.

  10. Compendium of Experimental Cetane Number Data

    SciTech Connect (OSTI)

    Murphy, M. J.; Taylor, J. D.; McCormick, R. L.

    2004-09-01

    In this report, we present a compilation of reported cetane numbers for pure chemical compounds. The compiled database contains cetane values for 299 pure compounds, including 156 hydrocarbons and 143 oxygenates. Cetane number is a relative ranking of fuels based on the amount of time between fuel injection and ignition. The cetane number is typically measured either in a combustion bomb or in a single-cylinder research engine. This report includes cetane values from several different measurement techniques - each of which has associated uncertainties. Additionally, many of the reported values are determined by measuring blending cetane numbers, which introduces significant error. In many cases, the measurement technique is not reported nor is there any discussion about the purity of the compounds. Nonetheless, the data in this report represent the best pure compound cetane number values available from the literature as of August 2004.

  11. Alaska Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Alaska Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 10 11 8 1990's 8 8 10 11 11 9 202 7 7 9 2000's 9 8 9 9 10 12 11 11 6 3 2010's 3 5 3 3 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016 Referring Pages: Number of Natural Gas

  12. Hawaii Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Hawaii Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 27 26 29 2000's 28 28 29 29 29 28 26 27 27 25 2010's 24 24 22 22 23 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016 Referring Pages: Number of Natural Gas Industrial

  13. Total Number of Operable Refineries

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

    Data Series: Total Number of Operable Refineries Number of Operating Refineries Number of Idle Refineries Atmospheric Crude Oil Distillation Operable Capacity (B/CD) Atmospheric Crude Oil Distillation Operating Capacity (B/CD) Atmospheric Crude Oil Distillation Idle Capacity (B/CD) Atmospheric Crude Oil Distillation Operable Capacity (B/SD) Atmospheric Crude Oil Distillation Operating Capacity (B/SD) Atmospheric Crude Oil Distillation Idle Capacity (B/SD) Vacuum Distillation Downstream Charge

  14. Arizona Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Arizona Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 358 344 354 1990's 526 532 532 526 519 530 534 480 514 555 2000's 526 504 488 450 414 425 439 395 383 390 2010's 368 371 379 383 386 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  15. Montana Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Montana Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 435 435 428 1990's 457 452 459 462 453 463 466 462 454 397 2000's 71 73 439 412 593 716 711 693 693 396 2010's 384 381 372 372 369 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  16. Nevada Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Nevada Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 93 98 100 1990's 100 113 114 117 119 120 121 93 93 109 2000's 90 90 96 97 179 192 207 220 189 192 2010's 184 177 177 195 218 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016

  17. New Hampshire Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) New Hampshire Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 153 295 376 1990's 364 361 344 334 324 332 367 385 389 417 2000's 432 331 437 550 305 397 421 578 5,298 155 2010's 306 362 466 403 326 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016

  18. North Dakota Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) North Dakota Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 138 148 151 1990's 165 170 171 174 186 189 206 216 404 226 2000's 192 203 223 234 241 239 241 253 271 279 2010's 307 259 260 266 269 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016

  19. Rhode Island Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) Rhode Island Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,158 1,152 1,122 1990's 1,135 1,107 1,096 1,066 1,064 359 363 336 325 302 2000's 317 283 54 236 223 223 245 256 243 260 2010's 249 245 248 271 266 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  20. South Dakota Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) South Dakota Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 261 267 270 1990's 275 283 319 355 381 396 444 481 464 445 2000's 416 402 533 526 475 542 528 548 598 598 2010's 580 556 574 566 575 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016

  1. Utah Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Utah Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 551 627 550 1990's 1,508 631 783 345 252 713 923 3,379 3,597 3,625 2000's 3,576 3,535 949 924 312 191 274 278 313 293 2010's 293 286 302 323 328 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release

  2. Vermont Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Vermont Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 22 21 14 1990's 15 13 18 20 24 23 27 30 36 37 2000's 38 36 38 41 43 41 35 37 35 36 2010's 38 36 38 13 13 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016 Referring Pages:

  3. Delaware Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Delaware Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 241 233 235 1990's 240 243 248 249 252 253 250 265 257 264 2000's 297 316 182 184 186 179 170 185 165 112 2010's 114 129 134 138 141 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  4. Florida Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Florida Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 575 552 460 1990's 452 377 388 433 481 515 517 561 574 573 2000's 520 518 451 421 398 432 475 467 449 607 2010's 581 630 507 528 520 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  5. Idaho Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Idaho Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 219 132 64 1990's 62 65 66 75 144 167 183 189 203 200 2000's 217 198 194 191 196 195 192 188 199 187 2010's 184 178 179 183 189 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016

  6. Maine Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Maine Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 73 73 74 1990's 80 81 80 66 89 74 87 81 110 108 2000's 178 233 66 65 69 69 73 76 82 85 2010's 94 102 108 120 126 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016 Referring

  7. West Virginia Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) West Virginia Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 463 208 211 1990's 182 198 159 197 191 192 182 173 217 147 2000's 207 213 184 142 137 145 155 114 109 101 2010's 102 94 97 95 92 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next

  8. Wyoming Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Wyoming Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 190 200 230 1990's 284 228 244 194 135 126 170 194 317 314 2000's 308 295 877 179 121 127 133 133 155 130 2010's 120 123 127 132 131 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  9. Battling bird flu by the numbers

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

    Battling bird flu by the numbers Battling bird flu by the numbers Lab theorists have developed a mathematical tool that could help health experts and crisis managers determine in real time whether an emerging infectious disease such as avian influenza H5N1 is poised to spread globally. May 27, 2008 Los Alamos National Laboratory sits on top of a once-remote mesa in northern New Mexico with the Jemez mountains as a backdrop to research and innovation covering multi-disciplines from bioscience,

  10. Departmental Business Instrument Numbering System

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    2000-12-05

    To prescribe procedures for assigning identifying numbers to all Department of Energy (DOE), including the National Nuclear Security Administration, business instruments. Cancels DOE 1331.2B. Canceled by DOE O 540.1A.

  11. Departmental Business Instrument Numbering System

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    2005-01-27

    The Order prescribes the procedures for assigning identifying numbers to all Department of Energy (DOE) and National Nuclear Security Administration (NNSA) business instruments. Cancels DOE O 540.1. Canceled by DOE O 540.1B.

  12. Alabama Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Alabama Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 53 54,306 55,400 56,822 1990's 56,903 57,265 58,068 57,827 60,320 60,902 62,064 65,919 76,467 64,185 2000's 66,193 65,794 65,788 65,297 65,223 65,294 66,337 65,879 65,313 67,674 2010's 68,163 67,696 67,252 67,136 67,806 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  13. Alabama Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Alabama Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2 2,313 2,293 2,380 1990's 2,431 2,523 2,509 2,458 2,477 2,491 2,512 2,496 2,464 2,620 2000's 2,792 2,781 2,730 2,743 2,799 2,787 2,735 2,704 2,757 3,057 2010's 3,039 2,988 3,045 3,143 3,244 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  14. Alabama Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Alabama Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 656 662,217 668,432 683,528 1990's 686,149 700,195 711,043 730,114 744,394 751,890 766,322 781,711 788,464 775,311 2000's 805,689 807,770 806,389 809,754 806,660 809,454 808,801 796,476 792,236 785,005 2010's 778,985 772,892 767,396 765,957 769,418 - = No Data Reported; -- = Not Applicable; NA = Not

  15. Alaska Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Alaska Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 11 11,484 11,649 11,806 1990's 11,921 12,071 12,204 12,359 12,475 12,584 12,732 12,945 13,176 13,409 2000's 13,711 14,002 14,342 14,502 13,999 14,120 14,384 13,408 12,764 13,215 2010's 12,998 13,027 13,133 13,246 13,399 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  16. Alaska Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Alaska Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 66 67,648 68,612 69,540 1990's 70,808 72,565 74,268 75,842 77,670 79,474 81,348 83,596 86,243 88,924 2000's 91,297 93,896 97,077 100,404 104,360 108,401 112,269 115,500 119,039 120,124 2010's 121,166 121,736 122,983 124,411 126,416 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  17. Arizona Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Arizona Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 46 46,702 46,636 46,776 1990's 47,292 53,982 47,781 47,678 48,568 49,145 49,693 50,115 51,712 53,022 2000's 54,056 54,724 56,260 56,082 56,186 56,572 57,091 57,169 57,586 57,191 2010's 56,676 56,547 56,532 56,585 56,649 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  18. Arizona Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Arizona Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 545 567,962 564,195 572,461 1990's 586,866 642,659 604,899 610,337 635,335 661,192 689,597 724,911 764,167 802,469 2000's 846,016 884,789 925,927 957,442 993,885 1,042,662 1,088,574 1,119,266 1,128,264 1,130,047 2010's 1,138,448 1,146,286 1,157,688 1,172,003 1,186,794 - = No Data Reported; -- = Not

  19. Arkansas Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Arkansas Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 60 60,355 61,630 61,848 1990's 61,530 61,731 62,221 62,952 63,821 65,490 67,293 68,413 69,974 71,389 2000's 72,933 71,875 71,530 71,016 70,655 69,990 69,475 69,495 69,144 69,043 2010's 67,987 67,815 68,765 68,791 69,011 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  20. Arkansas Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Arkansas Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1 1,410 1,151 1,412 1990's 1,396 1,367 1,319 1,364 1,417 1,366 1,488 1,336 1,300 1,393 2000's 1,414 1,122 1,407 1,269 1,223 1,120 1,120 1,055 1,104 1,025 2010's 1,079 1,133 990 1,020 1,009 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  1. Arkansas Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Arkansas Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 475 480,839 485,112 491,110 1990's 488,850 495,148 504,722 513,466 521,176 531,182 539,952 544,460 550,017 554,121 2000's 560,055 552,716 553,192 553,211 554,844 555,861 555,905 557,966 556,746 557,355 2010's 549,970 551,795 549,959 549,764 549,034 - = No Data Reported; -- = Not Applicable; NA =

  2. Massachusetts Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) Massachusetts Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 84,636 93,005 92,252 1990's 85,775 88,746 85,873 102,187 92,744 104,453 105,889 107,926 108,832 113,177 2000's 117,993 120,984 122,447 123,006 125,107 120,167 126,713 128,965 242,693 153,826 2010's 144,487 138,225 142,825 144,246 139,556 - = No Data Reported; -- = Not Applicable;

  3. Massachusetts Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) Massachusetts Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 5,626 7,199 13,057 1990's 6,539 5,006 8,723 7,283 8,019 10,447 10,952 11,058 11,245 8,027 2000's 8,794 9,750 9,090 11,272 10,949 12,019 12,456 12,678 36,928 19,208 2010's 12,751 10,721 10,840 11,063 10,946 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  4. Massachusetts Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) Massachusetts Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,082,777 1,100,635 1,114,920 1990's 1,118,429 1,127,536 1,137,911 1,155,443 1,179,869 1,180,860 1,188,317 1,204,494 1,212,486 1,232,887 2000's 1,278,781 1,283,008 1,295,952 1,324,715 1,306,142 1,297,508 1,348,848 1,361,470 1,236,480 1,370,353 2010's 1,389,592 1,408,314 1,447,947

  5. Michigan Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Michigan Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 178,469 185,961 191,474 1990's 195,766 198,890 201,561 204,453 207,629 211,817 214,843 222,726 224,506 227,159 2000's 230,558 225,109 247,818 246,123 246,991 253,415 254,923 253,139 252,382 252,017 2010's 249,309 249,456 249,994 250,994 253,127 - = No Data Reported; -- = Not Applicable; NA = Not

  6. Michigan Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Michigan Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 10,885 11,117 11,452 1990's 11,500 11,446 11,460 11,425 11,308 11,454 11,848 12,233 11,888 14,527 2000's 11,384 11,210 10,468 10,378 10,088 10,049 9,885 9,728 10,563 18,186 2010's 9,332 9,088 8,833 8,497 8,156 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  7. Michigan Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Michigan Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,452,554 2,491,149 2,531,304 1990's 2,573,570 2,609,561 2,640,579 2,677,085 2,717,683 2,767,190 2,812,876 2,859,483 2,903,698 2,949,628 2000's 2,999,737 3,011,205 3,110,743 3,140,021 3,161,370 3,187,583 3,193,920 3,188,152 3,172,623 3,169,026 2010's 3,152,468 3,153,895 3,161,033 3,180,349

  8. Minnesota Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Minnesota Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 88,789 90,256 92,916 1990's 95,474 97,388 99,707 93,062 102,857 103,874 105,531 108,686 110,986 114,127 2000's 116,529 119,007 121,751 123,123 125,133 126,310 129,149 128,367 130,847 131,801 2010's 132,163 132,938 134,394 135,557 136,382 - = No Data Reported; -- = Not Applicable; NA = Not Available;

  9. Minnesota Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Minnesota Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,585 2,670 2,638 1990's 2,574 2,486 2,515 2,477 2,592 2,531 2,564 2,233 2,188 2,267 2000's 2,025 1,996 2,029 2,074 2,040 1,432 1,257 1,146 1,131 2,039 2010's 2,106 1,770 1,793 1,870 1,878 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  10. Minnesota Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Minnesota Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 872,148 894,380 911,001 1990's 946,107 970,941 998,201 1,074,631 1,049,263 1,080,009 1,103,709 1,134,019 1,161,423 1,190,190 2000's 1,222,397 1,249,748 1,282,751 1,308,143 1,338,061 1,364,237 1,401,362 1,401,623 1,413,162 1,423,703 2010's 1,429,681 1,436,063 1,445,824 1,459,134 1,472,663 - = No

  11. Mississippi Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Mississippi Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 43,362 44,170 44,253 1990's 43,184 43,693 44,313 45,310 43,803 45,444 46,029 47,311 45,345 47,620 2000's 50,913 51,109 50,468 50,928 54,027 54,936 55,741 56,155 55,291 50,713 2010's 50,537 50,636 50,689 50,153 50,238 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  12. Mississippi Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Mississippi Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,312 1,263 1,282 1990's 1,317 1,314 1,327 1,324 1,313 1,298 1,241 1,199 1,165 1,246 2000's 1,199 1,214 1,083 1,161 996 1,205 1,181 1,346 1,132 1,141 2010's 980 982 936 933 943 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  13. Mississippi Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) Mississippi Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 370,094 372,238 376,353 1990's 382,251 386,264 392,155 398,472 405,312 415,123 418,442 423,397 415,673 426,352 2000's 434,501 438,069 435,146 438,861 445,212 445,856 437,669 445,043 443,025 437,715 2010's 436,840 442,479 442,840 445,589 444,423 - = No Data Reported; -- = Not

  14. Missouri Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Missouri Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 96,711 97,939 99,721 1990's 105,164 117,675 125,174 125,571 132,378 130,318 133,445 135,553 135,417 133,464 2000's 133,969 135,968 137,924 140,057 141,258 142,148 143,632 142,965 141,529 140,633 2010's 138,670 138,214 144,906 142,495 143,024 - = No Data Reported; -- = Not Applicable; NA = Not

  15. Missouri Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Missouri Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,832 2,880 3,063 1990's 3,140 3,096 2,989 3,040 3,115 3,033 3,408 3,097 3,151 3,152 2000's 3,094 3,085 2,935 3,115 3,600 3,545 3,548 3,511 3,514 3,573 2010's 3,541 3,307 3,692 3,538 3,497 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  16. Missouri Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Missouri Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,180,546 1,194,985 1,208,523 1990's 1,213,305 1,211,342 1,220,203 1,225,921 1,281,007 1,259,102 1,275,465 1,293,032 1,307,563 1,311,865 2000's 1,324,282 1,326,160 1,340,726 1,343,614 1,346,773 1,348,743 1,353,892 1,354,173 1,352,015 1,348,781 2010's 1,348,549 1,342,920 1,389,910 1,357,740

  17. Montana Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Montana Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 21,382 22,246 22,219 1990's 23,331 23,185 23,610 24,373 25,349 26,329 26,374 27,457 28,065 28,424 2000's 29,215 29,429 30,250 30,814 31,357 31,304 31,817 32,472 33,008 33,731 2010's 34,002 34,305 34,504 34,909 35,205 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  18. Montana Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Montana Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 167,883 171,785 171,156 1990's 174,384 177,726 182,641 188,879 194,357 203,435 205,199 209,806 218,851 222,114 2000's 224,784 226,171 229,015 232,839 236,511 240,554 245,883 247,035 253,122 255,472 2010's 257,322 259,046 259,957 262,122 265,849 - = No Data Reported; -- = Not Applicable; NA = Not

  19. Nebraska Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Nebraska Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 60,707 61,365 60,377 1990's 60,405 60,947 61,319 60,599 62,045 61,275 61,117 51,661 63,819 53,943 2000's 55,194 55,692 56,560 55,999 57,087 57,389 56,548 55,761 58,160 56,454 2010's 56,246 56,553 56,608 58,005 57,191 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  20. Nebraska Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Nebraska Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 675 684 702 1990's 712 718 696 718 766 2,432 2,234 11,553 10,673 10,342 2000's 10,161 10,504 9,156 9,022 8,463 7,973 7,697 7,668 11,627 7,863 2010's 7,912 7,955 8,160 8,495 8,791 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  1. Nevada Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Nevada Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 18,294 18,921 19,924 1990's 20,694 22,124 22,799 23,207 24,521 25,593 26,613 27,629 29,030 30,521 2000's 31,789 32,782 33,877 34,590 35,792 37,093 38,546 40,128 41,098 41,303 2010's 40,801 40,944 41,192 41,710 42,338 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  2. Nevada Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Nevada Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 213,422 219,981 236,237 1990's 256,119 283,307 295,714 305,099 336,353 364,112 393,783 426,221 458,737 490,029 2000's 520,233 550,850 580,319 610,756 648,551 688,058 726,772 750,570 758,315 760,391 2010's 764,435 772,880 782,759 794,150 808,970 - = No Data Reported; -- = Not Applicable; NA = Not

  3. New Hampshire Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) New Hampshire Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 8,831 9,159 10,237 1990's 10,521 11,088 11,383 11,726 12,240 12,450 12,755 13,225 13,512 13,932 2000's 14,219 15,068 15,130 15,047 15,429 16,266 16,139 16,150 41,332 16,937 2010's 16,645 17,186 17,758 17,298 17,421 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  4. New Hampshire Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) New Hampshire Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 60,078 61,969 64,059 1990's 65,310 67,991 69,356 70,938 72,656 74,232 75,175 77,092 78,786 80,958 2000's 82,813 84,760 87,147 88,170 88,600 94,473 94,600 94,963 67,945 96,924 2010's 95,361 97,400 99,738 98,715 99,146 - = No Data Reported; -- = Not Applicable; NA = Not Available;

  5. North Carolina Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) North Carolina Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3,236 3,196 3,381 1990's 2,802 3,506 3,119 2,664 3,401 3,652 3,973 5,375 6,228 5,672 2000's 5,288 2,962 3,200 3,101 3,021 2,891 2,701 2,991 2,984 2,384 2010's 2,457 2,468 2,525 2,567 2,596 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  6. North Carolina Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) North Carolina Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 435,826 472,928 492,821 1990's 520,140 539,321 575,096 607,388 652,307 678,147 699,159 740,013 777,805 815,908 2000's 858,004 891,227 905,816 953,732 948,283 992,906 1,022,430 1,063,871 1,095,362 1,102,001 2010's 1,115,532 1,128,963 1,142,947 1,161,398 1,183,152 - = No Data

  7. North Dakota Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) North Dakota Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 11,905 12,104 12,454 1990's 12,742 12,082 12,353 12,650 12,944 13,399 13,789 14,099 14,422 15,050 2000's 15,531 15,740 16,093 16,202 16,443 16,518 16,848 17,013 17,284 17,632 2010's 17,823 18,421 19,089 19,855 20,687 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  8. North Dakota Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) North Dakota Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 83,517 84,059 84,643 1990's 85,646 87,880 89,522 91,237 93,398 95,818 97,761 98,326 101,930 104,051 2000's 105,660 106,758 108,716 110,048 112,206 114,152 116,615 118,100 120,056 122,065 2010's 123,585 125,392 130,044 133,975 137,972 - = No Data Reported; -- = Not Applicable; NA =

  9. Ohio Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Ohio Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 213,601 219,257 225,347 1990's 233,075 236,519 237,861 240,684 245,190 250,223 259,663 254,991 258,076 266,102 2000's 269,561 269,327 271,160 271,203 272,445 277,767 270,552 272,555 272,899 270,596 2010's 268,346 268,647 267,793 269,081 269,758 - = No Data Reported; -- = Not Applicable; NA = Not

  10. Ohio Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Ohio Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 7,929 8,163 8,356 1990's 8,301 8,479 8,573 8,678 8,655 8,650 8,672 7,779 8,112 8,136 2000's 8,267 8,515 8,111 8,098 7,899 8,328 6,929 6,858 6,806 6,712 2010's 6,571 6,482 6,381 6,554 6,526 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  11. Ohio Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Ohio Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,648,972 2,678,838 2,714,839 1990's 2,766,912 2,801,716 2,826,713 2,867,959 2,921,536 2,967,375 2,994,891 3,041,948 3,050,960 3,111,108 2000's 3,178,840 3,195,584 3,208,466 3,225,908 3,250,068 3,272,307 3,263,062 3,273,791 3,262,716 3,253,184 2010's 3,240,619 3,236,160 3,244,274 3,271,074 3,283,869 -

  12. Oklahoma Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Oklahoma Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 87,824 86,666 86,172 1990's 85,790 86,744 87,120 88,181 87,494 88,358 89,852 90,284 89,711 80,986 2000's 80,558 79,045 80,029 79,733 79,512 78,726 78,745 93,991 94,247 94,314 2010's 92,430 93,903 94,537 95,385 96,004 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  13. Oklahoma Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Oklahoma Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,772 2,689 2,877 1990's 2,889 2,840 2,859 2,912 2,853 2,845 2,843 2,531 3,295 3,040 2000's 2,821 3,403 3,438 3,367 3,283 2,855 2,811 2,822 2,920 2,618 2010's 2,731 2,733 2,872 2,958 3,063 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  14. Oklahoma Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Oklahoma Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 809,171 805,107 806,875 1990's 814,296 824,172 832,677 842,130 845,448 856,604 866,531 872,454 877,236 867,922 2000's 859,951 868,314 875,338 876,420 875,271 880,403 879,589 920,616 923,650 924,745 2010's 914,869 922,240 927,346 931,981 937,237 - = No Data Reported; -- = Not Applicable; NA = Not

  15. Oregon Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Oregon Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 40,967 41,998 43,997 1990's 47,175 55,374 50,251 51,910 53,700 55,409 57,613 60,419 63,085 65,034 2000's 66,893 68,098 69,150 74,515 71,762 73,520 74,683 80,998 76,868 76,893 2010's 77,370 77,822 78,237 79,276 80,480 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  16. Oregon Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Oregon Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 676 1,034 738 1990's 699 787 740 696 765 791 799 704 695 718 2000's 717 821 842 926 907 1,118 1,060 1,136 1,075 1,051 2010's 1,053 1,066 1,076 1,085 1,099 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016

  17. Oregon Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Oregon Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 280,670 288,066 302,156 1990's 326,177 376,166 354,256 371,151 391,845 411,465 433,638 456,960 477,796 502,000 2000's 523,952 542,799 563,744 625,398 595,495 626,685 647,635 664,455 674,421 675,582 2010's 682,737 688,681 693,507 700,211 707,010 - = No Data Reported; -- = Not Applicable; NA = Not

  18. Pennsylvania Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) Pennsylvania Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 166,901 172,615 178,545 1990's 186,772 191,103 193,863 198,299 206,812 209,245 214,340 215,057 216,519 223,732 2000's 228,037 225,911 226,957 227,708 231,051 233,132 231,540 234,597 233,462 233,334 2010's 233,751 233,588 235,049 237,922 239,681 - = No Data Reported; -- = Not

  19. Pennsylvania Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) Pennsylvania Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 6,089 6,070 6,023 1990's 6,238 6,344 6,496 6,407 6,388 6,328 6,441 6,492 6,736 7,080 2000's 6,330 6,159 5,880 5,577 5,726 5,577 5,241 4,868 4,772 4,745 2010's 4,624 5,007 5,066 5,024 5,084 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  20. Pennsylvania Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) Pennsylvania Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,237,877 2,271,801 2,291,242 1990's 2,311,795 2,333,377 2,363,575 2,386,249 2,393,053 2,413,715 2,431,909 2,452,524 2,493,639 2,486,704 2000's 2,519,794 2,542,724 2,559,024 2,572,584 2,591,458 2,600,574 2,605,782 2,620,755 2,631,340 2,635,886 2010's 2,646,211 2,667,392 2,678,547

  1. Rhode Island Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) Rhode Island Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 15,128 16,096 16,924 1990's 17,765 18,430 18,607 21,178 21,208 21,472 21,664 21,862 22,136 22,254 2000's 22,592 22,815 23,364 23,270 22,994 23,082 23,150 23,007 23,010 22,988 2010's 23,049 23,177 23,359 23,742 23,934 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  2. Rhode Island Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) Rhode Island Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 180,656 185,861 190,796 1990's 195,100 196,438 197,926 198,563 200,959 202,947 204,259 212,777 208,208 211,097 2000's 214,474 216,781 219,769 221,141 223,669 224,320 225,027 223,589 224,103 224,846 2010's 225,204 225,828 228,487 231,763 233,786 - = No Data Reported; -- = Not

  3. South Carolina Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) South Carolina Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 35,414 37,075 38,856 1990's 39,904 39,999 40,968 42,191 45,487 47,293 48,650 50,817 52,237 53,436 2000's 54,794 55,257 55,608 55,909 56,049 56,974 57,452 57,544 56,317 55,850 2010's 55,853 55,846 55,908 55,997 56,172 - = No Data Reported; -- = Not Applicable; NA = Not Available; W

  4. South Carolina Natural Gas Number of Industrial Consumers (Number of

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

    Elements) Industrial Consumers (Number of Elements) South Carolina Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,256 1,273 1,307 1990's 1,384 1,400 1,568 1,625 1,928 1,802 1,759 1,764 1,728 1,768 2000's 1,715 1,702 1,563 1,574 1,528 1,535 1,528 1,472 1,426 1,358 2010's 1,325 1,329 1,435 1,452 1,426 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  5. South Carolina Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) South Carolina Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 302,321 313,831 327,527 1990's 339,486 344,763 357,818 370,411 416,773 412,259 426,088 443,093 460,141 473,799 2000's 489,340 501,161 508,686 516,362 527,008 541,523 554,953 570,213 561,196 565,774 2010's 570,797 576,594 583,633 593,286 604,743 - = No Data Reported; -- = Not

  6. South Dakota Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) South Dakota Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 12,480 12,438 12,771 1990's 13,443 13,692 14,133 16,523 15,539 16,285 16,880 17,432 17,972 18,453 2000's 19,100 19,378 19,794 20,070 20,457 20,771 21,149 21,502 21,819 22,071 2010's 22,267 22,570 22,955 23,214 23,591 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  7. South Dakota Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) South Dakota Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 101,468 102,084 103,538 1990's 105,436 107,846 110,291 128,029 119,544 124,152 127,269 130,307 133,095 136,789 2000's 142,075 144,310 147,356 150,725 148,105 157,457 160,481 163,458 165,694 168,096 2010's 169,838 170,877 173,856 176,204 179,042 - = No Data Reported; -- = Not

  8. Tennessee Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Tennessee Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 77,104 81,159 84,040 1990's 88,753 89,863 91,999 94,860 97,943 101,561 103,867 105,925 109,772 112,978 2000's 115,691 118,561 120,130 131,916 125,042 124,755 126,970 126,324 128,007 127,704 2010's 127,914 128,969 130,139 131,091 131,001 - = No Data Reported; -- = Not Applicable; NA = Not Available;

  9. Tennessee Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Tennessee Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,206 2,151 2,555 1990's 2,361 2,369 2,425 2,512 2,440 2,393 2,306 2,382 5,149 2,159 2000's 2,386 2,704 2,657 2,755 2,738 2,498 2,545 2,656 2,650 2,717 2010's 2,702 2,729 2,679 2,581 2,595 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  10. Tennessee Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Tennessee Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 534,882 565,856 599,042 1990's 627,031 661,105 696,140 733,363 768,421 804,724 841,232 867,793 905,757 937,896 2000's 969,537 993,363 1,009,225 1,022,628 1,037,429 1,049,307 1,063,328 1,071,756 1,084,102 1,083,573 2010's 1,085,387 1,089,009 1,084,726 1,094,122 1,106,681 - = No Data Reported; -- =

  11. Texas Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Texas Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 4,852 4,427 13,383 1990's 13,659 13,770 5,481 5,823 5,222 9,043 8,796 5,339 5,318 5,655 2000's 11,613 10,047 9,143 9,015 9,359 9,136 8,664 11,063 5,568 8,581 2010's 8,779 8,713 8,953 8,525 8,406 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  12. Utah Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Utah Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 31,329 32,637 32,966 1990's 34,697 35,627 36,145 37,816 39,183 40,101 40,107 40,689 42,054 43,861 2000's 47,201 47,477 50,202 51,063 51,503 55,174 55,821 57,741 59,502 60,781 2010's 61,976 62,885 63,383 64,114 65,134 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  13. Utah Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Utah Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 414,020 418,569 432,377 1990's 453,023 455,649 467,664 484,438 503,583 523,622 562,343 567,786 588,364 609,603 2000's 641,111 657,728 660,677 678,833 701,255 743,761 754,554 778,644 794,880 810,442 2010's 821,525 830,219 840,687 854,389 869,052 - = No Data Reported; -- = Not Applicable; NA = Not

  14. Vermont Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Vermont Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,447 2,698 2,768 1990's 2,949 3,154 3,198 3,314 3,512 3,649 3,790 3,928 4,034 4,219 2000's 4,316 4,416 4,516 4,602 4,684 4,781 4,861 4,925 4,980 5,085 2010's 5,137 5,256 5,535 5,441 5,589 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  15. Vermont Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Vermont Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 15,553 16,616 16,920 1990's 18,300 19,879 20,468 21,553 22,546 23,523 24,383 25,539 26,664 27,931 2000's 28,532 29,463 30,108 30,856 31,971 33,015 34,081 34,937 35,929 37,242 2010's 38,047 38,839 39,917 41,152 42,231 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  16. Virginia Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Virginia Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 54,071 54,892 61,012 1990's 63,751 67,997 69,629 70,161 72,188 74,690 77,284 78,986 77,220 80,500 2000's 84,646 84,839 86,328 87,202 87,919 90,577 91,481 93,015 94,219 95,704 2010's 95,401 96,086 96,503 97,499 98,741 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  17. Virginia Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Virginia Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 877 895 895 1990's 929 1,156 1,101 2,706 2,740 2,812 2,822 2,391 2,469 2,984 2000's 1,749 1,261 1,526 1,517 1,217 1,402 1,256 1,271 1,205 1,126 2010's 1,059 1,103 1,132 1,132 1,123 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  18. Virginia Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Virginia Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 550,318 573,731 601,906 1990's 622,883 651,203 664,500 690,061 721,495 753,003 789,985 812,866 847,938 893,887 2000's 907,855 941,582 982,521 996,564 1,029,389 1,066,302 1,085,509 1,101,863 1,113,016 1,124,717 2010's 1,133,103 1,145,049 1,155,636 1,170,161 1,183,894 - = No Data Reported; -- = Not

  19. Washington Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Washington Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 51,365 56,487 55,231 1990's 58,148 60,887 63,391 65,810 68,118 70,781 73,708 75,550 77,770 80,995 2000's 83,189 84,628 85,286 87,082 93,559 92,417 93,628 95,615 97,799 98,965 2010's 99,231 99,674 100,038 100,939 101,730 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  20. Washington Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Washington Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3,355 3,564 3,365 1990's 3,428 3,495 3,490 3,448 3,586 3,544 3,587 3,748 3,848 4,040 2000's 4,007 3,898 3,928 3,775 3,992 3,489 3,428 3,630 3,483 3,428 2010's 3,372 3,353 3,338 3,320 3,355 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  1. Washington Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Washington Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 392,469 413,008 425,624 1990's 458,013 492,189 528,913 565,475 604,315 638,603 673,357 702,701 737,208 779,104 2000's 813,319 841,617 861,943 895,800 926,510 966,199 997,728 1,025,171 1,047,319 1,059,239 2010's 1,067,979 1,079,277 1,088,762 1,102,318 1,118,193 - = No Data Reported; -- = Not

  2. California Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) California Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 413 404,507 407,435 410,231 1990's 415,073 421,278 412,467 411,648 411,140 411,535 408,294 406,803 588,224 416,791 2000's 413,003 416,036 420,690 431,795 432,367 434,899 442,052 446,267 447,160 441,806 2010's 439,572 440,990 442,708 444,342 443,115 - = No Data Reported; -- = Not Applicable; NA =

  3. California Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) California Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 31 44,764 44,680 46,243 1990's 46,048 44,865 40,528 42,748 38,750 38,457 36,613 35,830 36,235 36,435 2000's 35,391 34,893 33,725 34,617 41,487 40,226 38,637 39,134 39,591 38,746 2010's 38,006 37,575 37,686 37,996 37,548 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  4. California Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) California Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 7,626 7,904,858 8,113,034 8,313,776 1990's 8,497,848 8,634,774 8,680,613 8,726,187 8,790,733 8,865,541 8,969,308 9,060,473 9,181,928 9,331,206 2000's 9,370,797 9,603,122 9,726,642 9,803,311 9,957,412 10,124,433 10,329,224 10,439,220 10,515,162 10,510,950 2010's 10,542,584 10,625,190 10,681,916

  5. Colorado Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Colorado Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 108 109,770 110,769 112,004 1990's 112,661 113,945 114,898 115,924 115,994 118,502 121,221 123,580 125,178 129,041 2000's 131,613 134,393 136,489 138,621 138,543 137,513 139,746 141,420 144,719 145,624 2010's 145,460 145,837 145,960 150,145 150,235 - = No Data Reported; -- = Not Applicable; NA = Not

  6. Colorado Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Colorado Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1 896 923 976 1990's 1,018 1,074 1,108 1,032 1,176 1,528 2,099 2,923 3,349 4,727 2000's 4,994 4,729 4,337 4,054 4,175 4,318 4,472 4,592 4,816 5,084 2010's 6,232 6,529 6,906 7,293 7,823 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  7. Colorado Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Colorado Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 925 942,571 955,810 970,512 1990's 983,592 1,002,154 1,022,542 1,044,699 1,073,308 1,108,899 1,147,743 1,183,978 1,223,433 1,265,032 2000's 1,315,619 1,365,413 1,412,923 1,453,974 1,496,876 1,524,813 1,558,911 1,583,945 1,606,602 1,622,434 2010's 1,634,587 1,645,716 1,659,808 1,672,312 1,690,581 -

  8. Connecticut Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Connecticut Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2 2,709 2,818 2,908 1990's 3,061 2,921 2,923 2,952 3,754 3,705 3,435 3,459 3,441 3,465 2000's 3,683 3,881 3,716 3,625 3,470 3,437 3,393 3,317 3,196 3,138 2010's 3,063 3,062 3,148 4,454 4,217 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  9. Delaware Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Delaware Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 6 6,180 6,566 7,074 1990's 7,485 7,895 8,173 8,409 8,721 9,133 9,518 9,807 10,081 10,441 2000's 9,639 11,075 11,463 11,682 11,921 12,070 12,345 12,576 12,703 12,839 2010's 12,861 12,931 12,997 13,163 13,352 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  10. Delaware Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Delaware Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 81 82,829 84,328 86,428 1990's 88,894 91,467 94,027 96,914 100,431 103,531 106,548 109,400 112,507 115,961 2000's 117,845 122,829 126,418 129,870 133,197 137,115 141,276 145,010 147,541 149,006 2010's 150,458 152,005 153,307 155,627 158,502 - = No Data Reported; -- = Not Applicable; NA = Not

  11. Florida Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Florida Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 41 42,376 43,178 43,802 1990's 43,674 45,012 45,123 47,344 47,851 46,459 47,578 48,251 46,778 50,052 2000's 50,888 53,118 53,794 55,121 55,324 55,479 55,259 57,320 58,125 59,549 2010's 60,854 61,582 63,477 64,772 67,460 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  12. Florida Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Florida Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 442 444,848 446,690 452,544 1990's 457,648 467,221 471,863 484,816 497,777 512,365 521,674 532,790 542,770 556,628 2000's 571,972 590,221 603,690 617,373 639,014 656,069 673,122 682,996 679,265 674,090 2010's 675,551 679,199 686,994 694,210 703,535 - = No Data Reported; -- = Not Applicable; NA = Not

  13. Georgia Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Georgia Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 94 98,809 102,277 106,690 1990's 108,295 109,659 111,423 114,889 117,980 120,122 123,200 123,367 126,050 225,020 2000's 128,275 130,373 128,233 129,867 128,923 128,389 127,843 127,832 126,804 127,347 2010's 124,759 123,454 121,243 126,060 122,573 - = No Data Reported; -- = Not Applicable; NA = Not

  14. Georgia Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Georgia Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3 3,034 3,144 3,079 1990's 3,153 3,124 3,186 3,302 3,277 3,261 3,310 3,310 3,262 5,580 2000's 3,294 3,330 3,219 3,326 3,161 3,543 3,053 2,913 2,890 2,254 2010's 2,174 2,184 2,112 2,242 2,481 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  15. Georgia Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Georgia Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,190 1,237,201 1,275,128 1,308,972 1990's 1,334,935 1,363,723 1,396,860 1,430,626 1,460,141 1,495,992 1,538,458 1,553,948 1,659,730 1,732,865 2000's 1,680,749 1,737,850 1,735,063 1,747,017 1,752,346 1,773,121 1,726,239 1,793,650 1,791,256 1,744,934 2010's 1,740,587 1,740,006 1,739,543 1,805,425

  16. Hawaii Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Hawaii Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,896 2,852 2,842 1990's 2,837 2,786 2,793 3,222 2,805 2,825 2,823 2,783 2,761 2,763 2000's 2,768 2,777 2,781 2,804 2,578 2,572 2,548 2,547 2,540 2,535 2010's 2,551 2,560 2,545 2,627 2,789 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  17. Hawaii Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Hawaii Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 28,502 28,761 28,970 1990's 29,137 29,701 29,805 29,984 30,614 30,492 31,017 30,990 30,918 30,708 2000's 30,751 30,794 30,731 30,473 26,255 26,219 25,982 25,899 25,632 25,466 2010's 25,389 25,305 25,184 26,374 28,919 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  18. Idaho Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Idaho Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 17,482 18,454 18,813 1990's 19,452 20,328 21,145 21,989 22,999 24,150 25,271 26,436 27,697 28,923 2000's 30,018 30,789 31,547 32,274 33,104 33,362 33,625 33,767 37,320 38,245 2010's 38,506 38,912 39,202 39,722 40,229 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  19. Idaho Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Idaho Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 104,824 111,532 113,898 1990's 113,954 126,282 136,121 148,582 162,971 175,320 187,756 200,165 213,786 227,807 2000's 240,399 251,004 261,219 274,481 288,380 301,357 316,915 323,114 336,191 342,277 2010's 346,602 350,871 353,963 359,889 367,394 - = No Data Reported; -- = Not Applicable; NA = Not

  20. Illinois Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Illinois Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 241,367 278,473 252,791 1990's 257,851 261,107 263,988 268,104 262,308 264,756 265,007 268,841 271,585 274,919 2000's 279,179 278,506 279,838 281,877 273,967 276,763 300,606 296,465 298,418 294,226 2010's 291,395 293,213 297,523 282,743 294,391 - = No Data Reported; -- = Not Applicable; NA = Not

  1. Illinois Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Illinois Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 19,460 20,015 25,161 1990's 25,991 26,489 27,178 27,807 25,788 25,929 29,493 28,472 28,063 27,605 2000's 27,348 27,421 27,477 26,698 29,187 29,887 26,109 24,000 23,737 23,857 2010's 25,043 23,722 23,390 23,804 23,829 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  2. Illinois Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Illinois Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3,170,364 3,180,199 3,248,117 1990's 3,287,091 3,320,285 3,354,679 3,388,983 3,418,052 3,452,975 3,494,545 3,521,707 3,556,736 3,594,071 2000's 3,631,762 3,670,693 3,688,281 3,702,308 3,754,132 3,975,961 3,812,121 3,845,441 3,869,308 3,839,438 2010's 3,842,206 3,855,942 3,878,806 3,838,120

  3. Indiana Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Indiana Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 116,571 119,458 122,803 1990's 124,919 128,223 129,973 131,925 134,336 137,162 139,097 140,515 141,307 145,631 2000's 148,411 148,830 150,092 151,586 151,943 159,649 154,322 155,885 157,223 155,615 2010's 156,557 161,293 158,213 158,965 159,596 - = No Data Reported; -- = Not Applicable; NA = Not

  4. Indiana Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Indiana Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,250,476 1,275,401 1,306,747 1990's 1,327,772 1,358,640 1,377,023 1,402,770 1,438,483 1,463,640 1,489,647 1,509,142 1,531,914 1,570,253 2000's 1,604,456 1,613,373 1,657,640 1,644,715 1,588,738 1,707,195 1,661,186 1,677,857 1,678,158 1,662,663 2010's 1,669,026 1,707,148 1,673,132 1,681,841 1,693,267

  5. Iowa Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Iowa Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 80,797 81,294 82,549 1990's 83,047 84,387 85,325 86,452 86,918 88,585 89,663 90,643 91,300 92,306 2000's 93,836 95,485 96,496 96,712 97,274 97,767 97,823 97,979 98,144 98,416 2010's 98,396 98,541 99,113 99,017 99,182 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  6. Iowa Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Iowa Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,033 1,937 1,895 1990's 1,883 1,866 1,835 1,903 1,957 1,957 2,066 1,839 1,862 1,797 2000's 1,831 1,830 1,855 1,791 1,746 1,744 1,670 1,651 1,652 1,626 2010's 1,528 1,465 1,469 1,491 1,572 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  7. Iowa Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Iowa Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 690,532 689,655 701,687 1990's 706,842 716,088 729,081 740,722 750,678 760,848 771,109 780,746 790,162 799,015 2000's 812,323 818,313 824,218 832,230 839,415 850,095 858,915 865,553 872,980 875,781 2010's 879,713 883,733 892,123 895,414 900,420 - = No Data Reported; -- = Not Applicable; NA = Not

  8. Kansas Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Kansas Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 82,934 83,810 85,143 1990's 85,539 86,874 86,840 87,735 86,457 88,163 89,168 85,018 89,654 86,003 2000's 87,007 86,592 87,397 88,030 86,640 85,634 85,686 85,376 84,703 84,715 2010's 84,446 84,874 84,673 84,969 85,867 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  9. Kansas Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Kansas Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 4,440 4,314 4,366 1990's 4,357 3,445 3,296 4,369 3,560 3,079 2,988 7,014 10,706 5,861 2000's 8,833 9,341 9,891 9,295 8,955 8,300 8,152 8,327 8,098 7,793 2010's 7,664 7,954 7,970 7,877 7,429 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  10. Kansas Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Kansas Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 725,676 733,101 731,792 1990's 747,081 753,839 762,545 777,658 773,357 797,524 804,213 811,975 841,843 824,803 2000's 833,662 836,486 843,353 850,464 855,272 856,761 862,203 858,304 853,125 855,454 2010's 853,842 854,730 854,800 858,572 861,092 - = No Data Reported; -- = Not Applicable; NA = Not

  11. Kentucky Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Kentucky Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 63,024 63,971 65,041 1990's 67,086 68,461 69,466 71,998 73,562 74,521 76,079 77,693 80,147 80,283 2000's 81,588 81,795 82,757 84,110 84,493 85,243 85,236 85,210 84,985 83,862 2010's 84,707 84,977 85,129 85,999 85,318 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  12. Kentucky Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Kentucky Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,391 1,436 1,443 1990's 1,544 1,587 1,608 1,585 1,621 1,630 1,633 1,698 1,864 1,813 2000's 1,801 1,701 1,785 1,695 1,672 1,698 1,658 1,599 1,585 1,715 2010's 1,742 1,705 1,720 1,767 1,780 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  13. Kentucky Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Kentucky Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 596,320 606,106 614,058 1990's 624,477 633,942 644,281 654,664 668,774 685,481 696,989 713,509 726,960 735,371 2000's 744,816 749,106 756,234 763,290 767,022 770,080 770,171 771,047 753,531 754,761 2010's 758,129 759,584 757,790 761,575 760,131 - = No Data Reported; -- = Not Applicable; NA = Not

  14. Louisiana Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Louisiana Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 67,382 66,472 64,114 1990's 62,770 61,574 61,030 62,055 62,184 62,930 62,101 62,270 63,029 62,911 2000's 62,710 62,241 62,247 63,512 60,580 58,409 57,097 57,127 57,066 58,396 2010's 58,562 58,749 63,381 59,147 58,611 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  15. Louisiana Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Louisiana Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,617 1,503 1,531 1990's 1,504 1,469 1,452 1,592 1,737 1,383 1,444 1,406 1,380 1,397 2000's 1,318 1,440 1,357 1,291 1,460 1,086 962 945 988 954 2010's 942 920 963 916 883 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  16. Maine Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Maine Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3,435 3,731 3,986 1990's 4,250 4,455 4,838 4,979 5,297 5,819 6,414 6,606 6,662 6,582 2000's 6,954 6,936 7,375 7,517 7,687 8,178 8,168 8,334 8,491 8,815 2010's 9,084 9,681 10,179 11,415 11,810 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  17. Maine Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Maine Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 12,134 11,933 11,902 1990's 12,000 12,424 13,766 13,880 14,104 14,917 14,982 15,221 15,646 15,247 2000's 17,111 17,302 17,921 18,385 18,707 18,633 18,824 18,921 19,571 20,806 2010's 21,142 22,461 23,555 24,765 27,047 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  18. Maryland Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Maryland Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 51,252 53,045 54,740 1990's 55,576 61,878 62,858 63,767 64,698 66,094 69,991 69,056 67,850 69,301 2000's 70,671 70,691 71,824 72,076 72,809 73,780 74,584 74,856 75,053 75,771 2010's 75,192 75,788 75,799 77,117 77,846 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  19. Maryland Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Maryland Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 5,222 5,397 5,570 1990's 5,646 520 514 496 516 481 430 479 1,472 536 2000's 329 795 1,434 1,361 1,354 1,325 1,340 1,333 1,225 1,234 2010's 1,255 1,226 1,163 1,173 1,179 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  20. Maryland Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Maryland Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 755,294 760,754 767,219 1990's 774,707 782,373 894,677 807,204 824,137 841,772 871,012 890,195 901,455 939,029 2000's 941,384 959,772 978,319 987,863 1,009,455 1,024,955 1,040,941 1,053,948 1,057,521 1,067,807 2010's 1,071,566 1,077,168 1,078,978 1,099,272 1,101,292 - = No Data Reported; -- = Not

  1. West Virginia Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) West Virginia Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 31,283 33,192 33,880 1990's 32,785 32,755 33,289 33,611 33,756 36,144 33,837 33,970 35,362 35,483 2000's 41,949 35,607 35,016 35,160 34,932 36,635 34,748 34,161 34,275 34,044 2010's 34,063 34,041 34,078 34,283 34,339 - = No Data Reported; -- = Not Applicable; NA = Not Available; W

  2. West Virginia Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) West Virginia Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 351,024 349,765 349,347 1990's 349,673 350,489 352,463 352,997 352,929 353,629 358,049 362,432 359,783 362,292 2000's 360,471 363,126 361,171 359,919 358,027 374,301 353,292 347,433 347,368 343,837 2010's 344,131 342,069 340,256 340,102 338,652 - = No Data Reported; -- = Not

  3. Wisconsin Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Wisconsin Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 96,760 99,157 102,492 1990's 106,043 109,616 112,761 115,961 119,788 125,539 129,146 131,238 134,651 135,829 2000's 140,370 144,050 149,774 150,128 151,907 155,109 159,074 160,614 163,026 163,843 2010's 164,173 165,002 165,657 166,845 167,901 - = No Data Reported; -- = Not Applicable; NA = Not

  4. Wisconsin Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) Wisconsin Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 7,411 7,218 7,307 1990's 7,154 7,194 7,396 7,979 7,342 6,454 5,861 8,346 9,158 9,756 2000's 9,630 9,864 9,648 10,138 10,190 8,484 5,707 5,999 5,969 6,396 2010's 6,413 6,376 6,581 6,677 7,000 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  5. Wisconsin Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Wisconsin Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,054,347 1,072,585 1,097,514 1990's 1,123,557 1,151,939 1,182,834 1,220,500 1,253,333 1,291,424 1,324,570 1,361,348 1,390,068 1,426,909 2000's 1,458,959 1,484,536 1,514,700 1,541,455 1,569,719 1,592,621 1,611,772 1,632,200 1,646,644 1,656,614 2010's 1,663,583 1,671,834 1,681,001 1,692,891

  6. Wyoming Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) Wyoming Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 15,342 15,093 14,012 1990's 13,767 14,931 15,064 15,315 15,348 15,580 17,036 15,907 16,171 16,317 2000's 16,366 16,027 16,170 17,164 17,490 17,904 18,016 18,062 19,286 19,843 2010's 19,977 20,146 20,387 20,617 20,894 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  7. Wyoming Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) Wyoming Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 113,175 112,126 113,129 1990's 113,598 113,463 114,793 116,027 117,385 119,544 131,910 125,740 127,324 127,750 2000's 129,274 129,897 133,445 135,441 137,434 140,013 142,385 143,644 152,439 153,062 2010's 153,852 155,181 157,226 158,889 160,896 - = No Data Reported; -- = Not Applicable; NA = Not

  8. The deterministic chaos and random noise in turbulent jet

    SciTech Connect (OSTI)

    Yao, Tian-Liang; Liu, Hai-Feng Xu, Jian-Liang; Li, Wei-Feng

    2014-06-01

    A turbulent flow is usually treated as a superposition of coherent structure and incoherent turbulence. In this paper, the largest Lyapunov exponent and the random noise in the near field of round jet and plane jet are estimated with our previously proposed method of chaotic time series analysis [T. L. Yao, et al., Chaos 22, 033102 (2012)]. The results show that the largest Lyapunov exponents of the round jet and plane jet are in direct proportion to the reciprocal of the integral time scale of turbulence, which is in accordance with the results of the dimensional analysis, and the proportionality coefficients are equal. In addition, the random noise of the round jet and plane jet has the same linear relation with the Kolmogorov velocity scale of turbulence. As a result, the random noise may well be from the incoherent disturbance in turbulence, and the coherent structure in turbulence may well follow the rule of chaotic motion.

  9. UGE Scheduler Cycle Time

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

    UGE Scheduler Cycle Time UGE Scheduler Cycle Time Genepool Cycle Time Genepool Daily Genepool Weekly Phoebe Cycle Time Phoebe Daily Phoebe Weekly What is the Scheduler Cycle? The Univa Grid Engine Scheduler cycle performs a number of important tasks, including: Prioritizing Jobs Reserving Resources for jobs requesting more resources (slots / memory) Dispatching jobs or tasks to the compute nodes Evaluating job dependencies The "cycle time" is the length of time it takes the scheduler

  10. Global temperature deviations as a random walk

    SciTech Connect (OSTI)

    Karner, O.

    1996-12-31

    Surface air temperature is the main parameter to represent the earth`s contemporary climate. Several historical temperature records on a global/monthly basis are available. Time-series analysis shows that they can be modelled via autoregressive moving average models closely connected to the classical random walk model. Fitted models emphasize a nonstationary character of the global/monthly temperature deviation from a certain level. The nonstationarity explains all trends and periods, found in the last century`s variability of global mean temperature. This means that the short-term temperature trends are inevitable and may have little in common with a currently increasing carbon dioxide amount. The calculations show that a reasonable understanding of the contemporary global mean climate is attainable, assuming random forcing to the climate system and treating temperature deviation as a response to it. The forcings occur due to volcanic eruptions, redistribution of cloudiness, variations in snow and ice covered areas, changes in solar output, etc. Their impact can not be directly estimated from changes of the earth`s radiation budget at the top of the atmosphere, because actual measurements represent mixture of the forcings and responses. Thus, it is impossible empirically to separate the impact of one particular forcing (e.g., that due to increase of CO{sub 2} amount) from the sequence of all existing forcings in the earth climate system. More accurate modelling involving main feedback loops is necessary to ease such a separation.

  11. Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope

    SciTech Connect (OSTI)

    Quan, Wei; Lv, Lin Liu, Baiqi

    2014-11-15

    In order to improve the atom spin gyroscope's operational accuracy and compensate the random error caused by the nonlinear and weak-stability characteristic of the random atomic spin gyroscope (ASG) drift, the hybrid random drift error model based on autoregressive (AR) and genetic programming (GP) + genetic algorithm (GA) technique is established. The time series of random ASG drift is taken as the study object. The time series of random ASG drift is acquired by analyzing and preprocessing the measured data of ASG. The linear section model is established based on AR technique. After that, the nonlinear section model is built based on GP technique and GA is used to optimize the coefficients of the mathematic expression acquired by GP in order to obtain a more accurate model. The simulation result indicates that this hybrid model can effectively reflect the characteristics of the ASG's random drift. The square error of the ASG's random drift is reduced by 92.40%. Comparing with the AR technique and the GP + GA technique, the random drift is reduced by 9.34% and 5.06%, respectively. The hybrid modeling method can effectively compensate the ASG's random drift and improve the stability of the system.

  12. Verification Challenges at Low Numbers

    SciTech Connect (OSTI)

    Benz, Jacob M.; Booker, Paul M.; McDonald, Benjamin S.

    2013-06-01

    Many papers have dealt with the political difficulties and ramifications of deep nuclear arms reductions, and the issues of “Going to Zero”. Political issues include extended deterrence, conventional weapons, ballistic missile defense, and regional and geo-political security issues. At each step on the road to low numbers, the verification required to ensure compliance of all parties will increase significantly. Looking post New START, the next step will likely include warhead limits in the neighborhood of 1000 . Further reductions will include stepping stones at1000 warheads, 100’s of warheads, and then 10’s of warheads before final elimination could be considered of the last few remaining warheads and weapons. This paper will focus on these three threshold reduction levels, 1000, 100’s, 10’s. For each, the issues and challenges will be discussed, potential solutions will be identified, and the verification technologies and chain of custody measures that address these solutions will be surveyed. It is important to note that many of the issues that need to be addressed have no current solution. In these cases, the paper will explore new or novel technologies that could be applied. These technologies will draw from the research and development that is ongoing throughout the national laboratory complex, and will look at technologies utilized in other areas of industry for their application to arms control verification.

  13. California's Efforts for Advancing Ultrafine Particle Number...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Efforts for Advancing Ultrafine Particle Number Measurements for Clean Diesel Exhaust California's Efforts for Advancing Ultrafine Particle Number Measurements for Clean Diesel...

  14. Identification of Export Control Classification Number - ITER

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

    Identification of Export Control Classification Number - ITER (April 2012) As the "Shipper of Record" please provide the appropriate Export Control Classification Number (ECCN) for...

  15. Task Time Tracker

    Energy Science and Technology Software Center (OSTI)

    2013-07-24

    This client-side web app tracks the amount of time spent on arbitrary tasks. It allosw the creation of an unlimited number of arbitrarily named tasks ans via simple interactions, tracks the amount of time spent working on the drfined tasks.

  16. Alternating current response of carbon nanotubes with randomly distributed impurities

    SciTech Connect (OSTI)

    Hirai, Daisuke; Watanabe, Satoshi [Department of Materials Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656 (Japan); Yamamoto, Takahiro [Department of Electrical Engineering and Department of Liberal Arts (Physics), Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo 125-8585 (Japan)

    2014-10-27

    The increasing need for nanodevices has necessitated a better understanding of the electronic transport behavior of nanomaterials. We therefore theoretically examine the AC transport properties of metallic carbon nanotubes with randomly distributed impurities. We find that the long-range impurity scattering increases the emittance, but does not affect the DC conductance. The estimated dwell time of electrons increases with the potential amplitudes. That is, multiple scattering by the impurities increases the kinetic inductance in proportion to the dwell time, which eventually increases the emittance. We believe that our findings can contribute significantly to nanodevice development.

  17. Bayati Kim Saberi random graph sampler

    Energy Science and Technology Software Center (OSTI)

    2012-06-05

    This software package implements the algorithm from a paper by Bayati, Kim, and Saberi (first reference below) to generate a uniformly random sample of a graph with a prescribed degree distribution.

  18. Quasi-random array imaging collimator

    DOE Patents [OSTI]

    Fenimore, E.E.

    1980-08-20

    A hexagonally shaped quasi-random no-two-holes-touching imaging collimator. The quasi-random array imaging collimator eliminates contamination from small angle off-axis rays by using a no-two-holes-touching pattern which simultaneously provides for a self-supporting array increasing throughput by elimination of a substrate. The present invention also provides maximum throughput using hexagonally shaped holes in a hexagonal lattice pattern for diffraction limited applications. Mosaicking is also disclosed for reducing fabrication effort.

  19. Climate Zone Number 5 | Open Energy Information

    Open Energy Info (EERE)

    Climate Zone Number 5 Jump to: navigation, search A type of climate defined in the ASHRAE 169-2006 standard. Climate Zone Number 5 is defined as Cool- Humid(5A) with IP Units 5400...

  20. Real-Time Benchmark Suite

    Energy Science and Technology Software Center (OSTI)

    1992-01-17

    This software provides a portable benchmark suite for real time kernels. It tests the performance of many of the system calls, as well as the interrupt response time and task response time to interrupts. These numbers provide a baseline for comparing various real-time kernels and hardware platforms.

  1. Time Off

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

    Time Off Time Off A comprehensive benefits package with plan options for health care and retirement to take care of our employees today and tomorrow. Contact Benefits Office (505)...

  2. Geometrical effects on the electron residence time in semiconductor nano-particles

    SciTech Connect (OSTI)

    Koochi, Hakimeh; Ebrahimi, Fatemeh

    2014-09-07

    We have used random walk (RW) numerical simulations to investigate the influence of the geometry on the statistics of the electron residence time ?{sub r} in a trap-limited diffusion process through semiconductor nano-particles. This is an important parameter in coarse-grained modeling of charge carrier transport in nano-structured semiconductor films. The traps have been distributed randomly on the surface (r{sup 2} model) or through the whole particle (r{sup 3} model) with a specified density. The trap energies have been taken from an exponential distribution and the traps release time is assumed to be a stochastic variable. We have carried out (RW) simulations to study the effect of coordination number, the spatial arrangement of the neighbors and the size of nano-particles on the statistics of ?{sub r}. It has been observed that by increasing the coordination number n, the average value of electron residence time, ?{sup }{sub r} rapidly decreases to an asymptotic value. For a fixed coordination number n, the electron's mean residence time does not depend on the neighbors' spatial arrangement. In other words, ?{sup }{sub r} is a porosity-dependence, local parameter which generally varies remarkably from site to site, unless we are dealing with highly ordered structures. We have also examined the effect of nano-particle size d on the statistical behavior of ?{sup }{sub r}. Our simulations indicate that for volume distribution of traps, ?{sup }{sub r} scales as d{sup 2}. For a surface distribution of traps ?{sup }{sub r} increases almost linearly with d. This leads to the prediction of a linear dependence of the diffusion coefficient D on the particle size d in ordered structures or random structures above the critical concentration which is in accordance with experimental observations.

  3. Quasiparticle random-phase approximation with interactions from...

    Office of Scientific and Technical Information (OSTI)

    Quasiparticle random-phase approximation with interactions from the Similarity Renormalization Group Citation Details In-Document Search Title: Quasiparticle random-phase ...

  4. Listening to the noise: random fluctuations reveal gene network...

    Office of Scientific and Technical Information (OSTI)

    Journal Article: Listening to the noise: random fluctuations reveal gene network parameters Citation Details In-Document Search Title: Listening to the noise: random fluctuations...

  5. 2015 Salishan Random Access (Conference) | SciTech Connect

    Office of Scientific and Technical Information (OSTI)

    Conference: 2015 Salishan Random Access Citation Details In-Document Search Title: 2015 Salishan Random Access You are accessing a document from the Department of Energy's (DOE) ...

  6. 2015 Salishan Random Access (Conference) | SciTech Connect

    Office of Scientific and Technical Information (OSTI)

    Conference: 2015 Salishan Random Access Citation Details In-Document Search Title: 2015 Salishan Random Access Authors: Vigil, Benny Manuel 1 ; Mattson, Tim 2 ; Coteus, Paul ...

  7. Stabilizing Topological Phases in Graphene via Random Adsorption...

    Office of Scientific and Technical Information (OSTI)

    Stabilizing Topological Phases in Graphene via Random Adsorption Prev Next Title: Stabilizing Topological Phases in Graphene via Random Adsorption Authors: Jiang, Hua ; Qiao,...

  8. Application of Random Vibration Theory Methodology for Seismic...

    Energy Savers [EERE]

    Application of Random Vibration Theory Methodology for Seismic Soil-Structure Interaction Analysis Application of Random Vibration Theory Methodology for Seismic Soil-Structure...

  9. Alabama Natural Gas Number of Gas and Gas Condensate Wells (Number...

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

    Gas and Gas Condensate Wells (Number of Elements) Alabama Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  10. Ohio Natural Gas Number of Gas and Gas Condensate Wells (Number...

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

    Gas and Gas Condensate Wells (Number of Elements) Ohio Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  11. Wyoming Natural Gas Number of Gas and Gas Condensate Wells (Number...

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

    Gas and Gas Condensate Wells (Number of Elements) Wyoming Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  12. Texas Natural Gas Number of Gas and Gas Condensate Wells (Number...

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

    Gas and Gas Condensate Wells (Number of Elements) Texas Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  13. Indiana Natural Gas Number of Gas and Gas Condensate Wells (Number...

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

    Gas and Gas Condensate Wells (Number of Elements) Indiana Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  14. Alaska Natural Gas Number of Gas and Gas Condensate Wells (Number...

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

    Gas and Gas Condensate Wells (Number of Elements) Alaska Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  15. Oregon Natural Gas Number of Gas and Gas Condensate Wells (Number...

    Gasoline and Diesel Fuel Update (EIA)

    Gas and Gas Condensate Wells (Number of Elements) Oregon Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  16. U.S. Natural Gas Number of Gas and Gas Condensate Wells (Number...

    Gasoline and Diesel Fuel Update (EIA)

    Gas and Gas Condensate Wells (Number of Elements) U.S. Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  17. Nevada Natural Gas Number of Gas and Gas Condensate Wells (Number...

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

    Gas and Gas Condensate Wells (Number of Elements) Nevada Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  18. Utah Natural Gas Number of Gas and Gas Condensate Wells (Number...

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

    Gas and Gas Condensate Wells (Number of Elements) Utah Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  19. CHANGING TIMES

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

    District Number of Sub-agreements District MWs Fort Worth 8 89 14,776,000 Kansas City 14 205 11,440,570 Little Rock 46 1,069 51,810,300 St. Louis 11 58 3,552,000 Tulsa...

  20. Random paths and current fluctuations in nonequilibrium statistical mechanics

    SciTech Connect (OSTI)

    Gaspard, Pierre

    2014-07-15

    An overview is given of recent advances in nonequilibrium statistical mechanics about the statistics of random paths and current fluctuations. Although statistics is carried out in space for equilibrium statistical mechanics, statistics is considered in time or spacetime for nonequilibrium systems. In this approach, relationships have been established between nonequilibrium properties such as the transport coefficients, the thermodynamic entropy production, or the affinities, and quantities characterizing the microscopic Hamiltonian dynamics and the chaos or fluctuations it may generate. This overview presents results for classical systems in the escape-rate formalism, stochastic processes, and open quantum systems.

  1. ARM - Measurement - Cloud particle number concentration

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

    number concentration ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Cloud particle number concentration The total number of cloud particles present in any given volume of air. Categories Cloud Properties Instruments The above measurement is considered scientifically relevant for the following instruments. Refer to the datastream (netcdf) file headers of each instrument for a list of all available

  2. Calculating Atomic Number Densities for Uranium

    Energy Science and Technology Software Center (OSTI)

    1993-01-01

    Provides method to calculate atomic number densities of selected uranium compounds and hydrogenous moderators for use in nuclear criticality safety analyses at gaseous diffusion uranium enrichment facilities.

  3. OMB Control Number: 1910-5165

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

    damages assessed under Contract Work Hours and Safety Standards Act: Page 1 OMB Control Number: 1910-5165 Expires: 04302015 SEMI-ANNUAL DAVIS-BACON ENFORCEMENT REPORT...

  4. USE OF MAILBOX APPROACH, VIDEO SURVEILLANCE, AND SHORT-NOTICE RANDOM INSPECTIONS TO ENHANCE DETECTION OF UNDECLARED LEU PRODUCTION AT GAS CENTRIFUGE ENRICHMENT PLANTS.

    SciTech Connect (OSTI)

    BOYER, B.D.; GORDON, D.M.; JO, J.

    2006-07-16

    Current safeguards approaches used by the IAEA at gas centrifuge enrichment plants (GCEPs) need enhancement in order to detect undeclared LEU production with adequate detection probability. ''Mailbox'' declarations have been used in the last two decades to verify receipts, production, and shipments at some bulk-handling facilities (e.g., fuel-fabrication plants). The operator declares the status of his plant to the IAEA on a daily basis using a secure ''Mailbox'' system such as a secure tamper-resistant computer. The operator agrees to hold receipts and shipments for a specified period of time, along with a specified number of annual inspections, to enable inspector access to a statistically large enough population of UF{sub 6} cylinders and fuel assemblies to achieve the desired detection probability. The inspectors can access the ''Mailbox'' during randomly timed inspections and then verify the operator's declarations for that day. Previously, this type of inspection regime was considered mainly for verifying the material balance at fuel-fabrication, enrichment, and conversion plants. Brookhaven National Laboratory has expanded the ''Mailbox'' concept with short-notice random inspections (SNRIs), coupled with enhanced video surveillance, to include declaration and verification of UF{sub 6} cylinder operational data to detect activities associated with undeclared LEU production at GCEPs. Since the ''Mailbox'' declarations would also include data relevant to material-balance verification, these randomized inspections would replace the scheduled monthly interim inspections for material-balance purposes; in addition, the inspectors could simultaneously perform the required number of Limited-Frequency Unannounced Access (LFUA) inspections used for HEU detection. This approach would provide improved detection capabilities for a wider range of diversion activities with not much more inspection effort than at present.

  5. Low Mach Number Models in Computational Astrophysics

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

    Ann Almgren Low Mach Number Models in Computational Astrophysics February 4, 2014 Ann Almgren. Berkeley Lab Downloads Almgren-nug2014.pdf | Adobe Acrobat PDF file Low Mach Number Models in Computational Astrophysics - Ann Almgren, Berkeley Lab Last edited: 2016-02-01 08:06:52

  6. Time Card Entry System

    Energy Science and Technology Software Center (OSTI)

    1996-05-07

    The Time Card Entry System was developed for the Department of Enegy, Idaho Operations Office (DOE-ID) to interface with the DOE headquarters (DOE-HQ) Electronic Time and Attendance (ETA) system for payroll. It features pop-up window pick lists for Work Breakdown Structure numbers and Hour Codes and has extensive processing that ensures that time and attendance reported by the employee fulfills U.S. Government/OMB requirements before Timekeepers process the data at the end of the two weekmore » payroll cycle using ETA. A tour of duty profile (e.g., ten hour day, four day week with Sunday, friday and Saturday off), previously established in the ETA system, is imported into the Time Card Entry System by the timekeepers. An individual''s profile establishes the basis for validation of time of day and number of hours worked per day. At the end of the two cycle, data is exported by the timekeepers from the Time Card Entry System into ETA files.« less

  7. Particle Number & Particulate Mass Emissions Measurements on...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    on a 'Euro VI' Heavy-duty Engine using the PMP Methodologies Particle Number & Particulate Mass Emissions Measurements on a 'Euro VI' Heavy-duty Engine using the PMP ...

  8. Identification of Export Control Classification Number - ITER

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

    Identification of Export Control Classification Number - ITER (April 2012) As the "Shipper of Record" please provide the appropriate Export Control Classification Number (ECCN) for the products (equipment, components and/or materials) and if applicable the nonproprietary associated installation/maintenance documentation that will be shipped from the United States to the ITER International Organization in Cadarache, France or to ITER Members worldwide on behalf of the Company. In rare

  9. Stockpile Stewardship Quarterly Volume 1, Number 4

    National Nuclear Security Administration (NNSA)

    1, Number 4 * February 2012 Message from the Assistant Deputy Administrator for Stockpile Stewardship, Chris Deeney Defense Programs Stockpile Stewardship in Action Volume 1, Number 4 Inside this Issue 2 Applying Advanced Simulation Models to Neutron Tube Ion Extraction 3 Advanced Optical Cavities for Subcritical and Hydrodynamic Experiments 5 Progress Toward Ignition on the National Ignition Facility 7 Commissioning URSA Minor: The First LTD-Based Accelerator for Radiography 8 Publication

  10. Resilience of complex networks to random breakdown

    SciTech Connect (OSTI)

    Paul, Gerald; Sreenivasan, Sameet; Stanley, H. Eugene

    2005-11-01

    Using Monte Carlo simulations we calculate f{sub c}, the fraction of nodes that are randomly removed before global connectivity is lost, for networks with scale-free and bimodal degree distributions. Our results differ from the results predicted by an equation for f{sub c} proposed by Cohen et al. We discuss the reasons for this disagreement and clarify the domain for which the proposed equation is valid.

  11. Distribution of occupation numbers in finite Fermi systems and role of interaction in chaos and thermalization

    SciTech Connect (OSTI)

    Flambaum, V.V.; Izrailev, F.M. [School of Physics, University of New South Wales, Sydney 2052 (Australia)] [School of Physics, University of New South Wales, Sydney 2052 (Australia)

    1997-01-01

    A method is developed for calculation of single-particle occupation numbers in finite Fermi systems of interacting particles. It is more accurate than the canonical distribution method and gives the Fermi-Dirac distribution in the limit of large number of particles. It is shown that statistical effects of the interaction are absorbed by an increase of the effective temperature. Criteria for quantum chaos and statistical equilibrium are considered. All results are confirmed by numerical experiments in the two-body random interaction model. {copyright} {ital 1997} {ital The American Physical Society}

  12. Probing lepton number violation on three frontiers

    SciTech Connect (OSTI)

    Deppisch, Frank F. [Department of Physics and Astronomy, University College London (United Kingdom)

    2013-12-30

    Neutrinoless double beta decay constitutes the main probe for lepton number violation at low energies, motivated by the expected Majorana nature of the light but massive neutrinos. On the other hand, the theoretical interpretation of the (non-)observation of this process is not straightforward as the Majorana neutrinos can destructively interfere in their contribution and many other New Physics mechanisms can additionally mediate the process. We here highlight the potential of combining neutrinoless double beta decay with searches for Tritium decay, cosmological observations and LHC physics to improve the quantitative insight into the neutrino properties and to unravel potential sources of lepton number violation.

  13. WIPP Documents - All documents by number

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

    Note: Documents that do not have document numbers are not included in this listing. Large file size alert This symbol means the document may be a large file size. All documents by number Common document prefixes DOE/CAO DOE/TRU DOE/CBFO DOE/WIPP DOE/EA NM DOE/EIS Other DOE/CAO Back to top DOE/CAO 95-1095, Oct. 1995 Remote Handled Transuranic Waste Study This study was conducted to satisfy the requirements defined by the WIPP Land Withdrawal Act and considered by DOE to be a prudent exercise in

  14. Convergence properties of polynomial chaos approximations for L2 random variables.

    SciTech Connect (OSTI)

    Field, Richard V., Jr. (.,; .); Grigoriu, Mircea (Cornell University, Ithaca, NY)

    2007-03-01

    Polynomial chaos (PC) representations for non-Gaussian random variables are infinite series of Hermite polynomials of standard Gaussian random variables with deterministic coefficients. For calculations, the PC representations are truncated, creating what are herein referred to as PC approximations. We study some convergence properties of PC approximations for L{sub 2} random variables. The well-known property of mean-square convergence is reviewed. Mathematical proof is then provided to show that higher-order moments (i.e., greater than two) of PC approximations may or may not converge as the number of terms retained in the series, denoted by n, grows large. In particular, it is shown that the third absolute moment of the PC approximation for a lognormal random variable does converge, while moments of order four and higher of PC approximations for uniform random variables do not converge. It has been previously demonstrated through numerical study that this lack of convergence in the higher-order moments can have a profound effect on the rate of convergence of the tails of the distribution of the PC approximation. As a result, reliability estimates based on PC approximations can exhibit large errors, even when n is large. The purpose of this report is not to criticize the use of polynomial chaos for probabilistic analysis but, rather, to motivate the need for further study of the efficacy of the method.

  15. The 17 GHz active region number

    SciTech Connect (OSTI)

    Selhorst, C. L.; Pacini, A. A.; Costa, J. E. R.; Gimnez de Castro, C. G.; Valio, A.; Shibasaki, K.

    2014-08-01

    We report the statistics of the number of active regions (NAR) observed at 17 GHz with the Nobeyama Radioheliograph between 1992, near the maximum of cycle 22, and 2013, which also includes the maximum of cycle 24, and we compare with other activity indexes. We find that NAR minima are shorter than those of the sunspot number (SSN) and radio flux at 10.7 cm (F10.7). This shorter NAR minima could reflect the presence of active regions generated by faint magnetic fields or spotless regions, which were a considerable fraction of the counted active regions. The ratio between the solar radio indexes F10.7/NAR shows a similar reduction during the two minima analyzed, which contrasts with the increase of the ratio of both radio indexes in relation to the SSN during the minimum of cycle 23-24. These results indicate that the radio indexes are more sensitive to weaker magnetic fields than those necessary to form sunspots, of the order of 1500 G. The analysis of the monthly averages of the active region brightness temperatures shows that its long-term variation mimics the solar cycle; however, due to the gyro-resonance emission, a great number of intense spikes are observed in the maximum temperature study. The decrease in the number of these spikes is also evident during the current cycle 24, a consequence of the sunspot magnetic field weakening in the last few years.

  16. Pennsylvania Number of Natural Gas Consumers

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

    1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 618 606 604 540 627 666 1967-2014 Industrial Number of Consumers 4,745 4,624 5,007 5,066 5,024 5,084 1987-2014...

  17. The New Element Curium (Atomic Number 96)

    DOE R&D Accomplishments [OSTI]

    Seaborg, G. T.; James, R. A.; Ghiorso, A.

    1948-00-00

    Two isotopes of the element with atomic number 96 have been produced by the helium-ion bombardment of plutonium. The name curium, symbol Cm, is proposed for element 96. The chemical experiments indicate that the most stable oxidation state of curium is the III state.

  18. Washington Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    059,239 1,067,979 1,079,277 1,088,762 1,102,318 1,118,193 1987-2014 Sales 1,067,979 1,079,277 1,088,762 1,102,318 1,118,193 1997-2014 Commercial Number of Consumers 98,965 99,231...

  19. Minnesota Number of Natural Gas Consumers

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

    1,436,063 1,445,824 1,459,134 1,472,663 1997-2014 Commercial Number of Consumers 131,801 132,163 132,938 134,394 135,557 136,382 1987-2014 Sales 131,986 132,697 134,165 135,235...

  20. West Virginia Number of Natural Gas Consumers

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

    343,837 344,131 342,069 340,256 340,102 338,652 1987-2014 Sales 344,125 342,063 340,251 340,098 338,649 1997-2014 Transported 6 6 5 4 3 1997-2014 Commercial Number of Consumers...

  1. Connecticut Number of Natural Gas Consumers

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

    489,349 490,185 494,970 504,138 513,492 522,658 1986-2014 Sales 489,380 494,065 503,241 512,110 521,460 1997-2014 Transported 805 905 897 1,382 1,198 1997-2014 Commercial Number of...

  2. North Carolina Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    ,102,001 1,115,532 1,128,963 1,142,947 1,161,398 1,183,152 1987-2014 Sales 1,115,532 1,128,963 1,142,947 1,161,398 1,183,152 1997-2014 Commercial Number of Consumers 113,630...

  3. Climate Zone Number 1 | Open Energy Information

    Open Energy Info (EERE)

    Zone Number 1 is defined as Very Hot - Humid(1A) with IP Units 9000 < CDD50F and SI Units 5000 < CDD10C Dry(1B) with IP Units 9000 < CDD50F and SI Units 5000 < CDD10C...

  4. Maine Number of Natural Gas Consumers

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

    20,806 21,142 22,461 23,555 24,765 27,047 1987-2014 Sales 21,141 22,461 23,555 24,765 27,047 1997-2014 Transported 1 0 0 0 0 2010-2014 Commercial Number of Consumers 8,815 9,084...

  5. South Dakota Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    173,856 176,204 179,042 1997-2014 Commercial Number of Consumers 22,071 22,267 22,570 22,955 23,214 23,591 1987-2014 Sales 22,028 22,332 22,716 22,947 23,330 1998-2014...

  6. Study Reveals Fuel Injection Timing Impact on Particle Number Emissions (Fact Sheet)

    SciTech Connect (OSTI)

    Not Available

    2012-12-01

    Start of injection can improve environmental performance of fuel-efficient gasoline direct injection engines.

  7. Livermore Random I/O Testbench

    Energy Science and Technology Software Center (OSTI)

    2012-09-01

    LRIOT is a test bench framework that is designed to generate sophisticated I/O rates that can stress high-performance memory and storage systems, such as non-volatile random access memories (NVRAM)and storage class memory. Furthermore, LRIOT provides the capabilities to mix multiple types of concurrency, namely threading and task parallelism, as well as distributed execution using Message Passing Interface (MPI) libraries. It will be used by algorithm designers to generate access patterns that mimic their application's behavior,more » and by system designers to test high-performance NVRAM storage.« less

  8. Relativistic Random Phase Approximation At Finite Temperature

    SciTech Connect (OSTI)

    Niu, Y. F.; Paar, N.; Vretenar, D.; Meng, J.

    2009-08-26

    The fully self-consistent finite temperature relativistic random phase approximation (FTRRPA) has been established in the single-nucleon basis of the temperature dependent Dirac-Hartree model (FTDH) based on effective Lagrangian with density dependent meson-nucleon couplings. Illustrative calculations in the FTRRPA framework show the evolution of multipole responses of {sup 132}Sn with temperature. With increased temperature, in both monopole and dipole strength distributions additional transitions appear in the low energy region due to the new opened particle-particle and hole-hole transition channels.

  9. Experimental Analysis of a Piezoelectric Energy Harvesting System for Harmonic, Random, and Sine on Random Vibration

    SciTech Connect (OSTI)

    Cryns, Jackson W.; Hatchell, Brian K.; Santiago-Rojas, Emiliano; Silvers, Kurt L.

    2013-07-01

    Formal journal article Experimental analysis of a piezoelectric energy harvesting system for harmonic, random, and sine on random vibration Abstract: Harvesting power with a piezoelectric vibration powered generator using a full-wave rectifier conditioning circuit is experimentally compared for varying sinusoidal, random and sine on random (SOR) input vibration scenarios. Additionally, the implications of source vibration characteristics on harvester design are discussed. Studies in vibration harvesting have yielded numerous alternatives for harvesting electrical energy from vibrations but piezoceramics arose as the most compact, energy dense means of energy transduction. The rise in popularity of harvesting energy from ambient vibrations has made piezoelectric generators commercially available. Much of the available literature focuses on maximizing harvested power through nonlinear processing circuits that require accurate knowledge of generator internal mechanical and electrical characteristics and idealization of the input vibration source, which cannot be assumed in general application. In this manuscript, variations in source vibration and load resistance are explored for a commercially available piezoelectric generator. We characterize the source vibration by its acceleration response for repeatability and transcription to general application. The results agree with numerical and theoretical predictions for in previous literature that load optimal resistance varies with transducer natural frequency and source type, and the findings demonstrate that significant gains are seen with lower tuned transducer natural frequencies for similar source amplitudes. Going beyond idealized steady state sinusoidal and simplified random vibration input, SOR testing allows for more accurate representation of real world ambient vibration. It is shown that characteristic interactions from more complex vibrational sources significantly alter power generation and power processing requirements by increasing harvested power, shifting optimal conditioning impedance, inducing significant voltage supply fluctuations and ultimately rendering idealized sinusoidal and random analyses insufficient.

  10. Rhode Island Number of Natural Gas Consumers

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

    24,846 225,204 225,828 228,487 231,763 233,786 1987-2014 Sales 225,204 225,828 228,487 231,763 233,786 1997-2014 Commercial Number of Consumers 22,988 23,049 23,177 23,359 23,742 23,934 1987-2014 Sales 21,507 21,421 21,442 21,731 21,947 1998-2014 Transported 1,542 1,756 1,917 2,011 1,987 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 467 454 468 432 490 551 1967-2014 Industrial Number of Consumers 260 249 245 248 271 266 1987-2014 Sales 57 53 56 62 62 1998-2014 Transported 192

  11. South Carolina Number of Natural Gas Consumers

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

    565,774 570,797 576,594 583,633 593,286 604,743 1987-2014 Sales 570,797 576,594 583,633 593,286 604,743 1997-2014 Commercial Number of Consumers 55,850 55,853 55,846 55,908 55,997 56,172 1987-2014 Sales 55,776 55,760 55,815 55,902 56,074 1998-2014 Transported 77 86 93 95 98 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 393 432 396 383 426 452 1967-2014 Industrial Number of Consumers 1,358 1,325 1,329 1,435 1,452 1,426 1987-2014 Sales 1,139 1,137 1,215 1,223 1,199 1998-2014

  12. Tennessee Number of Natural Gas Consumers

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

    ,083,573 1,085,387 1,089,009 1,084,726 1,094,122 1,106,681 1987-2014 Sales 1,085,387 1,089,009 1,084,726 1,094,122 1,106,681 1997-2014 Commercial Number of Consumers 127,704 127,914 128,969 130,139 131,091 131,001 1987-2014 Sales 127,806 128,866 130,035 130,989 130,905 1998-2014 Transported 108 103 104 102 96 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 406 439 404 345 411 438 1967-2014 Industrial Number of Consumers 2,717 2,702 2,729 2,679 2,581 2,595 1987-2014 Sales 2,340

  13. Texas Number of Natural Gas Consumers

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

    4,248,613 4,288,495 4,326,156 4,370,057 4,424,103 4,469,282 1987-2014 Sales 4,287,929 4,326,076 4,369,990 4,424,037 4,469,220 1997-2014 Transported 566 80 67 66 62 1997-2014 Commercial Number of Consumers 313,384 312,277 314,041 314,811 314,036 317,217 1987-2014 Sales 310,842 312,164 312,574 311,493 313,971 1998-2014 Transported 1,435 1,877 2,237 2,543 3,246 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 534 605 587 512 553 583 1967-2014 Industrial Number of Consumers 8,581

  14. Kentucky Number of Natural Gas Consumers

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

    754,761 758,129 759,584 757,790 761,575 760,131 1987-2014 Sales 728,940 730,602 730,184 736,011 735,486 1997-2014 Transported 29,189 28,982 27,606 25,564 24,645 1997-2014 Commercial Number of Consumers 83,862 84,707 84,977 85,129 85,999 85,318 1987-2014 Sales 80,541 80,392 80,644 81,579 81,026 1998-2014 Transported 4,166 4,585 4,485 4,420 4,292 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 423 435 407 361 435 469 1967-2014 Industrial Number of Consumers 1,715 1,742 1,705 1,720

  15. Louisiana Number of Natural Gas Consumers

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

    889,570 893,400 897,513 963,688 901,635 899,378 1987-2014 Sales 893,400 897,513 963,688 901,635 899,378 1997-2014 Transported 0 0 0 0 0 1997-2014 Commercial Number of Consumers 58,396 58,562 58,749 63,381 59,147 58,611 1987-2014 Sales 58,501 58,685 63,256 58,985 58,438 1998-2014 Transported 61 64 125 162 173 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 405 461 441 415 488 532 1967-2014 Industrial Number of Consumers 954 942 920 963 916 883 1987-2014 Sales 586 573 628 570 546

  16. Maryland Number of Natural Gas Consumers

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

    067,807 1,071,566 1,077,168 1,078,978 1,099,272 1,101,292 1987-2014 Sales 923,870 892,844 867,627 852,555 858,352 1997-2014 Transported 147,696 184,324 211,351 246,717 242,940 1997-2014 Commercial Number of Consumers 75,771 75,192 75,788 75,799 77,117 77,846 1987-2014 Sales 54,966 53,778 52,383 52,763 53,961 1998-2014 Transported 20,226 22,010 23,416 24,354 23,885 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 912 898 891 846 923 961 1967-2014 Industrial Number of Consumers

  17. Mississippi Number of Natural Gas Consumers

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

    437,715 436,840 442,479 442,840 445,589 444,423 1987-2014 Sales 436,840 439,511 440,171 442,974 444,423 1997-2014 Transported 0 2,968 2,669 2,615 0 2010-2014 Commercial Number of Consumers 50,713 50,537 50,636 50,689 50,153 50,238 1987-2014 Sales 50,503 50,273 50,360 49,829 50,197 1998-2014 Transported 34 363 329 324 41 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 377 419 400 352 388 442 1967-2014 Industrial Number of Consumers 1,141 980 982 936 933 943 1987-2014 Sales 860 853

  18. Missouri Number of Natural Gas Consumers

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

    348,781 1,348,549 1,342,920 1,389,910 1,357,740 1,363,286 1987-2014 Sales 1,348,549 1,342,920 1,389,910 1,357,740 1,363,286 1997-2014 Transported 0 0 0 0 0 2010-2014 Commercial Number of Consumers 140,633 138,670 138,214 144,906 142,495 143,024 1987-2014 Sales 137,342 136,843 143,487 141,047 141,477 1998-2014 Transported 1,328 1,371 1,419 1,448 1,547 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 437 441 451 378 453 510 1967-2014 Industrial Number of Consumers 3,573 3,541 3,307

  19. Montana Number of Natural Gas Consumers

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

    255,472 257,322 259,046 259,957 262,122 265,849 1987-2014 Sales 256,841 258,579 259,484 261,637 265,323 1997-2014 Transported 481 467 473 485 526 2005-2014 Commercial Number of Consumers 33,731 34,002 34,305 34,504 34,909 35,205 1987-2014 Sales 33,652 33,939 33,967 34,305 34,558 1998-2014 Transported 350 366 537 604 647 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 699 602 651 557 601 612 1967-2014 Industrial Number of Consumers 396 384 381 372 372 369 1987-2014 Sales 312 304

  20. Utah Number of Natural Gas Consumers

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

    810,442 821,525 830,219 840,687 854,389 869,052 1987-2014 Sales 821,525 830,219 840,687 854,389 869,052 1997-2014 Commercial Number of Consumers 60,781 61,976 62,885 63,383 64,114 65,134 1987-2014 Sales 61,929 62,831 63,298 63,960 64,931 1998-2014 Transported 47 54 85 154 203 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 609 621 643 558 646 586 1967-2014 Industrial Number of Consumers 293 293 286 302 323 328 1987-2014 Sales 205 189 189 187 178 1998-2014 Transported 88 97 113

  1. Vermont Number of Natural Gas Consumers

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

    37,242 38,047 38,839 39,917 41,152 42,231 1987-2014 Sales 38,047 38,839 39,917 41,152 42,231 1997-2014 Commercial Number of Consumers 5,085 5,137 5,256 5,535 5,441 5,589 1987-2014 Sales 5,137 5,256 5,535 5,441 5,589 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 488 464 472 418 873 864 1967-2014 Industrial Number of Consumers 36 38 36 38 13 13 1987-2014 Sales 37 35 38 13 13 1998-2014 Transported 1 1 0 0 0 1999-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 80,290

  2. Virginia Number of Natural Gas Consumers

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

    1,124,717 1,133,103 1,145,049 1,155,636 1,170,161 1,183,894 1987-2014 Sales 1,076,080 1,081,581 1,088,340 1,102,646 1,114,224 1997-2014 Transported 57,023 63,468 67,296 67,515 69,670 1997-2014 Commercial Number of Consumers 95,704 95,401 96,086 96,503 97,499 98,741 1987-2014 Sales 85,521 85,522 85,595 86,618 87,470 1998-2014 Transported 9,880 10,564 10,908 10,881 11,271 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 707 722 669 624 699 731 1967-2014 Industrial Number of

  3. Washington Number of Natural Gas Consumers

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

    059,239 1,067,979 1,079,277 1,088,762 1,102,318 1,118,193 1987-2014 Sales 1,067,979 1,079,277 1,088,762 1,102,318 1,118,193 1997-2014 Commercial Number of Consumers 98,965 99,231 99,674 100,038 100,939 101,730 1987-2014 Sales 99,166 99,584 99,930 100,819 101,606 1998-2014 Transported 65 90 108 120 124 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 563 517 567 534 553 535 1967-2014 Industrial Number of Consumers 3,428 3,372 3,353 3,338 3,320 3,355 1987-2014 Sales 3,056 3,031

  4. Wisconsin Number of Natural Gas Consumers

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

    656,614 1,663,583 1,671,834 1,681,001 1,692,891 1,705,907 1987-2014 Sales 1,663,583 1,671,834 1,681,001 1,692,891 1,705,907 1997-2014 Transported 0 0 0 0 0 1997-2014 Commercial Number of Consumers 163,843 164,173 165,002 165,657 166,845 167,901 1987-2014 Sales 163,060 163,905 164,575 165,718 166,750 1998-2014 Transported 1,113 1,097 1,082 1,127 1,151 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 558 501 528 465 596 637 1967-2014 Industrial Number of Consumers 6,396 6,413 6,376

  5. Wyoming Number of Natural Gas Consumers

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

    153,062 153,852 155,181 157,226 158,889 160,896 1987-2014 Sales 117,735 118,433 118,691 117,948 118,396 1997-2014 Transported 36,117 36,748 38,535 40,941 42,500 1997-2014 Commercial Number of Consumers 19,843 19,977 20,146 20,387 20,617 20,894 1987-2014 Sales 14,319 14,292 14,187 14,221 14,452 1998-2014 Transported 5,658 5,854 6,200 6,396 6,442 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 523 558 580 514 583 583 1967-2014 Industrial Number of Consumers 130 120 123 127 132 131

  6. Nebraska Number of Natural Gas Consumers

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

    512,551 510,776 514,481 515,338 527,397 522,408 1987-2014 Sales 442,413 446,652 447,617 459,712 454,725 1997-2014 Transported 68,363 67,829 67,721 67,685 67,683 1997-2014 Commercial Number of Consumers 56,454 56,246 56,553 56,608 58,005 57,191 1987-2014 Sales 40,348 40,881 41,074 42,400 41,467 1998-2014 Transported 15,898 15,672 15,534 15,605 15,724 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 563 569 568 468 555 567 1967-2014 Industrial Number of Consumers 7,863 7,912 7,955

  7. Nevada Number of Natural Gas Consumers

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

    760,391 764,435 772,880 782,759 794,150 808,970 1987-2014 Sales 764,435 772,880 782,759 794,150 808,970 1997-2014 Commercial Number of Consumers 41,303 40,801 40,944 41,192 41,710 42,338 1987-2014 Sales 40,655 40,786 41,023 41,536 42,163 1998-2014 Transported 146 158 169 174 175 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 715 722 751 704 748 687 1967-2014 Industrial Number of Consumers 192 184 177 177 195 218 1987-2014 Sales 152 147 146 162 183 1998-2014 Transported 32 30 31

  8. New Hampshire Number of Natural Gas Consumers

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

    96,924 95,361 97,400 99,738 98,715 99,146 1987-2014 Sales 95,360 97,400 99,738 98,715 99,146 1997-2014 Transported 1 0 0 0 0 2010-2014 Commercial Number of Consumers 16,937 16,645 17,186 17,758 17,298 17,421 1987-2014 Sales 15,004 15,198 15,429 14,685 14,527 1998-2014 Transported 1,641 1,988 2,329 2,613 2,894 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 587 505 517 458 532 540 1967-2014 Industrial Number of Consumers 155 306 362 466 403 326 1987-2014 Sales 31 25 30 35 45

  9. New Mexico Number of Natural Gas Consumers

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

    560,479 559,852 570,637 561,713 572,224 614,313 1987-2014 Sales 559,825 570,592 561,652 572,146 614,231 1997-2014 Transported 27 45 61 78 82 1997-2014 Commercial Number of Consumers 48,846 48,757 49,406 48,914 50,163 55,689 1987-2014 Sales 45,679 46,104 45,298 46,348 51,772 1998-2014 Transported 3,078 3,302 3,616 3,815 3,917 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 506 516 507 509 534 461 1967-2014 Industrial Number of Consumers 471 438 360 121 123 116 1987-2014 Sales 390

  10. North Dakota Number of Natural Gas Consumers

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

    22,065 123,585 125,392 130,044 133,975 137,972 1987-2014 Sales 123,585 125,392 130,044 133,975 137,972 1997-2014 Transported 0 0 0 0 0 2004-2014 Commercial Number of Consumers 17,632 17,823 18,421 19,089 19,855 20,687 1987-2014 Sales 17,745 18,347 19,021 19,788 20,623 1998-2014 Transported 78 74 68 67 64 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 623 578 596 543 667 677 1967-2014 Industrial Number of Consumers 279 307 259 260 266 269 1987-2014 Sales 255 204 206 211 210

  11. Oklahoma Number of Natural Gas Consumers

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

    924,745 914,869 922,240 927,346 931,981 937,237 1987-2014 Sales 914,869 922,240 927,346 931,981 937,237 1997-2014 Transported 0 0 0 0 0 1997-2014 Commercial Number of Consumers 94,314 92,430 93,903 94,537 95,385 96,004 1987-2014 Sales 88,217 89,573 90,097 90,861 91,402 1998-2014 Transported 4,213 4,330 4,440 4,524 4,602 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 439 452 430 382 464 489 1967-2014 Industrial Number of Consumers 2,618 2,731 2,733 2,872 2,958 3,063 1987-2014

  12. Oregon Number of Natural Gas Consumers

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

    675,582 682,737 688,681 693,507 700,211 707,010 1987-2014 Sales 682,737 688,681 693,507 700,211 707,010 1997-2014 Commercial Number of Consumers 76,893 77,370 77,822 78,237 79,276 80,480 1987-2014 Sales 77,351 77,793 78,197 79,227 80,422 1998-2014 Transported 19 29 40 49 58 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 387 352 390 368 386 353 1967-2014 Industrial Number of Consumers 1,051 1,053 1,066 1,076 1,085 1,099 1987-2014 Sales 821 828 817 821 839 1998-2014 Transported

  13. Sensitivity in risk analyses with uncertain numbers.

    SciTech Connect (OSTI)

    Tucker, W. Troy; Ferson, Scott

    2006-06-01

    Sensitivity analysis is a study of how changes in the inputs to a model influence the results of the model. Many techniques have recently been proposed for use when the model is probabilistic. This report considers the related problem of sensitivity analysis when the model includes uncertain numbers that can involve both aleatory and epistemic uncertainty and the method of calculation is Dempster-Shafer evidence theory or probability bounds analysis. Some traditional methods for sensitivity analysis generalize directly for use with uncertain numbers, but, in some respects, sensitivity analysis for these analyses differs from traditional deterministic or probabilistic sensitivity analyses. A case study of a dike reliability assessment illustrates several methods of sensitivity analysis, including traditional probabilistic assessment, local derivatives, and a ''pinching'' strategy that hypothetically reduces the epistemic uncertainty or aleatory uncertainty, or both, in an input variable to estimate the reduction of uncertainty in the outputs. The prospects for applying the methods to black box models are also considered.

  14. Colorado Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    ,622,434 1,634,587 1,645,716 1,659,808 1,672,312 1,690,581 1986-2014 Sales 1,634,582 1,645,711 1,659,803 1,672,307 1,690,576 1997-2014 Transported 5 5 5 5 5 1997-2014 Commercial Number of Consumers 145,624 145,460 145,837 145,960 150,145 150,235 1986-2014 Sales 145,236 145,557 145,563 149,826 149,921 1998-2014 Transported 224 280 397 319 314 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 429 396 383 355 392 386 1967-2014 Industrial Number of Consumers 5,084 6,232 6,529 6,906

  15. Delaware Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    9,006 150,458 152,005 153,307 155,627 158,502 1986-2014 Sales 150,458 152,005 153,307 155,627 158,502 1997-2014 Commercial Number of Consumers 12,839 12,861 12,931 12,997 13,163 13,352 1986-2014 Sales 12,706 12,656 12,644 12,777 12,902 1998-2014 Transported 155 275 353 386 450 1999-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 910 948 810 772 849 890 1967-2014 Industrial Number of Consumers 112 114 129 134 138 141 1987-2014 Sales 40 35 29 28 28 1998-2014 Transported 74 94 105 110

  16. Florida Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    674,090 675,551 679,199 686,994 694,210 703,535 1986-2014 Sales 661,768 664,564 672,133 679,191 687,766 1997-2014 Transported 13,783 14,635 14,861 15,019 15,769 1997-2014 Commercial Number of Consumers 59,549 60,854 61,582 63,477 64,772 67,460 1986-2014 Sales 41,750 41,068 41,102 40,434 41,303 1998-2014 Transported 19,104 20,514 22,375 24,338 26,157 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 846 888 869 861 926 929 1967-2014 Industrial Number of Consumers 607 581 630 507 528

  17. Georgia Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    1,744,934 1,740,587 1,740,006 1,739,543 1,805,425 1,755,847 1986-2014 Sales 321,290 321,515 319,179 377,652 315,562 1997-2014 Transported 1,419,297 1,418,491 1,420,364 1,427,773 1,440,285 1997-2014 Commercial Number of Consumers 127,347 124,759 123,454 121,243 126,060 122,573 1986-2014 Sales 32,318 32,162 31,755 36,556 31,845 1998-2014 Transported 92,441 91,292 89,488 89,504 90,728 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 421 482 458 428 454 482 1967-2014 Industrial Number

  18. Hawaii Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    25,466 25,389 25,305 25,184 26,374 28,919 1987-2014 Sales 25,389 25,305 25,184 26,374 28,919 1998-2014 Commercial Number of Consumers 2,535 2,551 2,560 2,545 2,627 2,789 1987-2014 Sales 2,551 2,560 2,545 2,627 2,789 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 691 697 691 727 713 692 1980-2014 Industrial Number of Consumers 25 24 24 22 22 23 1997-2014 Sales 24 24 22 22 23 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 13,753 14,111 15,087 16,126 17,635 17,

  19. Idaho Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    42,277 346,602 350,871 353,963 359,889 367,394 1987-2014 Sales 346,602 350,871 353,963 359,889 367,394 1997-2014 Commercial Number of Consumers 38,245 38,506 38,912 39,202 39,722 40,229 1987-2014 Sales 38,468 38,872 39,160 39,681 40,188 1998-2014 Transported 38 40 42 41 41 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 412 390 433 404 465 422 1967-2014 Industrial Number of Consumers 187 184 178 179 183 189 1987-2014 Sales 108 103 105 109 115 1998-2014 Transported 76 75 74 74 74

  20. Iowa Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    875,781 879,713 883,733 892,123 895,414 900,420 1987-2014 Sales 879,713 883,733 892,123 895,414 900,420 1997-2014 Commercial Number of Consumers 98,416 98,396 98,541 99,113 99,017 99,182 1987-2014 Sales 96,996 97,075 97,580 97,334 97,409 1998-2014 Transported 1,400 1,466 1,533 1,683 1,773 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 576 525 526 442 572 579 1967-2014 Industrial Number of Consumers 1,626 1,528 1,465 1,469 1,491 1,572 1987-2014 Sales 1,161 1,110 1,042 1,074 1,135

  1. Kansas Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    855,454 853,842 854,730 854,800 858,572 861,092 1987-2014 Sales 853,842 854,730 854,779 858,546 861,066 1997-2014 Transported 0 0 21 26 26 2004-2014 Commercial Number of Consumers 84,715 84,446 84,874 84,673 84,969 85,867 1987-2014 Sales 78,310 78,559 78,230 78,441 79,231 1998-2014 Transported 6,136 6,315 6,443 6,528 6,636 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 384 377 378 301 391 425 1967-2014 Industrial Number of Consumers 7,793 7,664 7,954 7,970 7,877 7,429 1987-2014

  2. Alabama Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    785,005 778,985 772,892 767,396 765,957 769,418 1986-2014 Sales 778,985 772,892 767,396 765,957 769,418 1997-2014 Transported 0 0 0 0 0 1997-2014 Commercial Number of Consumers 67,674 68,163 67,696 67,252 67,136 67,806 1986-2014 Sales 68,017 67,561 67,117 67,006 67,677 1998-2014 Transported 146 135 135 130 129 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 359 397 371 320 377 406 1967-2014 Industrial Number of Consumers 3,057 3,039 2,988 3,045 3,143 3,244 1986-2014 Sales 2,758

  3. Alaska Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    120,124 121,166 121,736 122,983 124,411 126,416 1986-2014 Sales 121,166 121,736 122,983 124,411 126,416 1997-2014 Commercial Number of Consumers 13,215 12,998 13,027 13,133 13,246 13,399 1986-2014 Sales 12,673 12,724 13,072 13,184 13,336 1998-2014 Transported 325 303 61 62 63 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 1,258 1,225 1,489 1,515 1,411 1,338 1967-2014 Industrial Number of Consumers 3 3 5 3 3 1 1987-2014 Sales 2 2 3 2 1 1998-2014 Transported 1 3 0 1 0 1998-2014

  4. Arizona Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    ,130,047 1,138,448 1,146,286 1,157,688 1,172,003 1,186,794 1986-2014 Sales 1,138,448 1,146,280 1,157,682 1,171,997 1,186,788 1997-2014 Transported 0 6 6 6 6 1997-2014 Commercial Number of Consumers 57,191 56,676 56,547 56,532 56,585 56,649 1986-2014 Sales 56,510 56,349 56,252 56,270 56,331 1998-2014 Transported 166 198 280 315 318 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 563 564 577 558 581 538 1967-2014 Industrial Number of Consumers 390 368 371 379 383 386 1987-2014

  5. Arkansas Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    557,355 549,970 551,795 549,959 549,764 549,034 1986-2014 Sales 549,970 551,795 549,959 549,764 549,034 1997-2014 Commercial Number of Consumers 69,043 67,987 67,815 68,765 68,791 69,011 1986-2014 Sales 67,676 67,454 68,151 68,127 68,291 1998-2014 Transported 311 361 614 664 720 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 527 592 590 603 692 734 1967-2014 Industrial Number of Consumers 1,025 1,079 1,133 990 1,020 1,009 1986-2014 Sales 580 554 523 513 531 1998-2014 Transported

  6. Parameters affecting the resilience of scale-free networks to random failures.

    SciTech Connect (OSTI)

    Link, Hamilton E.; LaViolette, Randall A.; Lane, Terran; Saia, Jared

    2005-09-01

    It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. in (1) study scale-free networks including some which approximate the measured degree distribution of the Internet. Their results suggest that if each node in this network failed independently with probability 0.99, most of the remaining nodes would still be connected in a giant component. In this paper, we show that a large and important subclass of scale-free networks are not robust to massive numbers of random node deletions. In particular, we study scale-free networks which have minimum node degree of 1 and a power-law degree distribution beginning with nodes of degree 1 (power-law networks). We show that, in a power-law network approximating the Internet's reported distribution, when the probability of deletion of each node is 0.5 only about 25% of the surviving nodes in the network remain connected in a giant component, and the giant component does not persist beyond a critical failure rate of 0.9. The new result is partially due to improved analytical accommodation of the large number of degree-0 nodes that result after node deletions. Our results apply to power-law networks with a wide range of power-law exponents, including Internet-like networks. We give both analytical and empirical evidence that such networks are not generally robust to massive random node deletions.

  7. Volume, Number of Shipments Surpass Goals

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

    shatters records in first year of accelerated shipping effort October 3, 2012 Los Alamos National Laboratory shatters records in first year of accelerated shipping effort Volume, Number of Shipments Surpass Goals LOS ALAMOS, NEW MEXICO, October 3, 2012-In the first year of an effort to accelerate shipments of transuranic (TRU) waste to the Waste Isolation Pilot Plant (WIPP), Los Alamos National Laboratory shattered its own record with 59 more shipments than planned, and became one of the largest

  8. Low Mach Number Models in Computational Astrophysics

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

    In memoriam: Michael Welcome 1957 - 2014 RIP Almgren CCSE Low Mach Number Models in Computational Astrophysics Ann Almgren Center for Computational Sciences and Engineering Lawrence Berkeley National Laboratory NUG 2014: NERSC@40 February 4, 2014 Collaborators: John Bell, Chris Malone, Andy Nonaka, Stan Woosley, Michael Zingale Almgren CCSE Introduction We often associate astrophysics with explosive phenomena: novae supernovae gamma-ray bursts X-ray bursts Type Ia Supernovae Largest

  9. Notices Total Estimated Number of Annual

    Energy Savers [EERE]

    372 Federal Register / Vol. 78, No. 181 / Wednesday, September 18, 2013 / Notices Total Estimated Number of Annual Burden Hours: 10,128. Abstract: Enrollment in the Federal Student Aid (FSA) Student Aid Internet Gateway (SAIG) allows eligible entities to securely exchange Title IV, Higher Education Act (HEA) assistance programs data electronically with the Department of Education processors. Organizations establish Destination Point Administrators (DPAs) to transmit, receive, view and update

  10. Stockpile Stewardship Quarterly, Volume 2, Number 1

    National Nuclear Security Administration (NNSA)

    1 * May 2012 Message from the Assistant Deputy Administrator for Stockpile Stewardship, Chris Deeney Defense Programs Stockpile Stewardship in Action Volume 2, Number 1 Inside this Issue 2 LANL and ANL Complete Groundbreaking Shock Experiments at the Advanced Photon Source 3 Characterization of Activity-Size-Distribution of Nuclear Fallout 5 Modeling Mix in High-Energy-Density Plasma 6 Quality Input for Microscopic Fission Theory 8 Fiber Reinforced Composites Under Pressure: A Case Study in

  11. U.S. Natural Gas Number of Underground Storage Acquifers Capacity (Number

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

    of Elements) Acquifers Capacity (Number of Elements) U.S. Natural Gas Number of Underground Storage Acquifers Capacity (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 49 2000's 49 39 38 43 43 44 44 43 43 43 2010's 43 43 44 47 46 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016 Referring Pages: Number of

  12. Random unitary maps for quantum state reconstruction

    SciTech Connect (OSTI)

    Merkel, Seth T. [Institute for Quantum Computing, Waterloo, Ontario N2L 3G1 (Canada); Riofrio, Carlos A.; Deutsch, Ivan H. [Center for Quantum Information and Control (CQuIC), Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico, 87131 (United States); Flammia, Steven T. [Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5 (Canada); Kavli Institute for Theoretical Physics, University of California, Santa Barbara, California 93106 (United States)

    2010-03-15

    We study the possibility of performing quantum state reconstruction from a measurement record that is obtained as a sequence of expectation values of a Hermitian operator evolving under repeated application of a single random unitary map, U{sub 0}. We show that while this single-parameter orbit in operator space is not informationally complete, it can be used to yield surprisingly high-fidelity reconstruction. For a d-dimensional Hilbert space with the initial observable in su(d), the measurement record lacks information about a matrix subspace of dimension {>=}d-2 out of the total dimension d{sup 2}-1. We determine the conditions on U{sub 0} such that the bound is saturated, and show they are achieved by almost all pseudorandom unitary matrices. When we further impose the constraint that the physical density matrix must be positive, we obtain even higher fidelity than that predicted from the missing subspace. With prior knowledge that the state is pure, the reconstruction will be perfect (in the limit of vanishing noise) and for arbitrary mixed states, the fidelity is over 0.96, even for small d, and reaching F>0.99 for d>9. We also study the implementation of this protocol based on the relationship between random matrices and quantum chaos. We show that the Floquet operator of the quantum kicked top provides a means of generating the required type of measurement record, with implications on the relationship between quantum chaos and information gain.

  13. Methods and optical fibers that decrease pulse degradation resulting from random chromatic dispersion

    DOE Patents [OSTI]

    Chertkov, Michael; Gabitov, Ildar

    2004-03-02

    The present invention provides methods and optical fibers for periodically pinning an actual (random) accumulated chromatic dispersion of an optical fiber to a predicted accumulated dispersion of the fiber through relatively simple modifications of fiber-optic manufacturing methods or retrofitting of existing fibers. If the pinning occurs with sufficient frequency (at a distance less than or are equal to a correlation scale), pulse degradation resulting from random chromatic dispersion is minimized. Alternatively, pinning may occur quasi-periodically, i.e., the pinning distance is distributed between approximately zero and approximately two to three times the correlation scale.

  14. Compare Activities by Number of Employees

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

    this page, please call 202-586-8800. Employees per Building by Building Type Inpatient health care buildings averaged six times more employees per building than any other building...

  15. Low-temperature random matrix theory at the soft edge

    SciTech Connect (OSTI)

    Edelman, Alan; Persson, Per-Olof; Sutton, Brian D.

    2014-06-15

    Low temperature random matrix theory is the study of random eigenvalues as energy is removed. In standard notation, ? is identified with inverse temperature, and low temperatures are achieved through the limit ? ? ?. In this paper, we derive statistics for low-temperature random matrices at the soft edge, which describes the extreme eigenvalues for many random matrix distributions. Specifically, new asymptotics are found for the expected value and standard deviation of the general-? Tracy-Widom distribution. The new techniques utilize beta ensembles, stochastic differential operators, and Riccati diffusions. The asymptotics fit known high-temperature statistics curiously well and contribute to the larger program of general-? random matrix theory.

  16. Total number of longwall faces drops below 50

    SciTech Connect (OSTI)

    Fiscor, S.

    2009-02-15

    For the first time since Coal Age began its annual Longwall Census the number of faces has dropped below 50. A total of five mines operate two longwall faces. CONSOL Energy remains the leader with 12 faces. Arch Coal operates five longwall mines; Robert E. Murray owns five longwall mines. West Virginia has 13 longwalls, followed by Pennsylvania (8), Utah (6) and Alabama (6). A detailed table gives for each longwall installation, the ownership, seam height, cutting height, panel width and length, overburden, number of gate entries, depth of cut, model of equipment used (shearer, haulage system, roof support, face conveyor, stage loader, crusher, electrical controls and voltage to face). 2 tabs., 1 photo.

  17. Property:Number of Plants Included in Planned Estimate | Open...

    Open Energy Info (EERE)

    Number of Plants Included in Planned Estimate Jump to: navigation, search Property Name Number of Plants Included in Planned Estimate Property Type String Description Number of...

  18. Property:NumberOfLEDSTools | Open Energy Information

    Open Energy Info (EERE)

    Name NumberOfLEDSTools Property Type Number Retrieved from "http:en.openei.orgwindex.php?titleProperty:NumberOfLEDSTools&oldid322418" Feedback Contact needs updating Image...

  19. Property:Number of Color Cameras | Open Energy Information

    Open Energy Info (EERE)

    Color Cameras Jump to: navigation, search Property Name Number of Color Cameras Property Type Number Pages using the property "Number of Color Cameras" Showing 25 pages using this...

  20. Health Code Number (HCN) Development Procedure

    SciTech Connect (OSTI)

    Petrocchi, Rocky; Craig, Douglas K.; Bond, Jayne-Anne; Trott, Donna M.; Yu, Xiao-Ying

    2013-09-01

    This report provides the detailed description of health code numbers (HCNs) and the procedure of how each HCN is assigned. It contains many guidelines and rationales of HCNs. HCNs are used in the chemical mixture methodology (CMM), a method recommended by the department of energy (DOE) for assessing health effects as a result of exposures to airborne aerosols in an emergency. The procedure is a useful tool for proficient HCN code developers. Intense training and quality assurance with qualified HCN developers are required before an individual comprehends the procedure to develop HCNs for DOE.

  1. The numbers will follow | Jefferson Lab

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

    The numbers will follow September 26, 2008 As all of you well know, the safety performance of Jefferson Lab, our laboratory, has been nothing short of stellar over the past couple of years. To cap it all, you were subjected to what is usually rated as the toughest of the sit-down examinations, the HSS audit. Not only did you exceed expectations, but you did so by a large margin. A basis for this great result, as documented by the HSS team, was the engagement and commitment of the workforce, the

  2. Mo Year Report Period: EIA ID NUMBER:

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

    Mo Year Report Period: EIA ID NUMBER: http://www.eia.gov/survey/form/eia_14/instructions.pdf Mailing Address: Secure File Transfer option available at: (e.g., PO Box, RR) https://signon.eia.doe.gov/upload/noticeoog.jsp Electronic Transmission: The PC Electronic Zip Code - Data Reporting Option (PEDRO) is available. If interested in software, call (202) 586-9659. Email form to: OOG.SURVEYS@eia.doe.gov - - - - Fax form to: (202) 586-9772 Mail form to: Oil & Gas Survey Email address: U.S.

  3. Experimental Stations by Number | Stanford Synchrotron Radiation

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

    Lightsource Experimental Stations by Number Beam Line by Techniques Photon Source Parameters Station Type Techniques Energy Range Contact Person Experimental Station 1-5 X-ray Materials Small-angle X-ray Scattering (SAXS) focused 4600-16000 eV Christopher J. Tassone Tim J. Dunn Experimental Station 2-1 X-ray Powder diffraction Thin film diffraction Focused 5000 - 14500 eV Apurva Mehta Charles Troxel Jr Experimental Station 2-2 X-ray X-ray Absorption Spectroscopy 1000-40000 eV Ryan Davis

  4. OMB Control Number: 1910-5165

    Energy Savers [EERE]

    OMB Control Number: 1910-5165 Expires: xx/xx/201x SEMI-ANNUAL DAVIS-BACON ENFORCEMENT REPORT Please submit this Semi-Annual Davis-Bacon Enforcement Report to your site DOE/NNSA Contractor Human Resource Division (CHRD) Office. If you do not have a DOE/NNSA CHRD Office, please submit the report to: DBAEnforcementReports@hq.doe.gov. The following questions regarding enforcement activity (Davis-Bacon and Related Acts) by this Agency are required by 29 CFR, Part 5.7(b), and Department of Labor, All

  5. What's Behind the Numbers? | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    What's Behind the Numbers? Dr. Richard Newell Dr. Richard Newell What does this mean for me? New website shows data on the why's, when's and how's of crude oil prices. Among the most visible prices that consumers may see on a daily basis are the ones found on the large signs at the gasoline stations alongside our streets and highways. The biggest single factor affecting gasoline prices is the cost of crude oil, the main raw material for gasoline production, which accounts for well over half the

  6. On the mixing time of geographical threshold graphs

    SciTech Connect (OSTI)

    Bradonjic, Milan

    2009-01-01

    In this paper, we study the mixing time of random graphs generated by the geographical threshold graph (GTG) model, a generalization of random geometric graphs (RGG). In a GTG, nodes are distributed in a Euclidean space, and edges are assigned according to a threshold function involving the distance between nodes as well as randomly chosen node weights. The motivation for analyzing this model is that many real networks (e.g., wireless networks, the Internet, etc.) need to be studied by using a 'richer' stochastic model (which in this case includes both a distance between nodes and weights on the nodes). We specifically study the mixing times of random walks on 2-dimensional GTGs near the connectivity threshold. We provide a set of criteria on the distribution of vertex weights that guarantees that the mixing time is {Theta}(n log n).

  7. MENTOR QUESTIONNAIRE Name: Title: Email: Office Phone Number:

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

    MENTOR QUESTIONNAIRE Name: Title: Email: Office Phone Number: Office Address: is interested in this program because: Are you willing to act as a mentor for ? Yes No Expectations of the Mentoring Program How long? 6-months minimum commitment. Are you willing to commit to the 6-months minimum timeframe? Yes No How much time? You decide with your mentee; 1-4 hours/month is recommended. Please return completed form to Ames Lab Human Resources, 105 TASF. Are you willing to commit 1-4 hours per month

  8. Arizona Natural Gas Number of Gas and Gas Condensate Wells (Number of

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

    Elements) Gas and Gas Condensate Wells (Number of Elements) Arizona Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3 1990's 5 6 6 6 6 7 7 8 8 8 2000's 9 8 7 9 6 6 7 7 6 6 2010's 5 5 5 5 5 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date: 3/31/2016 Referring Pages:

  9. Michigan Number of Natural Gas Consumers

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

    3,169,026 3,152,468 3,153,895 3,161,033 3,180,349 3,192,807 1987-2014 Sales 2,952,550 2,946,507 2,939,693 2,950,315 2,985,315 1997-2014 Transported 199,918 207,388 221,340 230,034 207,492 1997-2014 Commercial Number of Consumers 252,017 249,309 249,456 249,994 250,994 253,127 1987-2014 Sales 217,325 213,995 212,411 213,532 219,240 1998-2014 Transported 31,984 35,461 37,583 37,462 33,887 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 649 611 656 578 683 736 1967-2014 Industrial

  10. New Jersey Number of Natural Gas Consumers

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

    2,635,324 2,649,282 2,659,205 2,671,308 2,686,452 2,705,274 1987-2014 Sales 2,556,514 2,514,492 2,467,520 2,428,664 2,482,281 1997-2014 Transported 92,768 144,713 203,788 257,788 222,993 1997-2014 Commercial Number of Consumers 234,125 234,158 234,721 237,602 236,746 240,083 1987-2014 Sales 200,680 196,963 192,913 185,030 186,591 1998-2014 Transported 33,478 37,758 44,689 51,716 53,492 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 771 775 817 735 726 842 1967-2014 Industrial

  11. Ohio Number of Natural Gas Consumers

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

    3,253,184 3,240,619 3,236,160 3,244,274 3,271,074 3,283,869 1987-2014 Sales 1,418,217 1,352,292 855,055 636,744 664,015 1997-2014 Transported 1,822,402 1,883,868 2,389,219 2,634,330 2,619,854 1997-2014 Commercial Number of Consumers 270,596 268,346 268,647 267,793 269,081 269,758 1987-2014 Sales 92,621 85,877 51,308 35,966 37,035 1998-2014 Transported 175,725 182,770 216,485 233,115 232,723 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 594 583 601 543 625 679 1967-2014

  12. California Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    0,510,950 10,542,584 10,625,190 10,681,916 10,754,908 10,781,720 1986-2014 Sales 10,469,734 10,545,585 10,547,706 10,471,814 10,372,973 1997-2014 Transported 72,850 79,605 134,210 283,094 408,747 1997-2014 Commercial Number of Consumers 441,806 439,572 440,990 442,708 444,342 443,115 1986-2014 Sales 399,290 390,547 387,760 387,806 385,878 1998-2014 Transported 40,282 50,443 54,948 56,536 57,237 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 561 564 558 572 574 536 1967-2014

  13. Illinois Number of Natural Gas Consumers

    Gasoline and Diesel Fuel Update (EIA)

    ,839,438 3,842,206 3,855,942 3,878,806 3,838,120 3,868,501 1987-2014 Sales 3,568,120 3,594,047 3,605,796 3,550,217 3,570,339 1997-2014 Transported 274,086 261,895 273,010 287,903 298,162 1997-2014 Commercial Number of Consumers 294,226 291,395 293,213 297,523 282,743 294,391 1987-2014 Sales 240,197 241,582 244,480 225,913 235,097 1998-2014 Transported 51,198 51,631 53,043 56,830 59,294 1998-2014 Average Consumption per Consumer (Thousand Cubic Ft.) 757 680 735 632 816 837 1967-2014 Industrial

  14. Contractor: Contract Number: Contract Type: Total Estimated

    Office of Environmental Management (EM)

    Contract Number: Contract Type: Total Estimated Contract Cost: Performance Period Total Fee Paid FY2004 $294,316 FY2005 $820,074 FY2006 $799,449 FY2007 $877,898 FY2008 $866,608 FY2009 $886,404 FY2010 $800,314 FY2011 $871,280 FY2012 $824,517 FY2013 Cumulative Fee Paid $7,040,860 $820,074 $799,449 $877,898 $916,130 $886,608 Computer Sciences Corporation DE-AC06-04RL14383 $895,358 $899,230 $907,583 Cost Plus Award Fee $134,100,336 $8,221,404 Fee Available Contract Period: Fee Information Minimum

  15. Dynamics of dispersive photon-number QND measurements in a micromaser

    SciTech Connect (OSTI)

    Kozlovskii, A. V. [Russian Academy of Sciences, Lebedev Physical Institute (Russian Federation)], E-mail: kozlovsk@sci.lebedev.ru

    2007-04-15

    A numerical analysis of dispersive quantum nondemolition measurement of the photon number of a microwave cavity field is presented. Simulations show that a key property of the dispersive atom-field interaction used in Ramsey interferometry is the extremely high sensitivity of the dynamics of atomic and field states to basic parameters of the system. When a monokinetic atomic beam is sent through a microwave cavity, a qualitative change in the field state can be caused by an uncontrollably small deviation of parameters (such as atom path length through the cavity, atom velocity, cavity mode frequency detuning, or atom-field coupling constants). The resulting cavity field can be either in a Fock state or in a super-Poissonian state (characterized by a large photon-number variance). When the atoms have a random velocity spread, the field is squeezed to a Fock state for arbitrary values of the system's parameters. However, this makes detection of Ramsey fringes impossible, because the probability of detecting an atom in the upper or lower electronic state becomes a random quantity almost uniformly distributed over the interval between zero and unity, irrespective of the cavity photon number.

  16. Effects of a random spatial variation of the plasma density on the mode conversion in cold, unmagnetized, and stratified plasmas

    SciTech Connect (OSTI)

    Jung Yu, Dae; Kim, Kihong

    2013-12-15

    We study the effects of a random spatial variation of the plasma density on the mode conversion of electromagnetic waves into electrostatic oscillations in cold, unmagnetized, and stratified plasmas. Using the invariant imbedding method, we calculate precisely the electromagnetic field distribution and the mode conversion coefficient, which is defined to be the fraction of the incident wave power converted into electrostatic oscillations, for the configuration where a numerically generated random density variation is added to the background linear density profile. We repeat similar calculations for a large number of random configurations and take an average of the results. We obtain a peculiar nonmonotonic dependence of the mode conversion coefficient on the strength of randomness. As the disorder increases from zero, the maximum value of the mode conversion coefficient decreases initially, then increases to a maximum, and finally decreases towards zero. The range of the incident angle in which mode conversion occurs increases monotonically as the disorder increases. We present numerical results suggesting that the decrease of mode conversion mainly results from the increased reflection due to the Anderson localization effect originating from disorder, whereas the increase of mode conversion of the intermediate disorder regime comes from the appearance of many resonance points and the enhanced tunneling between the resonance points and the cutoff point. We also find a very large local enhancement of the magnetic field intensity for particular random configurations. In order to obtain high mode conversion efficiency, it is desirable to restrict the randomness close to the resonance region.

  17. Listening to the noise: random fluctuations reveal gene network parameters

    Office of Scientific and Technical Information (OSTI)

    (Journal Article) | SciTech Connect Journal Article: Listening to the noise: random fluctuations reveal gene network parameters Citation Details In-Document Search Title: Listening to the noise: random fluctuations reveal gene network parameters The cellular environment is abuzz with noise. The origin of this noise is attributed to the inherent random motion of reacting molecules that take part in gene expression and post expression interactions. In this noisy environment, clonal populations

  18. Spin-Hall-assisted magnetic random access memory (Journal Article) |

    Office of Scientific and Technical Information (OSTI)

    SciTech Connect Spin-Hall-assisted magnetic random access memory Citation Details In-Document Search Title: Spin-Hall-assisted magnetic random access memory We propose a write scheme for perpendicular spin-transfer torque magnetoresistive random-access memory that significantly reduces the required tunnel current density and write energy. A sub-nanosecond in-plane polarized spin current pulse is generated using the spin-Hall effect, disturbing the stable magnetic state. Subsequent switching

  19. Random-matrix approach to the statistical compound nuclear reaction...

    Office of Scientific and Technical Information (OSTI)

    nuclear reaction at low energies using the Monte-Carlo technique Citation Details In-Document Search Title: Random-matrix approach to the statistical compound nuclear ...

  20. Synthesis, Structure, and Properties of Alternating and Random...

    Office of Scientific and Technical Information (OSTI)

    Synthesis, Structure, and Properties of Alternating and Random Poly(styrene-b-butadiene) Multiblock Copolymers Citation Details In-Document Search Title: Synthesis, Structure, and...

  1. DOE Contract Number DE-AC05-060R231

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

    DOE Contract Number DE-AC05-060R231 00 Modification M045 Oak Ridge Associated Universities Page 2 of2 14. Description of AmendmenVModification: The purpose of this modification is to accomplish the following: 1) Advise you of changes in 10 CFR 707 which is invoked by DEAR 970.5223-4: 1) the definition of Testing Designated Positions (TOPs) now includes all contractor personnel with security clearances; 2) the percent of personnel to be randomly tested on an annual basis has been decreased from

  2. Fact #874: May 25, 2015 Number of Electric Stations and Electric...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    fuel (10,710 stations). The number of charging units is of particular importance for electric vehicles due to the length of time it takes vehicles to charge compared to other...

  3. Fact #874: May 25, 2015 Number of Electric Stations and Electric Charging Units Increasing

    Broader source: Energy.gov [DOE]

    There are more electric stations than any other alternative fuel (10,710 stations). The number of charging units is of particular importance for electric vehicles due to the length of time it takes...

  4. Detailed Chemical Kinetic Reaction Mechanisms for Primary Reference Fuels for Diesel Cetane Number and Spark-Ignition Octane Number

    SciTech Connect (OSTI)

    Westbrook, C K; Pitz, W J; Mehl, M; Curran, H J

    2010-03-03

    For the first time, a detailed chemical kinetic reaction mechanism is developed for primary reference fuel mixtures of n-hexadecane and 2,2,4,4,6,8,8-heptamethyl nonane for diesel cetane ratings. The mechanisms are constructed using existing rules for reaction pathways and rate expressions developed previously for the primary reference fuels for gasoline octane ratings, n-heptane and iso-octane. These reaction mechanisms are validated by comparisons between computed and experimental results for shock tube ignition and for oxidation under jet-stirred reactor conditions. The combined kinetic reaction mechanism contains the submechanisms for the primary reference fuels for diesel cetane ratings and submechanisms for the primary reference fuels for gasoline octane ratings, all in one integrated large kinetic reaction mechanism. Representative applications of this mechanism to two test problems are presented, one describing fuel/air autoignition variations with changes in fuel cetane numbers, and the other describing fuel combustion in a jet-stirred reactor environment with the fuel varying from pure 2,2,4,4,6,8,8-heptamethyl nonane (Cetane number of 15) to pure n-hexadecane (Cetane number of 100). The final reaction mechanism for the primary reference fuels for diesel fuel and gasoline is available on the web.

  5. U.S. Natural Gas Number of Commercial Consumers - Sales (Number of

    Gasoline and Diesel Fuel Update (EIA)

    Elements) - Sales (Number of Elements) U.S. Natural Gas Number of Commercial Consumers - Sales (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 4,823,842 4,599,494 2000's 4,576,873 4,532,034 4,588,964 4,662,853 4,644,363 4,698,626 4,733,822 2010's 4,584,884 4,556,220 4,518,745 4,491,326 4,533,729 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  6. U.S. Natural Gas Number of Commercial Consumers - Transported (Number of

    Gasoline and Diesel Fuel Update (EIA)

    Elements) Transported (Number of Elements) U.S. Natural Gas Number of Commercial Consumers - Transported (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 220,655 410,695 2000's 433,944 464,412 475,420 489,324 495,586 499,402 539,557 2010's 716,692 763,597 837,652 881,196 885,257 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release

  7. U.S. Natural Gas Number of Industrial Consumers - Sales (Number of

    Gasoline and Diesel Fuel Update (EIA)

    Elements) Sales (Number of Elements) U.S. Natural Gas Number of Industrial Consumers - Sales (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 182,424 157,050 2000's 157,806 152,974 143,177 142,816 151,386 146,450 135,070 2010's 129,119 124,552 121,821 123,124 122,182 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  8. U.S. Natural Gas Number of Industrial Consumers - Transported (Number of

    Gasoline and Diesel Fuel Update (EIA)

    Elements) Transported (Number of Elements) U.S. Natural Gas Number of Industrial Consumers - Transported (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 49,014 71,281 2000's 75,826 64,052 62,738 62,698 57,672 59,773 58,760 2010's 63,611 64,749 67,551 69,164 69,953 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 2/29/2016 Next Release Date:

  9. U.S. Natural Gas Number of Residential Consumers - Sales (Number of

    Gasoline and Diesel Fuel Update (EIA)

    Elements) Sales (Number of Elements) U.S. Natural Gas Number of Residential Consumers - Sales (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 55,934,175 56,520,482 56,023,710 2000's 56,261,031 56,710,548 57,267,445 57,815,669 58,524,797 59,787,524 60,129,047 2010's 60,267,648 60,408,842 60,010,723 59,877,464 60,222,681 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  10. U.S. Natural Gas Number of Residential Consumers - Transported (Number of

    Gasoline and Diesel Fuel Update (EIA)

    Elements) Transported (Number of Elements) U.S. Natural Gas Number of Residential Consumers - Transported (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 252,783 801,264 2,199,519 2000's 2,978,319 3,576,181 3,839,809 4,055,781 3,971,337 3,829,303 4,037,233 2010's 5,274,697 5,531,680 6,364,411 6,934,929 7,005,081 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  11. Montana Natural Gas Number of Gas and Gas Condensate Wells (Number of

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

    Elements) Gas and Gas Condensate Wells (Number of Elements) Montana Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2,700 1990's 2,607 2,802 2,890 3,075 2,940 2,918 2,990 3,071 3,423 3,634 2000's 3,321 4,331 4,544 4,539 4,971 5,751 6,578 6,925 7,095 7,031 2010's 6,059 6,477 6,240 5,754 5,754 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure

  12. New Jersey Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) New Jersey Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 200,387 206,261 212,496 1990's 217,548 215,408 212,726 215,948 219,061 222,632 224,749 226,714 234,459 232,831 2000's 243,541 212,726 214,526 223,564 223,595 226,007 227,819 230,855 229,235 234,125 2010's 234,158 234,721 237,602 236,746 240,083 - = No Data Reported; -- = Not Applicable; NA = Not

  13. New Jersey Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) New Jersey Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 6,265 6,123 6,079 1990's 5,976 8,444 11,474 11,224 10,608 10,362 10,139 17,625 16,282 10,089 2000's 9,686 9,247 8,473 9,027 8,947 8,500 8,245 8,036 7,680 7,871 2010's 7,505 7,391 7,290 7,216 7,157 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  14. New Jersey Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) New Jersey Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,869,903 1,918,185 1,950,165 1990's 1,982,136 2,005,020 2,032,115 2,060,511 2,089,911 2,123,323 2,147,622 2,193,629 2,252,248 2,245,904 2000's 2,364,058 2,466,771 2,434,533 2,562,856 2,582,714 2,540,283 2,578,191 2,609,788 2,601,051 2,635,324 2010's 2,649,282 2,659,205 2,671,308 2,686,452

  15. New Mexico Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) New Mexico Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 36,444 36,940 36,960 1990's 38,026 38,622 40,312 40,166 39,846 38,099 37,796 38,918 42,067 43,834 2000's 44,164 44,306 45,469 45,491 45,961 47,745 47,233 48,047 49,235 48,846 2010's 48,757 49,406 48,914 50,163 55,689 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to

  16. New Mexico Natural Gas Number of Gas and Gas Condensate Wells (Number of

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

    Elements) Gas and Gas Condensate Wells (Number of Elements) New Mexico Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 17,087 1990's 17,124 20,021 18,040 20,846 23,292 23,510 24,134 27,421 28,200 26,007 2000's 33,948 35,217 35,873 37,100 38,574 40,157 41,634 42,644 44,241 44,784 2010's 44,748 32,302 28,206 27,073 27,957 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  17. New Mexico Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) New Mexico Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,703 1,668 1,653 1990's 1,407 1,337 141 152 1,097 1,065 1,365 1,366 1,549 1,482 2000's 1,517 1,875 1,356 1,270 1,164 988 1,062 470 383 471 2010's 438 360 121 123 116 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  18. New Mexico Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) New Mexico Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 348,759 356,192 361,521 1990's 369,451 379,472 389,063 397,681 409,095 421,896 428,621 443,167 454,065 473,375 2000's 479,894 485,969 496,577 498,852 509,119 530,277 533,971 547,512 556,905 560,479 2010's 559,852 570,637 561,713 572,224 614,313 - = No Data Reported; -- = Not Applicable; NA = Not

  19. New York Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) New York Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 23,276 24,654 27,426 1990's 25,008 28,837 28,198 23,833 21,833 22,484 15,300 23,099 5,294 6,136 2000's 6,553 6,501 3,068 2,984 2,963 3,752 3,642 7,484 7,080 6,634 2010's 6,236 6,609 5,910 6,311 6,313 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  20. U.S. Natural Gas Number of Commercial Consumers (Number of Elements)

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

    Commercial Consumers (Number of Elements) U.S. Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 4,013,040 4,124,745 4,168,048 1990's 4,236,280 4,357,252 4,409,699 4,464,906 4,533,905 4,636,500 4,720,227 4,761,409 5,044,497 5,010,189 2000's 5,010,817 4,996,446 5,064,384 5,152,177 5,139,949 5,198,028 5,273,379 5,308,785 5,444,335 5,322,332 2010's 5,301,576 5,319,817 5,356,397 5,372,522 5,418,986 - =

  1. U.S. Natural Gas Number of Industrial Consumers (Number of Elements)

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

    Industrial Consumers (Number of Elements) U.S. Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 195,544 199,041 225,346 1990's 218,341 216,529 209,616 209,666 202,940 209,398 206,049 234,855 226,191 228,331 2000's 220,251 217,026 205,915 205,514 209,058 206,223 193,830 198,289 225,044 207,624 2010's 192,730 189,301 189,372 192,288 192,135 - = No Data Reported; -- = Not Applicable; NA = Not

  2. U.S. Natural Gas Number of Residential Consumers (Number of Elements)

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

    Residential Consumers (Number of Elements) U.S. Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 47,710,444 48,474,449 49,309,593 1990's 50,187,178 51,593,206 52,331,397 52,535,411 53,392,557 54,322,179 55,263,673 56,186,958 57,321,746 58,223,229 2000's 59,252,728 60,286,364 61,107,254 61,871,450 62,496,134 63,616,827 64,166,280 64,964,769 65,073,996 65,329,582 2010's 65,542,345 65,940,522

  3. California Natural Gas Number of Gas and Gas Condensate Wells (Number of

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

    Elements) Gas and Gas Condensate Wells (Number of Elements) California Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 1,214 1990's 1,162 1,377 1,126 1,092 1,261 997 978 930 847 1,152 2000's 1,169 1,244 1,232 1,249 1,272 1,356 1,451 1,540 1,645 1,643 2010's 1,580 1,308 1,423 1,335 1,118 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  4. District of Columbia Natural Gas Number of Commercial Consumers (Number of

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

    Elements) Commercial Consumers (Number of Elements) District of Columbia Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 11 14,683 11,370 11,354 1990's 11,322 11,318 11,206 11,133 11,132 11,089 10,952 10,874 10,658 12,108 2000's 11,106 10,816 10,870 10,565 10,406 10,381 10,410 9,915 10,024 10,288 2010's 9,879 10,050 9,771 9,963 10,049 - = No Data Reported; -- = Not Applicable; NA = Not

  5. District of Columbia Natural Gas Number of Residential Consumers (Number of

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

    Elements) Residential Consumers (Number of Elements) District of Columbia Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 134 130,748 134,758 134,837 1990's 136,183 136,629 136,438 135,986 135,119 135,299 135,215 134,807 132,867 137,206 2000's 138,252 138,412 143,874 136,258 138,134 141,012 141,953 142,384 142,819 143,436 2010's 144,151 145,524 145,938 146,712 147,877 - = No Data Reported; --

  6. Kansas Natural Gas Number of Gas and Gas Condensate Wells (Number of

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

    Elements) Gas and Gas Condensate Wells (Number of Elements) Kansas Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 13,935 1990's 16,980 17,948 18,400 19,472 19,365 22,020 21,388 21,500 21,000 17,568 2000's 15,206 15,357 16,957 17,387 18,120 18,946 19,713 19,713 17,862 21,243 2010's 22,145 25,758 24,697 23,792 24,354 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  7. Project Registration Number Assignments (Active) | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Project Registration Number Assignments (Active) Project Registration Number Assignments (Active) As of: October 2015 Provides a table of Project Registration Number Assignments (Active) PDF icon Project Registration Number Assignment (Active) More Documents & Publications Project Registration Number Assignments (Completed) All Active DOE Technical Standards Document Active Project Justification Statement For Additional Information Contact: Jeffrey Feit phone: 301-903-0471 e-mail:

  8. Project Registration Number Assignments (Completed) | Department of Energy

    Energy Savers [EERE]

    Registration Number Assignments (Completed) Project Registration Number Assignments (Completed) As of: March 2016 Provides a table of Project Registration Number Assignments (Completed) PDF icon Project Registration Number Assignments (Completed) More Documents & Publications All Active DOE Technical Standards Document Project Registration Number Assignments (Active) The Proposed Reaffirmations and Cancellations For Additional Information Contact: Jeffrey Feit phone: 301-903-0471 e-mail:

  9. Random matrices and chaos in nuclear physics: Nuclear structure

    SciTech Connect (OSTI)

    Weidenmueller, H. A.; Mitchell, G. E. [Max-Planck-Institut fuer Kernphysik, D-69029 Heidelberg (Germany); North Carolina State University, Raleigh, North Carolina 27695 (United States) and Triangle Universities Nuclear Laboratory, Durham, North Carolina 27706 (United States)

    2009-04-15

    Evidence for the applicability of random-matrix theory to nuclear spectra is reviewed. In analogy to systems with few degrees of freedom, one speaks of chaos (more accurately, quantum chaos) in nuclei whenever random-matrix predictions are fulfilled. An introduction into the basic concepts of random-matrix theory is followed by a survey over the extant experimental information on spectral fluctuations, including a discussion of the violation of a symmetry or invariance property. Chaos in nuclear models is discussed for the spherical shell model, for the deformed shell model, and for the interacting boson model. Evidence for chaos also comes from random-matrix ensembles patterned after the shell model such as the embedded two-body ensemble, the two-body random ensemble, and the constrained ensembles. All this evidence points to the fact that chaos is a generic property of nuclear spectra, except for the ground-state regions of strongly deformed nuclei.

  10. Property:NEPA SerialNumber | Open Energy Information

    Open Energy Info (EERE)

    SerialNumber Jump to: navigation, search Property Name NEPA SerialNumber Property Type String This is a property of type String. Pages using the property "NEPA SerialNumber"...

  11. Property:OutagePhoneNumber | Open Energy Information

    Open Energy Info (EERE)

    OutagePhoneNumber Jump to: navigation, search Property Name OutagePhoneNumber Property Type String Description An outage hotline or 24-hour customer service number Note: uses...

  12. Alaska Maximum Number of Active Crews Engaged in Seismic Surveying...

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

    Seismic Surveying (Number of Elements) Alaska Maximum Number of Active Crews Engaged in Seismic Surveying (Number of Elements) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec...

  13. Virginia Natural Gas Number of Gas and Gas Condensate Wells ...

    Gasoline and Diesel Fuel Update (EIA)

    Gas and Gas Condensate Wells (Number of Elements) Virginia Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  14. Colorado Natural Gas Number of Gas and Gas Condensate Wells ...

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

    Gas and Gas Condensate Wells (Number of Elements) Colorado Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  15. Nebraska Natural Gas Number of Gas and Gas Condensate Wells ...

    Gasoline and Diesel Fuel Update (EIA)

    Gas and Gas Condensate Wells (Number of Elements) Nebraska Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  16. Missouri Natural Gas Number of Gas and Gas Condensate Wells ...

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

    Gas and Gas Condensate Wells (Number of Elements) Missouri Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  17. Michigan Natural Gas Number of Gas and Gas Condensate Wells ...

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

    Gas and Gas Condensate Wells (Number of Elements) Michigan Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  18. Kentucky Natural Gas Number of Gas and Gas Condensate Wells ...

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

    Gas and Gas Condensate Wells (Number of Elements) Kentucky Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  19. Tennessee Natural Gas Number of Gas and Gas Condensate Wells...

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

    Gas and Gas Condensate Wells (Number of Elements) Tennessee Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  20. Pennsylvania Natural Gas Number of Gas and Gas Condensate Wells...

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

    Gas and Gas Condensate Wells (Number of Elements) Pennsylvania Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  1. Mississippi Natural Gas Number of Gas and Gas Condensate Wells...

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

    Gas and Gas Condensate Wells (Number of Elements) Mississippi Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  2. Oklahoma Natural Gas Number of Gas and Gas Condensate Wells ...

    Gasoline and Diesel Fuel Update (EIA)

    Gas and Gas Condensate Wells (Number of Elements) Oklahoma Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  3. Illinois Natural Gas Number of Gas and Gas Condensate Wells ...

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

    Gas and Gas Condensate Wells (Number of Elements) Illinois Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  4. Arkansas Natural Gas Number of Gas and Gas Condensate Wells ...

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

    Gas and Gas Condensate Wells (Number of Elements) Arkansas Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  5. Maryland Natural Gas Number of Gas and Gas Condensate Wells ...

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

    Gas and Gas Condensate Wells (Number of Elements) Maryland Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  6. Louisiana Natural Gas Number of Gas and Gas Condensate Wells...

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

    Gas and Gas Condensate Wells (Number of Elements) Louisiana Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  7. Property:Number of Plants included in Capacity Estimate | Open...

    Open Energy Info (EERE)

    Plants included in Capacity Estimate Jump to: navigation, search Property Name Number of Plants included in Capacity Estimate Property Type Number Retrieved from "http:...

  8. Property:NumberOfLowEmissionDevelopmentStrategiesExample | Open...

    Open Energy Info (EERE)

    issionDevelopmentStrategiesExample Property Type Number Retrieved from "http:en.openei.orgwindex.php?titleProperty:NumberOfLowEmissionDevelopmentStrategiesExample&oldid326472...

  9. Property:NumberOfLowEmissionDevelopmentStrategiesExamples | Open...

    Open Energy Info (EERE)

    sionDevelopmentStrategiesExamples Property Type Number Retrieved from "http:en.openei.orgwindex.php?titleProperty:NumberOfLowEmissionDevelopmentStrategiesExamples&oldid323715...

  10. Property:NumberOfResourceAssessments | Open Energy Information

    Open Energy Info (EERE)

    Jump to: navigation, search This is a property of type Number. Retrieved from "http:en.openei.orgwindex.php?titleProperty:NumberOfResourceAssessments&oldid31439...

  11. Finite Mach number spherical shock wave, application to shock ignition

    SciTech Connect (OSTI)

    Vallet, A.; Ribeyre, X.; Tikhonchuk, V.

    2013-08-15

    A converging and diverging spherical shock wave with a finite initial Mach number M{sub s0} is described by using a perturbative approach over a small parameter M{sub s}{sup ?2}. The zeroth order solution is the Guderley's self-similar solution. The first order correction to this solution accounts for the effects of the shock strength. Whereas it was constant in the Guderley's asymptotic solution, the amplification factor of the finite amplitude shock ?(t)?dU{sub s}/dR{sub s} now varies in time. The coefficients present in its series form are iteratively calculated so that the solution does not undergo any singular behavior apart from the position of the shock. The analytical form of the corrected solution in the vicinity of singular points provides a better physical understanding of the finite shock Mach number effects. The correction affects mainly the flow density and the pressure after the shock rebound. In application to the shock ignition scheme, it is shown that the ignition criterion is modified by more than 20% if the fuel pressure prior to the final shock is taken into account. A good agreement is obtained with hydrodynamic simulations using a Lagrangian code.

  12. UGE Scheduler Cycle Time

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

    UGE Scheduler Cycle Time UGE Scheduler Cycle Time Genepool Cycle Time Genepool Daily Genepool Weekly Phoebe Cycle Time Phoebe Daily Phoebe Weekly What is the Scheduler Cycle? The...

  13. Multiplexer and time duration measuring circuit

    DOE Patents [OSTI]

    Gray, Jr., James

    1980-01-01

    A multiplexer device is provided for multiplexing data in the form of randomly developed, variable width pulses from a plurality of pulse sources to a master storage. The device includes a first multiplexer unit which includes a plurality of input circuits each coupled to one of the pulse sources, with all input circuits being disabled when one input circuit receives an input pulse so that only one input pulse is multiplexed by the multiplexer unit at any one time.

  14. Improving Ramsey spectroscopy in the extreme-ultraviolet region with a random-sampling approach

    SciTech Connect (OSTI)

    Eramo, R.; Bellini, M. [Istituto Nazionale di Ottica (INO-CNR), Largo E. Fermi 6, I-50125 Florence (Italy); European Laboratory for Non-linear Spectroscopy (LENS), I-50019 Sesto Fiorentino, Florence (Italy); Corsi, C.; Liontos, I. [European Laboratory for Non-linear Spectroscopy (LENS), I-50019 Sesto Fiorentino, Florence (Italy); Cavalieri, S. [European Laboratory for Non-linear Spectroscopy (LENS), I-50019 Sesto Fiorentino, Florence (Italy); Department of Physics, University of Florence, I-50019 Sesto Fiorentino, Florence (Italy)

    2011-04-15

    Ramsey-like techniques, based on the coherent excitation of a sample by delayed and phase-correlated pulses, are promising tools for high-precision spectroscopic tests of QED in the extreme-ultraviolet (xuv) spectral region, but currently suffer experimental limitations related to long acquisition times and critical stability issues. Here we propose a random subsampling approach to Ramsey spectroscopy that, by allowing experimentalists to reach a given spectral resolution goal in a fraction of the usual acquisition time, leads to substantial improvements in high-resolution spectroscopy and may open the way to a widespread application of Ramsey-like techniques to precision measurements in the xuv spectral region.

  15. Multicanonical sampling of rare events in random matrices

    SciTech Connect (OSTI)

    Saito, Nen; Iba, Yukito; Hukushima, Koji

    2010-09-15

    A method based on multicanonical Monte Carlo is applied to the calculation of large deviations in the largest eigenvalue of random matrices. The method is successfully tested with the Gaussian orthogonal ensemble, sparse random matrices, and matrices whose components are subject to uniform density. Specifically, the probability that all eigenvalues of a matrix are negative is estimated in these cases down to the values of {approx}10{sup -200}, a region where simple random sampling is ineffective. The method can be applied to any ensemble of matrices and used for sampling rare events characterized by any statistics.

  16. System and method for simultaneously collecting serial number information from numerous identity tags

    DOE Patents [OSTI]

    Doty, Michael A. (Manteca, CA)

    1997-01-01

    A system and method for simultaneously collecting serial number information reports from numerous colliding coded-radio-frequency identity tags. Each tag has a unique multi-digit serial number that is stored in non-volatile RAM. A reader transmits an ASCII coded "D" character on a carrier of about 900 MHz and a power illumination field having a frequency of about 1.6 Ghz. A one MHz tone is modulated on the 1.6 Ghz carrier as a timing clock for a microprocessor in each of the identity tags. Over a thousand such tags may be in the vicinity and each is powered-up and clocked by the 1.6 Ghz power illumination field. Each identity tag looks for the "D" interrogator modulated on the 900 MHz carrier, and each uses a digit of its serial number to time a response. Clear responses received by the reader are repeated for verification. If no verification or a wrong number is received by any identity tag, it uses a second digital together with the first to time out a more extended period for response. Ultimately, the entire serial number will be used in the worst case collision environments; and since the serial numbers are defined as being unique, the final possibility will be successful because a clear time-slot channel will be available.

  17. System and method for simultaneously collecting serial number information from numerous identity tags

    DOE Patents [OSTI]

    Doty, M.A.

    1997-01-07

    A system and method are disclosed for simultaneously collecting serial number information reports from numerous colliding coded-radio-frequency identity tags. Each tag has a unique multi-digit serial number that is stored in non-volatile RAM. A reader transmits an ASCII coded ``D`` character on a carrier of about 900 MHz and a power illumination field having a frequency of about 1.6 Ghz. A one MHz tone is modulated on the 1.6 Ghz carrier as a timing clock for a microprocessor in each of the identity tags. Over a thousand such tags may be in the vicinity and each is powered-up and clocked by the 1.6 Ghz power illumination field. Each identity tag looks for the ``D`` interrogator modulated on the 900 MHz carrier, and each uses a digit of its serial number to time a response. Clear responses received by the reader are repeated for verification. If no verification or a wrong number is received by any identity tag, it uses a second digital together with the first to time out a more extended period for response. Ultimately, the entire serial number will be used in the worst case collision environments; and since the serial numbers are defined as being unique, the final possibility will be successful because a clear time-slot channel will be available. 5 figs.

  18. STANDING ORDER 1. Standing Order Number: EP-DIV-S0-20222, R.O

    Office of Environmental Management (EM)

    STANDING ORDER 1. Standing Order Number: EP-DIV-S0-20222, R.O 2. Standing Order Type: (check one) [8J Division D Facility 3. Applicable Facilities: All EWMO Facilities 4. Standing Order Title: EWMO Legacy TRU Waste Pause 5. Distribution List: (By Functional Title) TA-54 Timely Order Book, Waste Characterization, Reduction, and Repackaging Facility (WCRRF) Timely Order Book, Radioassay and Nondestructive Testing (RANT) Facility Timely Order Book, and Environmental Programs (EP) Document Control

  19. Search for baryon-number and lepton-number violating decays of $Lambda$ hyperons using the CLAS detector at Jefferson Laboratory

    SciTech Connect (OSTI)

    McCracken, Michael E.

    2015-10-09

    We present a search for ten baryon-number violating decay modes of $\\Lambda$ hyperons using the CLAS detector at Jefferson Laboratory. Nine of these decay modes result in a single meson and single lepton in the final state ($\\Lambda \\rightarrow m \\ell$) and conserve either the sum or the difference of baryon and lepton number ($B \\pm L$). The tenth decay mode ($\\Lambda \\rightarrow \\bar{p}\\pi^+$) represents a difference in baryon number of two units and no difference in lepton number. We observe no significant signal and set upper limits on the branching fractions of these reactions in the range $(4-200)\\times 10^{-7}$ at the $90\\%$ confidence level.

  20. Search for baryon-number and lepton-number violating decays of $Lambda$ hyperons using the CLAS detector at Jefferson Laboratory

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    McCracken, Michael E.

    2015-10-09

    We present a search for ten baryon-number violating decay modes of $\\Lambda$ hyperons using the CLAS detector at Jefferson Laboratory. Nine of these decay modes result in a single meson and single lepton in the final state ($\\Lambda \\rightarrow m \\ell$) and conserve either the sum or the difference of baryon and lepton number ($B \\pm L$). The tenth decay mode ($\\Lambda \\rightarrow \\bar{p}\\pi^+$) represents a difference in baryon number of two units and no difference in lepton number. We observe no significant signal and set upper limits on the branching fractions of these reactions in the range $(4-200)\\times 10^{-7}$moreat the $90\\%$ confidence level.less

  1. Random-matrix approach to the statistical compound nuclear reaction...

    Office of Scientific and Technical Information (OSTI)

    AC52-06NA25396 Resource Type: Conference Resource Relation: Conference: Random Matrix Theory, Integrable Systems, and Topology in Physics ; 2015-11-02 - 2015-11-02 ; Stony...

  2. Quasiparticle random-phase approximation with interactions from the

    Office of Scientific and Technical Information (OSTI)

    Similarity Renormalization Group (Journal Article) | SciTech Connect Quasiparticle random-phase approximation with interactions from the Similarity Renormalization Group Citation Details In-Document Search Title: Quasiparticle random-phase approximation with interactions from the Similarity Renormalization Group Authors: Hergert, H. ; Papakonstantinou, P. ; Roth, R. Publication Date: 2011-06-16 OSTI Identifier: 1099608 Type: Publisher's Accepted Manuscript Journal Name: Physical Review C

  3. Listening to the noise: random fluctuations reveal gene network parameters

    Office of Scientific and Technical Information (OSTI)

    (Journal Article) | SciTech Connect Journal Article: Listening to the noise: random fluctuations reveal gene network parameters Citation Details In-Document Search Title: Listening to the noise: random fluctuations reveal gene network parameters × You are accessing a document from the Department of Energy's (DOE) SciTech Connect. This site is a product of DOE's Office of Scientific and Technical Information (OSTI) and is provided as a public service. Visit OSTI to utilize additional

  4. Export support of renewable energy industries. Task number 1, deliverable number 3. Final report

    SciTech Connect (OSTI)

    1998-01-14

    The United States Export Council for Renewable Energy (US/ECRE), a consortium of six industry associations, promotes the interests of the renewable energy and energy efficiency member companies which provide goods and services in biomass, geothermal, hydropower, passive solar, photovoltaics, solar thermal, wind, wood energy, and energy efficiency technologies. US/ECRE`s mission is to catalyze export markets for renewable energy and energy efficiency technologies worldwide. Under this grant, US/ECRE has conducted a number of in-house activities, as well as to manage activities by member trade associations, affiliate organizations and non-member contractors and consultants. The purpose of this document is to report on task coordination and effectiveness.

  5. Export support of renewable energy industries, grant number 1, deliverable number 3. Final report

    SciTech Connect (OSTI)

    1998-01-14

    The United States Export Council for Renewable Energy (US/ECRE), a consortium of six industry associations, promotes the interests of the renewable energy and energy efficiency member companies which provide goods and services in biomass, geothermal, hydropower, passive solar, photovoltaics, solar thermal, wind, wood energy, and energy efficiency technologies. US/ECRE`s mission is to catalyze export markets for renewable energy and energy efficiency technologies worldwide. Under this grant, US/ECRE has conducted a number of in-house activities, as well as to manage activities by member trade associations, affiliate organizations and non-member contractors and consultants. The purpose of this document is to report on grant coordination and effectiveness.

  6. Study of Engine Operating Parameter Effects on GDI Engine Particle-Number

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Emissions | Department of Energy Study of Engine Operating Parameter Effects on GDI Engine Particle-Number Emissions Study of Engine Operating Parameter Effects on GDI Engine Particle-Number Emissions Results show that fuel-injection timing is the dominant factor contributing to PN emissions from this wall-guided GDI engine. PDF icon p-10_he.pdf More Documents & Publications Advanced Combustion and Fuels Vehicle Technologies Office Merit Review 2014: Particulate Emissions Control by

  7. Property:ASHRAE 169 Climate Zone Number | Open Energy Information

    Open Energy Info (EERE)

    5 + Adair County, Oklahoma ASHRAE 169-2006 Climate Zone + Climate Zone Number 3 + Adams County, Colorado ASHRAE 169-2006 Climate Zone + Climate Zone Number 5 + Adams County,...

  8. Fact #803: November 11, 2013 Average Number of Transmission Gears is on the

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Rise | Department of Energy 3: November 11, 2013 Average Number of Transmission Gears is on the Rise Fact #803: November 11, 2013 Average Number of Transmission Gears is on the Rise The number of gears a transmission has affects a vehicle's fuel economy and performance. The more gears a vehicle has, the more time the engine spends within an optimal operating range while the vehicle speeds up and slows down. To achieve a better match between engine speed and wheel speed, manufacturers have

  9. Fact #851 December 15, 2014 The Average Number of Gears used in

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Transmissions Continues to Rise | Department of Energy 1 December 15, 2014 The Average Number of Gears used in Transmissions Continues to Rise Fact #851 December 15, 2014 The Average Number of Gears used in Transmissions Continues to Rise The number of gears in a transmission affects a vehicle's fuel economy and performance. The more gears a vehicle has, the more time the engine spends within an optimal operating range while the vehicle speeds up and slows down. To achieve a better match

  10. Events in time: Basic analysis of Poisson data

    SciTech Connect (OSTI)

    Engelhardt, M.E.

    1994-09-01

    The report presents basic statistical methods for analyzing Poisson data, such as the member of events in some period of time. It gives point estimates, confidence intervals, and Bayesian intervals for the rate of occurrence per unit of time. It shows how to compare subsets of the data, both graphically and by statistical tests, and how to look for trends in time. It presents a compound model when the rate of occurrence varies randomly. Examples and SAS programs are given.

  11. Modeling the Number of Ignitions Following an Earthquake: Developing

    Office of Environmental Management (EM)

    Prediction Limits for Overdispersed Count Data | Department of Energy the Number of Ignitions Following an Earthquake: Developing Prediction Limits for Overdispersed Count Data Modeling the Number of Ignitions Following an Earthquake: Developing Prediction Limits for Overdispersed Count Data Modeling the Number of Ignitions Following an Earthquake: Developing Prediction Limits for Overdispersed Count Data Authors: Elizabeth J. Kelly and Raymond N. Tell PDF icon Modeling the Number of

  12. Social Security Number Reduction Project | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Social Security Number Reduction Project Social Security Number Reduction Project The document below provides information regarding acceptable uses of the Social Security Number (SSN). PDF icon Baseline Inventory.pdf More Documents & Publications DOE Guidance on the Use of the SSN Manchester Software 1099 Reporting PIA, Idaho National Laboratory Occupational Medicine - Assistant PIA, Idaho National Laboratory

  13. Kubo relations and radiative corrections for lepton number washout

    SciTech Connect (OSTI)

    Bdeker, Dietrich; Laine, M. E-mail: laine@itp.unibe.ch

    2014-05-01

    The rates for lepton number washout in extensions of the Standard Model containing right-handed neutrinos are key ingredients in scenarios for baryogenesis through leptogenesis. We relate these rates to real-time correlation functions at finite temperature, without making use of any particle approximations. The relations are valid to quadratic order in neutrino Yukawa couplings and to all orders in Standard Model couplings. They take into account all spectator processes, and apply both in the symmetric and in the Higgs phase of the electroweak theory. We use the relations to compute washout rates at next-to-leading order in g, where g denotes a Standard Model gauge or Yukawa coupling, both in the non-relativistic and in the relativistic regime. Even in the non-relativistic regime the parametrically dominant radiative corrections are only suppressed by a single power of g. In the non-relativistic regime radiative corrections increase the washout rate by a few percent at high temperatures, but they are of order unity around the weak scale and in the relativistic regime.

  14. Count-doubling time safety circuit

    DOE Patents [OSTI]

    Rusch, Gordon K.; Keefe, Donald J.; McDowell, William P.

    1981-01-01

    There is provided a nuclear reactor count-factor-increase time monitoring circuit which includes a pulse-type neutron detector, and means for counting the number of detected pulses during specific time periods. Counts are compared and the comparison is utilized to develop a reactor scram signal, if necessary.

  15. Quantifying the number of color centers in single fluorescent nanodiamonds by photon correlation spectroscopy and Monte Carlo simulation

    SciTech Connect (OSTI)

    Hui, Y.Y.; Chang, Y.-R.; Lee, H.-Y.; Chang, H.-C.; Lim, T.-S.; Fann Wunshain

    2009-01-05

    The number of negatively charged nitrogen-vacancy centers (N-V){sup -} in fluorescent nanodiamond (FND) has been determined by photon correlation spectroscopy and Monte Carlo simulations at the single particle level. By taking account of the random dipole orientation of the multiple (N-V){sup -} fluorophores and simulating the probability distribution of their effective numbers (N{sub e}), we found that the actual number (N{sub a}) of the fluorophores is in linear correlation with N{sub e}, with correction factors of 1.8 and 1.2 in measurements using linearly and circularly polarized lights, respectively. We determined N{sub a}=8{+-}1 for 28 nm FND particles prepared by 3 MeV proton irradiation.

  16. Random telegraph signals by alkanethiol-protected Au nanoparticles in chemically assembled single-electron transistors

    SciTech Connect (OSTI)

    Kano, Shinya; Azuma, Yasuo; Tanaka, Daisuke; Sakamoto, Masanori; Teranishi, Toshiharu; Smith, Luke W.; Smith, Charles G.; Majima, Yutaka

    2013-12-14

    We have studied random telegraph signals (RTSs) in a chemically assembled single-electron transistor (SET) at temperatures as low as 300 mK. The RTSs in the chemically assembled SET were investigated by measuring the sourcedrain current, using a histogram of the RTS dwell time, and calculating the power spectrum density of the drain currenttime characteristics. It was found that the dwell time of the RTS was dependent on the drain voltage of the SET, but was independent of the gate voltage. Considering the spatial structure of the chemically assembled SET, the origin of the RTS is attributed to the trapped charges on an alkanethiol-protected Au nanoparticle positioned near the SET. These results are important as they will help to realize stable chemically assembled SETs in practical applications.

  17. Random telegraph noise in metallic single-walled carbon nanotubes

    SciTech Connect (OSTI)

    Chung, Hyun-Jong; Woo Uhm, Tae; Won Kim, Sung; Gyu You, Young; Wook Lee, Sang; Ho Jhang, Sung; Campbell, Eleanor E. B.; Woo Park, Yung

    2014-05-12

    We have investigated random telegraph noise (RTN) observed in individual metallic carbon nanotubes (CNTs). Mean lifetimes in high- and low-current states, ?{sub high} and ?{sub low}, have been studied as a function of bias-voltage and gate-voltage as well as temperature. By analyzing the statistics and features of the RTN, we suggest that this noise is due to the random transition of defects between two metastable states, activated by inelastic scattering with conduction electrons. Our results indicate an important role of defect motions in the 1/f noise in CNTs.

  18. ORISE: Report shows number of health physics degrees for 2010

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

    report shows number of health physics degrees increased for graduates, decreased for undergraduates in 2010 Decreased number of B.S. degrees remains higher than levels in the early 2000 FOR IMMEDIATE RELEASE Dec. 20, 2011 FY12-09 OAK RIDGE, Tenn.-The number of health physics graduate degrees increased for both master's and doctoral candidates in 2010, but decreased for bachelor's degrees, says a report released this year by the Oak Ridge Institute for Science and Education. The ORISE report,

  19. Heavy pair production currents with general quantum numbers in

    Office of Scientific and Technical Information (OSTI)

    dimensionally regularized nonrelativistic QCD (Journal Article) | SciTech Connect Heavy pair production currents with general quantum numbers in dimensionally regularized nonrelativistic QCD Citation Details In-Document Search Title: Heavy pair production currents with general quantum numbers in dimensionally regularized nonrelativistic QCD We discuss the form and construction of general color singlet heavy particle-antiparticle pair production currents for arbitrary quantum numbers, and

  20. Developing and Enhancing Workforce Training Programs: Number of Projects by

    Energy Savers [EERE]

    State | Department of Energy Developing and Enhancing Workforce Training Programs: Number of Projects by State Developing and Enhancing Workforce Training Programs: Number of Projects by State Map of the United States showing the location of Workforce Training Projects, funded through the American Recovery and Reinvestment Act PDF icon Developing and Enhancing Workforce Training Programs: Number of Projects by State More Documents & Publications Workforce Development Wind Projects

  1. Daylight Savings Time Starts

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

    Daylight Savings Time Starts Daylight Savings Time Starts WHEN: Mar 08, 2015 3:00 AM - 11:59 PM WHERE: World Time Zones CATEGORY: Holiday INTERNAL: Calendar Login Daylight Savings...

  2. Modeling the Number of Ignitions Following an Earthquake: Developing...

    Office of Environmental Management (EM)

    Developing Prediction Limits for Overdispersed Count Data Authors: Elizabeth J. Kelly and Raymond N. Tell PDF icon Modeling the Number of Ignitions Following an Earthquake:...

  3. Conducting Your Annual VPP Self-Evaluation by the Numbers

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    ... VPP Annual Self-evaluation: By the Numbers Example pre-meeting training 5. Picking who to interview * Fixed - Pick by positionlocation - Safety Council Chair - Union Steward - ...

  4. Dependence of Band Renormalization Effect on the Number of Copper...

    Office of Scientific and Technical Information (OSTI)

    DOE Contract Number: AC02-76SF00515 Resource Type: Journal Article Resource Relation: Journal Name: Submitted to Physical Review Letters; Journal Volume: 103; Journal Issue: 6 ...

  5. Request for Proposals Number RHB-5-52483

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

    9 National Renewable Energy Laboratory Managed and Operated by the Alliance for Sustainable Energy, LLC Request for Proposals Number RHB-5-52483 "Subsurface Utility Engineering...

  6. Quark-Gluon Plasma Model and Origin of Magic Numbers

    SciTech Connect (OSTI)

    Ghahramany, N.; Ghanaatian, M.; Hooshmand, M.

    2008-04-21

    Using Boltzman distribution in a quark-gluon plasma sample it is possible to obtain all existing magic numbers and their extensions without applying the spin and spin-orbit couplings. In this model it is assumed that in a quark-gluon thermodynamic plasma, quarks have no interactions and they are trying to form nucleons. Considering a lattice for a central quark and the surrounding quarks, using a statistical approach to find the maximum number of microstates, the origin of magic numbers is explained and a new magic number is obtained.

  7. Number of NERSC Users and Projects Through the Years

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

    Through the Years Careers Visitor Info Web Policies Home About Usage and User Demographics Users and Projects Through the Years Number of NERSC Users and Projects Through...

  8. Temporary EPA ID Number Request | Open Energy Information

    Open Energy Info (EERE)

    Temporary EPA ID Number RequestLegal Abstract A developer that may "generate hazardous waste only from an episodic event" may instead apply for a temporary hazardous waste...

  9. Plasmonic waves of a semi-infinite random nanocomposite

    SciTech Connect (OSTI)

    Moradi, Afshin

    2013-10-15

    The dispersion curves of the plasmonic waves of a semi-infinite random metal-dielectric nanocomposite, consisting of bulk metal embedded with dielectric inclusions, are presented. Two branches of p-polarized surface plasmon-polariton modes are found to exist. The possibility of experimentally observing the surface waves by attenuated total reflection is demonstrated.

  10. Improving the chances of successful protein structure determination with a random forest classifier

    SciTech Connect (OSTI)

    Jahandideh, Samad [Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92307 (United States); Joint Center for Structural Genomics, (United States); Jaroszewski, Lukasz; Godzik, Adam, E-mail: adam@burnham.org [Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92307 (United States); Joint Center for Structural Genomics, (United States); University of California, San Diego, La Jolla, California (United States)

    2014-03-01

    Using an extended set of protein features calculated separately for protein surface and interior, a new version of XtalPred based on a random forest classifier achieves a significant improvement in predicting the success of structure determination from the primary amino-acid sequence. Obtaining diffraction quality crystals remains one of the major bottlenecks in structural biology. The ability to predict the chances of crystallization from the amino-acid sequence of the protein can, at least partly, address this problem by allowing a crystallographer to select homologs that are more likely to succeed and/or to modify the sequence of the target to avoid features that are detrimental to successful crystallization. In 2007, the now widely used XtalPred algorithm [Slabinski et al. (2007 ?), Protein Sci.16, 24722482] was developed. XtalPred classifies proteins into five crystallization classes based on a simple statistical analysis of the physicochemical features of a protein. Here, towards the same goal, advanced machine-learning methods are applied and, in addition, the predictive potential of additional protein features such as predicted surface ruggedness, hydrophobicity, side-chain entropy of surface residues and amino-acid composition of the predicted protein surface are tested. The new XtalPred-RF (random forest) achieves significant improvement of the prediction of crystallization success over the original XtalPred. To illustrate this, XtalPred-RF was tested by revisiting target selection from 271 Pfam families targeted by the Joint Center for Structural Genomics (JCSG) in PSI-2, and it was estimated that the number of targets entered into the protein-production and crystallization pipeline could have been reduced by 30% without lowering the number of families for which the first structures were solved. The prediction improvement depends on the subset of targets used as a testing set and reaches 100% (i.e. twofold) for the top class of predicted targets.

  11. ELEMENTARY APPROACH TO SELF-ASSEMBLY AND ELASTIC PROPERTIES OF RANDOM COPOLYMERS

    SciTech Connect (OSTI)

    S. M. CHITANVIS

    2000-10-01

    The authors have mapped the physics of a system of random copolymers onto a time-dependent density functional-type field theory using techniques of functional integration. Time in the theory is merely a label for the location of a given monomer along the extent of a flexible chain. We derive heuristically within this approach a non-local constraint which prevents segments on chains in the system from straying too far from each other, and leads to self-assembly. The structure factor is then computed in a straightforward fashion. The long wave-length limit of the structure factor is used to obtain the elastic modulus of the network. It is shown that there is a surprising competition between the degree of micro-phase separation and the elastic moduli of the system.

  12. Table B10. Employment Size Category, Number of Buildings, 1999

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

    0. Employment Size Category, Number of Buildings, 1999" ,"Number of Buildings (thousand)" ,"All Buildings","Number of Workers" ,,"Fewer than 5 Workers","5 to 9 Workers","10 to 19 Workers","20 to 49 Workers","50 to 99 Workers","100 to 249 Workers","250 or More Workers" "All Buildings ................",4657,2376,807,683,487,174,90,39 "Building Floorspace" "(Square

  13. Mailing Addresses and Information Numbers for Operations, Field, and Site

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Offices | Department of Energy About Energy.gov » Mailing Addresses and Information Numbers for Operations, Field, and Site Offices Mailing Addresses and Information Numbers for Operations, Field, and Site Offices Name Telephone Number U.S. Department of Energy Ames Site Office 111 TASF, Iowa State University Ames, Iowa 50011 515-294-9557 U.S. Department of Energy Argonne Site Office 9800 S. Cass Avenue Argonne, IL 60439 630-252-2000 U.S. Department of Energy Berkeley Site Office Berkeley

  14. Decision on the number of turns in the eRHIC Nov15 design

    SciTech Connect (OSTI)

    Brooks, S.

    2015-12-01

    When moving from the “Jun’15” to the “Nov’15” eRHIC FFAG design, the number of accelerating passes through the linac was reduced from 16 to 12. There are an equal number of decelerating passes, so the total reduced from 32 to 24. At the same time, the linac energy was increased from 1.322GeV to 1.665GeV and the RF frequency changed from 422MHz to 647MHz. The maximum beam energy remained approximately constant, changing from 21.164GeV to exactly 20GeV.

  15. Graph of Total Number of Oligos Within Windows of a Sequence

    Energy Science and Technology Software Center (OSTI)

    1995-11-28

    SEQWIN is user-friendly software which graphs the total number of oligos present in a sequence. The sequence is scanned one window at a time; windows can be overlapping. Each bar on the graph represents a single window down the sequence. The user specifies the sequence of interest and a list of oligos as program input. If the sequence is known, locations of specific structure or sequences can be specified and compared with the bars onmorea graph. The window size, amount of overlap of the windows, number of windows to be considered, and the starting position of the first window used can be adjusted at the user's discretion.less

  16. Logging into Deltek Time & Expense (T&E) | The Ames Laboratory

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

    Logging into Deltek Time & Expense (T&E) Document Number: NA Effective Date: 07/2014

  17. OVIS: Scalable Run Time Data Collection Analysis and Visualization

    Office of Scientific and Technical Information (OSTI)

    (Abstract). (Conference) | SciTech Connect OVIS: Scalable Run Time Data Collection Analysis and Visualization (Abstract). Citation Details In-Document Search Title: OVIS: Scalable Run Time Data Collection Analysis and Visualization (Abstract). Abstract not provided. Authors: Chen, Frank Xiaoxiao ; Das, Ananya Publication Date: 2009-07-01 OSTI Identifier: 1141424 Report Number(s): SAND2009-4761C 506638 DOE Contract Number: DE-AC04-94AL85000 Resource Type: Conference Resource Relation:

  18. Eigenfunction Expansion of the Space-Time Dependent Neutron Survival

    Office of Scientific and Technical Information (OSTI)

    Probability. (Conference) | SciTech Connect Eigenfunction Expansion of the Space-Time Dependent Neutron Survival Probability. Citation Details In-Document Search Title: Eigenfunction Expansion of the Space-Time Dependent Neutron Survival Probability. Abstract not provided. Authors: Kamm, Ryan James ; Prinja, Anil K. Publication Date: 2013-10-01 OSTI Identifier: 1117182 Report Number(s): SAND2013-9422C 480812 DOE Contract Number: AC04-94AL85000 Resource Type: Conference Resource Relation:

  19. Eigenfunction Expansion of the Space-Time Dependent Neutron Survival

    Office of Scientific and Technical Information (OSTI)

    Probability. (Journal Article) | SciTech Connect Journal Article: Eigenfunction Expansion of the Space-Time Dependent Neutron Survival Probability. Citation Details In-Document Search Title: Eigenfunction Expansion of the Space-Time Dependent Neutron Survival Probability. Abstract not provided. Authors: Kamm, Ryan James ; Prinja, Anil K. Publication Date: 2013-07-01 OSTI Identifier: 1106880 Report Number(s): SAND2013-5731J 465496 DOE Contract Number: AC04-94AL85000 Resource Type: Journal

  20. Modeling the Number of Ignitions Following an Earthquake: Developing...

    Office of Environmental Management (EM)

    the likelihood of various fire scenarios. The first component of the approach is a statistical model to predict the number of ignitions for a new earthquake event. This model is...

  1. Property:NumberOfUsers | Open Energy Information

    Open Energy Info (EERE)

    property "NumberOfUsers" Showing 25 pages using this property. (previous 25) (next 25) H HOMER + 578 + HOMER + 14 + HOMER + 1 + HOMER + 34 + HOMER + 6 + HOMER + 68 + HOMER + 89...

  2. Number of NERSC Users and Projects Through the Years

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

    Users and Projects Through the Years Careers Visitor Info Web Policies Home » About » Usage and User Demographics » Users and Projects Through the Years Number of NERSC Users and Projects Through the Years These numbers exclude staff and vendor accounts. Year Number of Users Number of Projects 2014 5,950 846 2013 5.191 768 2012 4,659 728 2011 4,934 641 2010 4,294 540 2009 3,731 506 2008 3,271 464 2007 3,111 404 2006 2,978 385 2005 2,677 348 2004 2,416 347 2003 2,323 318 2002 2,594 337 2001

  3. Property:Buildings/ReportNumber | Open Energy Information

    Open Energy Info (EERE)

    Jump to: navigation, search This is a property of type String. Pages using the property "BuildingsReportNumber" Showing 2 pages using this property. G General Merchandise 50%...

  4. Parameterized reduced-order models using hyper-dual numbers....

    Office of Scientific and Technical Information (OSTI)

    This report presents a methodology for developing parameterized ROMs, which is based on ... DOE Contract Number: AC04-94AL85000 Resource Type: Technical Report Research Org: Sandia ...

  5. Alaska Maximum Number of Active Crews Engaged in Seismic Surveying...

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

    Seismic Surveying (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2000's 13 4 23 12...

  6. Network Randomization and Dynamic Defense for Critical Infrastructure Systems

    SciTech Connect (OSTI)

    Chavez, Adrian R.; Martin, Mitchell Tyler; Hamlet, Jason; Stout, William M.S.; Lee, Erik

    2015-04-01

    Critical Infrastructure control systems continue to foster predictable communication paths, static configurations, and unpatched systems that allow easy access to our nation's most critical assets. This makes them attractive targets for cyber intrusion. We seek to address these attack vectors by automatically randomizing network settings, randomizing applications on the end devices themselves, and dynamically defending these systems against active attacks. Applying these protective measures will convert control systems into moving targets that proactively defend themselves against attack. Sandia National Laboratories has led this effort by gathering operational and technical requirements from Tennessee Valley Authority (TVA) and performing research and development to create a proof-of-concept solution. Our proof-of-concept has been tested in a laboratory environment with over 300 nodes. The vision of this project is to enhance control system security by converting existing control systems into moving targets and building these security measures into future systems while meeting the unique constraints that control systems face.

  7. Doorway states in the random-phase approximation

    SciTech Connect (OSTI)

    De Pace, A.; Molinari, A.; Weidenmüller, H.A.

    2014-12-15

    By coupling a doorway state to a sea of random background states, we develop the theory of doorway states in the framework of the random-phase approximation (RPA). Because of the symmetry of the RPA equations, that theory is radically different from the standard description of doorway states in the shell model. We derive the Pastur equation in the limit of large matrix dimension and show that the results agree with those of matrix diagonalization in large spaces. The complexity of the Pastur equation does not allow for an analytical approach that would approximately describe the doorway state. Our numerical results display unexpected features: The coupling of the doorway state with states of opposite energy leads to strong mutual attraction.

  8. Charting an Inflationary Landscape with Random Matrix Theory

    SciTech Connect (OSTI)

    Marsh, M.C. David; McAllister, Liam; Pajer, Enrico; Wrase, Timm E-mail: mcallister@cornell.edu E-mail: timm.wrase@stanford.edu

    2013-11-01

    We construct a class of random potentials for N >> 1 scalar fields using non-equilibrium random matrix theory, and then characterize multifield inflation in this setting. By stipulating that the Hessian matrices in adjacent coordinate patches are related by Dyson Brownian motion, we define the potential in the vicinity of a trajectory. This method remains computationally efficient at large N, permitting us to study much larger systems than has been possible with other constructions. We illustrate the utility of our approach with a numerical study of inflation in systems with up to 100 coupled scalar fields. A significant finding is that eigenvalue repulsion sharply reduces the duration of inflation near a critical point of the potential: even if the curvature of the potential is fine-tuned to be small at the critical point, small cross-couplings in the Hessian cause the curvature to grow in the neighborhood of the critical point.

  9. Random lasing in organo-lead halide perovskite microcrystal networks

    SciTech Connect (OSTI)

    Dhanker, R.; Brigeman, A. N.; Giebink, N. C.; Larsen, A. V.; Stewart, R. J.; Asbury, J. B.

    2014-10-13

    We report optically pumped random lasing in planar methylammonium lead iodide perovskite microcrystal networks that form spontaneously from spin coating. Low thresholds (<200??J/cm{sup 2}) and narrow linewidths (???100??m and spatially overlap with one another, resulting in chaotic pulse-to-pulse intensity fluctuations due to gain competition. These results demonstrate this class of hybrid organic-inorganic perovskite as a platform to study random lasing with well-defined, low-level disorder, and support the potential of these materials for use in semiconductor laser applications.

  10. Time of Flight

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

    beam is pulsed, the energy of the neutrons that are produced can be determined by Time-of-Flight (TOF) techniques. Neutron Time-of-Flight Since the LANSCE proton beam is...

  11. High resolution time interval counter

    DOE Patents [OSTI]

    Condreva, K.J.

    1994-07-26

    A high resolution counter circuit measures the time interval between the occurrence of an initial and a subsequent electrical pulse to two nanoseconds resolution using an eight megahertz clock. The circuit includes a main counter for receiving electrical pulses and generating a binary word--a measure of the number of eight megahertz clock pulses occurring between the signals. A pair of first and second pulse stretchers receive the signal and generate a pair of output signals whose widths are approximately sixty-four times the time between the receipt of the signals by the respective pulse stretchers and the receipt by the respective pulse stretchers of a second subsequent clock pulse. Output signals are thereafter supplied to a pair of start and stop counters operable to generate a pair of binary output words representative of the measure of the width of the pulses to a resolution of two nanoseconds. Errors associated with the pulse stretchers are corrected by providing calibration data to both stretcher circuits, and recording start and stop counter values. Stretched initial and subsequent signals are combined with autocalibration data and supplied to an arithmetic logic unit to determine the time interval in nanoseconds between the pair of electrical pulses being measured. 3 figs.

  12. High resolution time interval counter

    DOE Patents [OSTI]

    Condreva, Kenneth J. (Livermore, CA)

    1994-01-01

    A high resolution counter circuit measures the time interval between the occurrence of an initial and a subsequent electrical pulse to two nanoseconds resolution using an eight megahertz clock. The circuit includes a main counter for receiving electrical pulses and generating a binary word--a measure of the number of eight megahertz clock pulses occurring between the signals. A pair of first and second pulse stretchers receive the signal and generate a pair of output signals whose widths are approximately sixty-four times the time between the receipt of the signals by the respective pulse stretchers and the receipt by the respective pulse stretchers of a second subsequent clock pulse. Output signals are thereafter supplied to a pair of start and stop counters operable to generate a pair of binary output words representative of the measure of the width of the pulses to a resolution of two nanoseconds. Errors associated with the pulse stretchers are corrected by providing calibration data to both stretcher circuits, and recording start and stop counter values. Stretched initial and subsequent signals are combined with autocalibration data and supplied to an arithmetic logic unit to determine the time interval in nanoseconds between the pair of electrical pulses being measured.

  13. Physical Modeling of Spinel Crystals Settling at Low Reynolds Numbers

    Office of Scientific and Technical Information (OSTI)

    (Journal Article) | SciTech Connect Journal Article: Physical Modeling of Spinel Crystals Settling at Low Reynolds Numbers Citation Details In-Document Search Title: Physical Modeling of Spinel Crystals Settling at Low Reynolds Numbers The crystallization of large octahedral crystals of spinel during the high-level waste (HLW) vitrification process poses a potential danger to electrically heated ceramic melters. Large spinel crystals rapidly settle under gravitational attraction and

  14. Reducing the Particulate Emission Numbers in DI Gasoline Engines |

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Department of Energy the Particulate Emission Numbers in DI Gasoline Engines Reducing the Particulate Emission Numbers in DI Gasoline Engines Formation of droplets was minimized through optimization of fuel vaporization and distribution avoiding air/fuel zones richer than stoichiometric and temperatures that promote particle formation PDF icon deer10_klindt.pdf More Documents & Publications Bosch Powertrain Technologies Vehicle Emissions Review - 2012 Ethanol Effects on Lean-Burn and

  15. Treatment of the intrinsic Hamiltonian in particle-number nonconserving

    Office of Scientific and Technical Information (OSTI)

    theories (Journal Article) | SciTech Connect Treatment of the intrinsic Hamiltonian in particle-number nonconserving theories Citation Details In-Document Search Title: Treatment of the intrinsic Hamiltonian in particle-number nonconserving theories Authors: Hergert, H. ; Roth, R. Publication Date: 2009-11-01 OSTI Identifier: 1209398 Type: Published Article Journal Name: Physics Letters. Section B Additional Journal Information: Journal Volume: 682; Journal Issue: 1; Journal ID: ISSN

  16. California's Efforts for Advancing Ultrafine Particle Number Measurements

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    for Clean Diesel Exhaust | Department of Energy Efforts for Advancing Ultrafine Particle Number Measurements for Clean Diesel Exhaust California's Efforts for Advancing Ultrafine Particle Number Measurements for Clean Diesel Exhaust Presentation given at DEER 2006, August 20-24, 2006, Detroit, Michigan. Sponsored by the U.S. DOE's EERE FreedomCar and Fuel Partnership and 21st Century Truck Programs. PDF icon 2006_deer_huai.pdf More Documents & Publications Measurement of diesel solid

  17. Record Number Attend EM's Science Alliance | Department of Energy

    Energy Savers [EERE]

    Record Number Attend EM's Science Alliance Record Number Attend EM's Science Alliance October 30, 2013 - 12:00pm Addthis A record 1,200 students and educators visited EM’s Portsmouth Gaseous Diffusion Plant for the fourth annual Science Alliance. A record 1,200 students and educators visited EM's Portsmouth Gaseous Diffusion Plant for the fourth annual Science Alliance. PIKETON, Ohio - More than 1,200 students and educators from 23 southern Ohio schools visited EM's Portsmouth Gaseous

  18. Video: Recovery Act by the Numbers | Department of Energy

    Energy Savers [EERE]

    Recovery Act by the Numbers Video: Recovery Act by the Numbers February 17, 2016 - 11:30am Addthis Watch this video to learn how the Recovery Act helped jumpstart America's clean energy economy. | Video by Simon Edelman and graphics by Carly Wilkins, Energy Department. Paul Lester Paul Lester Digital Content Specialist, Office of Public Affairs Simon Edelman Simon Edelman Chief Creative Officer Carly Wilkins Carly Wilkins Multimedia Designer MORE ON THE RECOVERY ACT MAP: Learn about the impact

  19. INTERACTIVE: Energy Intensity and Carbon Intensity by the Numbers |

    Energy Savers [EERE]

    Department of Energy INTERACTIVE: Energy Intensity and Carbon Intensity by the Numbers INTERACTIVE: Energy Intensity and Carbon Intensity by the Numbers February 19, 2016 - 11:53am Addthis Daniel Wood Daniel Wood Data Visualization and Cartographic Specialist, Office of Public Affairs Watch our CO2 drop dramatically compared to other countries in this interactive Curious about the total amount of carbon we emit into the atmosphere? Compare countries from around the globe using this tool. If

  20. Time-Resolved

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

    and time) three correspond to the three broad categories of synchrotron experimental measurement techniques: spectroscopy (energy), scattering (momentum), and imaging...

  1. Intergranular degradation assessment via random grain boundary network analysis

    DOE Patents [OSTI]

    Kumar, Mukul (San Ramon, CA); Schwartz, Adam J. (Pleasanton, CA); King, Wayne E. (San Ramon, CA)

    2002-01-01

    A method is disclosed for determining the resistance of polycrystalline materials to intergranular degradation or failure (IGDF), by analyzing the random grain boundary network connectivity (RGBNC) microstructure. Analysis of the disruption of the RGBNC microstructure may be assess the effectiveness of materials processing in increasing IGDF resistance. Comparison of the RGBNC microstructures of materials exposed to extreme operating conditions to unexposed materials may be used to diagnose and predict possible onset of material failure due to

  2. Cryptographic synchronization recovery by measuring randomness of decrypted data

    DOE Patents [OSTI]

    Maestas, Joseph H. (Albuquerque, NM); Pierson, Lyndon G. (Albuquerque, NM)

    1990-01-01

    The invention relates to synchronization of encrypted data communication systems and a method which looks for any lack of pattern or intelligent information in the received data and triggers a resynchronization signal based thereon. If the encrypter/decrypter pairs are out of cryptographic synchronization, the received (decrypted) data resembles pseudorandom data. A method and system are provided for detecting such pseudorandom binary data by, for example, ones density. If the data is sufficiently random the system is resynchronized.

  3. Graphene materials having randomly distributed two-dimensional structural defects

    DOE Patents [OSTI]

    2013-10-08

    Graphene-based storage materials for high-power battery applications are provided. The storage materials are composed of vertical stacks of graphene sheets and have reduced resistance for Li ion transport. This reduced resistance is achieved by incorporating a random distribution of structural defects into the stacked graphene sheets, whereby the structural defects facilitate the diffusion of Li ions into the interior of the storage materials.

  4. Mid-Atomic-Number Cylindrical Wire Array Precursor Plasma Studies on Zebra

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Stafford, A; Safronova, A. S.; Kantsyrev, V. L.; Coverdale, Christine Anne; Weller, M. E.; Shrestha, I.; Shlyaptseva, V. V.; Chuvatin, A. S.

    2014-12-30

    The precursor plasmas from low wire number cylindrical wire arrays (CWAs) were previously shown to radiate at temperatures >300 eV for Ni-60 (94% Cu and 6% Ni) wires in experiments on the 1-MA Zebra generator. Continued research into precursor plasmas has studied additional midatomic-number materials including Cu and Alumel (95% Ni, 2% Al, 2% Mn, and 1% Si) to determine if the >300 eV temperatures are common for midatomic-number materials. Additionally, current scaling effects were observed by performing CWA precursor experiments at an increased current of 1.5 MA using a load current multiplier. Our results show an increase in amore » linear radiation yield of ~50% (16 versus 10 kJ/cm) for the experiments at increased current. However, plasma conditions inferred through the modeling of X-ray time-gated spectra are very similar for the precursor plasma in both current conditions.« less

  5. IMPACT OF CAPILLARY AND BOND NUMBERS ON RELATIVE PERMEABILITY

    SciTech Connect (OSTI)

    Kishore K. Mohanty

    2002-09-30

    Recovery and recovery rate of oil, gas and condensates depend crucially on their relative permeability. Relative permeability in turn depends on the pore structure, wettability and flooding conditions, which can be represented by a set of dimensionless groups including capillary and bond numbers. The effect of flooding conditions on drainage relative permeabilities is not well understood and is the overall goal of this project. This project has three specific objectives: to improve the centrifuge relative permeability method, to measure capillary and bond number effects experimentally, and to develop a pore network model for multiphase flows. A centrifuge has been built that can accommodate high pressure core holders and x-ray saturation monitoring. The centrifuge core holders can operate at a pore pressure of 6.9 MPa (1000 psi) and an overburden pressure of 17 MPa (2500 psi). The effect of capillary number on residual saturation and relative permeability in drainage flow has been measured. A pore network model has been developed to study the effect of capillary numbers and viscosity ratio on drainage relative permeability. Capillary and Reynolds number dependence of gas-condensate flow has been studied during well testing. A method has been developed to estimate relative permeability parameters from gas-condensate well test data.

  6. RL-721 REV7 I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    1 MSA Annual Categorical Exclusion for Air Conditioning Systems for Existing Equipment under 10 CFR 1021, Subpart D, Appendix B, B1.4 for Calendar Year 2014 II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions - e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform installation or modification of air conditioning systems

  7. RL-721 REV7 I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    6, Rev 1 MSA Annual Categorical Exclusion for Relocation of Buildings under 10 CFR 1021, Subpart D, Appendix B, Bl.22 for Calendar Year 2014 II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions -e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform relocation of buildings (including, but not limited to, trailers and

  8. RL-721 REV7 I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    19, Rev 1 MSA Annual Categorical Exclusion for Traffic Flow Adjustments under 10 CFR 1021, Subpart D, Appendix B, Bl.32 for Calendar Year 2014 II. Project Description and location (including Time Period over which proposed action will occur and Project Dimensions -e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform traffic flow adjustments to existing roads (including, but not limited

  9. RL-721 REV7 I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    3, Rev 1 MSA Annual Categorical Exclusion for Site Characterization and Environmental Monitoring under 10 CFR 1021, Subpart D, Appendix B, B3.1 for Calendar Year 2014 II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions -e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform site characterization and environmental

  10. RL-721 REV7 I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    2 MSA Annual Categorical Exclusion for Air Conditioning Systems for Existing Equipment under 10 CFR 1021, Subpart D, Appendix B, B1.4 for Calendar Year 2015. II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions - e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform installation or modification of air conditioning systems

  11. RL-721 REV7 I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    2 MSA Annual Categorical Exclusion for Asbestos Removal under 10 CFR 1021, Subpart D, Appendix B, Bl.l6 for Calendar Year 2015. II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions - e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors remove asbestos-containing materials (ACM) from buildings in accordance with applicable

  12. RL-721 REV7 I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    8, Rev 2 MSA Annual Categorical Exclusion for Installation or Relocation of Machinery and Equipment under 10 CFR 1021, Subpart D, Appendix B, B1.31 for Calendar Year 2015. 11. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions - e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform installation or relocation and operation of

  13. RL-721 REV7 I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    2 MSA Annual Categorical Exclusion for Oil Spill Cleanup under 10 CFR 1021, Subpart D, Appendix B, B5.6 for Calendar Year 2015. II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions -e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA} and its subcontractors perform removal of oil and contaminated materials recovered in oil spill cleanup operations and

  14. RL-721 REV7 I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    42 Radiological Survey Activities in the 600 Area of the Hanford Site Supporting Land Conveyance II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions - e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): The U.S. Department of Energy, Richland Operations (DOE-RL) proposes to conduct radiological surveys of a portion of the 600 Area of the Hanford Site. The surveys are needed to

  15. RL-721 REV? I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    9, Rev 2 MSA Annual Categorical Exclusion for Training Exercises and Simulations under 10 CFR 1021, Subpart D, Appendix B, Bl.2 for Calendar Year 2014 II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions -e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform training exercises and simulations (including, but not limited

  16. RL-721 REV? I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    1 MSA Annual Categorical Exclusion for Routine Maintenance and Custodial Services under 10 CFR 1021, Subpart D, Appendix B, 81.3 for Calendar Year 2014 II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions -e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform routine maintenance activities and custodial services for

  17. RL-721 REV? I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    8, Rev 1 MSA Annual Categorical Exclusion for Installation or Relocation of Machinery and Equipment under 10 CFR 1021, Subpart D, Appendix B, B1.31 for Calendar Year 2014 II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions - e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform installation or relocation and operation of

  18. RL-721 REV? I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    1 MSA Annual Categorical Exclusion for Drop-Off, Collection, and Transfer Facilities for Recyclable Materials under 10 CFR 1021, Subpart D, Appendix B, Bl.35 for Calendar Year 2014 II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions -e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform siting, construction, modification,

  19. RL-721 REV? I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    1 MSA Annual Categorical Exclusion for Facility Safety and Environmental Improvements under 10 CFR 1021, Subpart D, Appendix B, B2.5 for Calendar Year 2014 II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions - e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform safety and environmental improvements of a facility

  20. RL-721 REV? I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    5, Rev 1 MSA Annual Categorical Exclusion for Actions to Conserve Energy or Water under 10 CFR 1021, Subpart D, Appendix B, B5.1 for Calendar Year 2014 II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions - e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform actions to conserve energy or water, demonstrate potential

  1. RL-721 REV? I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    9, Rev 1 MSA Annual Categorical Exclusion for Facilities to Store Packaged Hazardous Waste for 90 Days or Less under 10 CFR 1021, Subpart D, Appendix B, B6.4 for Calendar Year 2014 II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions -e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform siting, construction, modification,

  2. RL-721 REV? I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    3, Rev 2 MSA Annual Categorical Exclusion for Support Buildings under 10 CFR 1021, Subpart D, Appendix B, Bl.l5 for Calendar Year 2015. II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions -e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform siting, construction or modification, and operation of support buildings and

  3. RL-721 REV? I. Project Title: NEPA REVIEW SCREENING FORM Document ID Number:

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

    2 MSA Annual Categorical Exclusion for Facility Safety and Environmental Improvements under 10 CFR 1021, Subpart D, Appendix B, B2.5 for Calendar Year 2015. II. Project Description and Location (including Time Period over which proposed action will occur and Project Dimensions -e.g., acres displaced/disturbed, excavation length/depth, area/location/number of buildings, etc.): Mission Support Alliance (MSA) and its subcontractors perform safety and environmental improvements of a facility

  4. Table HC6.10 Home Appliances Usage Indicators by Number of Household Members, 2005

    Gasoline and Diesel Fuel Update (EIA)

    0 Home Appliances Usage Indicators by Number of Household Members, 2005 Total.............................................................................. 111.1 30.0 34.8 18.4 15.9 12.0 Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day........................................... 8.2 1.4 1.9 1.4 1.0 2.4 2 Times A Day........................................................ 24.6 4.3 7.6 4.3 4.8 3.7 Once a Day............................................................ 42.3 9.9

  5. Proton NMR analysis of octane number for motor gasoline: Part IV

    SciTech Connect (OSTI)

    Ichikawa, M.; Nonaka, N.; Amano, H.; Takada, I.; Ishimori, S.; Andoh, H.; Kumamoto, K.

    1992-08-01

    Software for predicting the octane number of motor gasoline by proton magnetic resonance (PMR) spectrometry has been formulated. At the same time, a method has been studied to predict the composition of gasoline (in terms of the contents of paraffin, olefin, and aromatic compounds). The formulated program was evaluated by using it to predict the octane numbers of 31 samples of marketed summer gasoline (including 16 regular and 15 premium products), whose octane numbers and compositions were identified according to the ASTM standards. Also, the relationship between the PMR spectrum and gasoline composition was subjected to linear regression analysis by using the 31 samples whose octane numbers were calculated, and the appropriateness of the resultant regression equations was assessed. This report concerns the results of the study in which the octane numbers of the 31 samples were satisfactorily predicted by the formulated program and useful linear regression equation were obtained for the prediction of the composition of gasoline. 9 refs., 9 figs., 3 tabs.

  6. STATEMENT AND ACKNOWLEDGMENT OMB Control Number: 9000-0014

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

    ACKNOWLEDGMENT OMB Control Number: 9000-0014 Expiration Date: 12/31/2017 PART I - STATEMENT OF PRIME CONTRACTOR 1. PRIME CONTRACT NO. 2. DATE SUBCONTRACT AWARDED 3. SUBCONTRACT NUMBER 15b. TITLE OF PERSON SIGNING AUTHORIZED FOR LOCAL REPRODUCTION PREVIOUS EDITION IS NOT USABLE STANDARD FORM 1413 (REV. 4/2013) Prescribed by GSA/FAR (48 CFR) 53.222(e) 4. PRIME CONTRACTOR 5. SUBCONTRACTOR a. NAME a. NAME b. STREET ADDRESS b. STREET ADDRESS c. CITY d. STATE e. ZIP CODE c. CITY d. STATE e. ZIP CODE

  7. Contract Number DE-AC27-10RV15051

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

    Contract Number DE-AC27-10RV15051 Modification 106 SF-30 Attachment Attachment DE-AC27-10RV15051 MODIFICATION 106 Replacement Pages (Total: 53, including this Cover Page)  Section B.1, Type of Contract - Items Being Acquired, Page B-8  Section H, Special Contract Requirements, Pages i, ii, and H-27  Section I, Contract Clauses, Pages I-1 thru I-48 222-S LAS&T Contract DE-AC27-10RV15051 Conformed thru Contract Modification No. 106 B-8 (e) OPTION PERIOD III: CLIN Number Description

  8. Method for rapidly determining a pulp kappa number using spectrophotometry

    DOE Patents [OSTI]

    Chai, Xin-Sheng (Atlanta, GA); Zhu, Jun Yong (Marietta, GA)

    2002-01-01

    A system and method for rapidly determining the pulp kappa number through direct measurement of the potassium permanganate concentration in a pulp-permanganate solution using spectrophotometry. Specifically, the present invention uses strong acidification to carry out the pulp-permanganate oxidation reaction in the pulp-permanganate solution to prevent the precipitation of manganese dioxide (MnO.sub.2). Consequently, spectral interference from the precipitated MnO.sub.2 is eliminated and the oxidation reaction becomes dominant. The spectral intensity of the oxidation reaction is then analyzed to determine the pulp kappa number.

  9. Treatability study Number PDC-1-O-T. Final report

    SciTech Connect (OSTI)

    1998-04-22

    Los Alamos National Laboratory provided treatability study samples from four waste streams, designated Stream {number_sign}1, Stream {number_sign}3, Stream {number_sign}6, and Stream {number_sign}7. Stream {number_sign}1 consisted of one 55-gallon drum of personal protective equipment (PPE), rags, and neutralizing agent (bicarbonate) generated during the cleanup of a sodium dichromate solution spill. Stream {number_sign}3 was one 55-gallon drum of paper, rags, lab utensils, tools, and tape from the decontamination of a glovebox. The sample of Stream {number_sign}6 was packaged in three 30-gallon drums and a 100 ft{sup 3} wooden box. It consisted of plastic sheeting, PPE, and paper generated from the cleanup of mock explosive (barium nitrate) from depleted uranium parts. Stream {number_sign}7 was scrap metal (copper, stainless and carbon steel joined with silver solder) from the disassembly of gas manifolds. The objective of the treatability study is to determine: (1) whether the Perma-Fix stabilization/solidification process can treat the waste sample to meet Land Disposal Restrictions and the Waste Acceptance Criteria for LANL Technical Area 54, Area G, and (2) optimum loading and resulting weight and volume of finished waste form. The stabilized waste was mixed into grout that had been poured into a lined drum. After each original container of waste was processed, the liner was closed and a new liner was placed in the same drum on top of the previous closed liner. This allowed an overall reduction in waste volume but kept waste segregated to minimize the amount of rework in case analytical results indicated any batch did not meet treatment standards. Samples of treated waste from each waste stream were analyzed by Perma-Fix Analytical Services to get a preliminary approximation of TCLP metals. Splits of these samples were sent to American Environmental Network`s mixed waste analytical lab in Cary, NC for confirmation analysis. Results were all below applicable limits.

  10. Table B8. Year Constructed, Number of Buildings, 1999

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

    B8. Year Constructed, Number of Buildings, 1999" ,"Number of Buildings (thousand)" ,"All Buildings","Year Constructed" ,,"1919 or Before","1920 to 1945","1946 to 1959","1960 to 1969","1970 to 1979","1980 to 1989","1990 to 1999" "All Buildings ................",4657,419,499,763,665,774,846,690 "Building Floorspace" "(Square Feet)" "1,001 to 5,000

  11. Energy By The Numbers: Recovery Act | Department of Energy

    Energy Savers [EERE]

    By The Numbers: Recovery Act Energy By The Numbers: Recovery Act Addthis America is now a world leader in clean energy. But how did we get there? One key reason is the Recovery Act of 2009, a historic investment to revitalize the economy during the worst financial crisis since the Great Depression. This investment created millions of jobs -- including thousands of clean energy jobs in sectors that never even existed before. For example, in 2009 there was not a single utility-scale photovoltaic

  12. Stochastic simulation for the propagation of high-frequency acoustic waves through a random velocity field

    SciTech Connect (OSTI)

    Lu, B.; Darmon, M.; Leymarie, N.; Chatillon, S.; Potel, C.

    2012-05-17

    In-service inspection of Sodium-Cooled Fast Reactors (SFR) requires the development of non-destructive techniques adapted to the harsh environment conditions and the examination complexity. From past experiences, ultrasonic techniques are considered as suitable candidates. The ultrasonic telemetry is a technique used to constantly insure the safe functioning of reactor inner components by determining their exact position: it consists in measuring the time of flight of the ultrasonic response obtained after propagation of a pulse emitted by a transducer and its interaction with the targets. While in-service the sodium flow creates turbulences that lead to temperature inhomogeneities, which translates into ultrasonic velocity inhomogeneities. These velocity variations could directly impact the accuracy of the target locating by introducing time of flight variations. A stochastic simulation model has been developed to calculate the propagation of ultrasonic waves in such an inhomogeneous medium. Using this approach, the travel time is randomly generated by a stochastic process whose inputs are the statistical moments of travel times known analytically. The stochastic model predicts beam deviations due to velocity inhomogeneities, which are similar to those provided by a determinist method, such as the ray method.

  13. Random effects of fissile lumps in molten salt reactors

    SciTech Connect (OSTI)

    Dulla, S.; Ravetto, P.; Prinja, A. K.

    2013-07-01

    The problem of the effect of fissile lumps spatially appearing in a random fashion inside a fluid fuel reactor is addressed. The effect on reactivity is evaluated by means of first-order perturbation theory. The analysis is carried out in diffusion theory with the presence of delayed neutron emissions in one dimensional plane geometry. The estimation of the mean value and standard deviation of the reactivity inserted is performed by Monte Carlo simulations and a deterministic quadrature approach, to compare the methods in terms of computational effort and the accuracy of the results. The results presented show that the effects constitute an important issue in the assessment of these innovative systems. (authors)

  14. Plasmonic modes and extinction properties of a random nanocomposite cylinder

    SciTech Connect (OSTI)

    Moradi, Afshin

    2014-04-15

    We study the properties of surface plasmon-polariton waves of a random metal-dielectric nanocomposite cylinder, consisting of bulk metal embedded with dielectric nanoparticles. We use the Maxwell-Garnett formulation to model the effective dielectric function of the composite medium and show that there exist two surface mode bands. We investigate the extinction properties of the system, and obtain the dependence of the extinction spectrum on the nanoparticles shape and concentration as well as the cylinder radius and the incidence angle for both TE and TM polarization.

  15. Survey of lepton number violation via effective operators

    SciTech Connect (OSTI)

    Gouvea, Andre de; Jenkins, James [Northwestern University, Department of Physics and Astronomy, 2145 Sheridan Road, Evanston, Illinois 60208 (United States)

    2008-01-01

    We survey 129 lepton number violating effective operators, consistent with the minimal standard model gauge group and particle content, of mass dimension up to and including 11. Upon requiring that each one radiatively generates the observed neutrino masses, we extract an associated characteristic cutoff energy scale which we use to calculate other observable manifestations of these operators for a number of current and future experimental probes, concentrating on lepton number violating phenomena. These include searches for neutrinoless double-beta decay and rare meson, lepton, and gauge boson decays. We also consider searches at hadron/lepton collider facilities in anticipation of the CERN LHC and the future ILC. We find that some operators are already disfavored by current data, while more are ripe to be probed by next-generation experiments. We also find that our current understanding of lepton mixing disfavors a subset of higher dimensional operators. While neutrinoless double-beta decay is the most promising signature of lepton number violation for the majority of operators, a handful is best probed by other means. We argue that a combination of constraints from various independent experimental sources will help to pinpoint the ''correct'' model of neutrino mass, or at least aid in narrowing down the set of possibilities.

  16. Energy Intensity and Carbon Intensity by the Numbers | Department of Energy

    Energy Savers [EERE]

    Intensity and Carbon Intensity by the Numbers Energy Intensity and Carbon Intensity by the Numbers

  17. MAGNETIC FIELD LINE RANDOM WALK IN ISOTROPIC TURBULENCE WITH ZERO MEAN FIELD

    SciTech Connect (OSTI)

    Sonsrettee, W.; Ruffolo, D.; Snodin, A. P.; Wongpan, P. [Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400 (Thailand); Subedi, P.; Matthaeus, W. H. [Bartol Research Institute, University of Delaware, Newark, DE 19716 (United States); Chuychai, P., E-mail: bturbulence@gmail.com, E-mail: david.ruf@mahidol.ac.th, E-mail: andrew.snodin@gmail.com, E-mail: pat.wongpan@postgrad.otago.ac.nz, E-mail: piyanate@gmail.com, E-mail: prasub@udel.edu, E-mail: whm@udel.edu [Thailand Center of Excellence in Physics, CHE, Ministry of Education, Bangkok 10400 (Thailand)

    2015-01-01

    In astrophysical plasmas, magnetic field lines often guide the motions of thermal and non-thermal particles. The field line random walk (FLRW) is typically considered to depend on the Kubo number R = (b/B {sub 0})(?{sub ?}/? ) for rms magnetic fluctuation b, large-scale mean field B {sub 0}, and parallel and perpendicular coherence scales ?{sub ?} and ? , respectively. Here we examine the FLRW when R ? ? by taking B {sub 0} ? 0 for finite b{sub z} (fluctuation component along B {sub 0}), which differs from the well-studied route with b{sub z} = 0 or b{sub z} << B {sub 0} as the turbulence becomes quasi-two-dimensional (quasi-2D). Fluctuations with B {sub 0} = 0 are typically isotropic, which serves as a reasonable model of interstellar turbulence. We use a non-perturbative analytic framework based on Corrsin's hypothesis to determine closed-form solutions for the asymptotic field line diffusion coefficient for three versions of the theory, which are directly related to the k {sup 1} or k {sup 2} moment of the power spectrum. We test these theories by performing computer simulations of the FLRW, obtaining the ratio of diffusion coefficients for two different parameterizations of a field line. Comparing this with theoretical ratios, the random ballistic decorrelation version of the theory agrees well with the simulations. All results exhibit an analog to Bohm diffusion. In the quasi-2D limit, previous works have shown that Corrsin-based theories deviate substantially from simulation results, but here we find that as B {sub 0} ? 0, they remain in reasonable agreement. We conclude that their applicability is limited not by large R, but rather by quasi-two-dimensionality.

  18. Large-Scale Uncertainty and Error Analysis for Time-dependent Fluid/Structure Interactions in Wind Turbine Applications

    SciTech Connect (OSTI)

    Alonso, Juan J.; Iaccarino, Gianluca

    2013-08-25

    The following is the final report covering the entire period of this aforementioned grant, June 1, 2011 - May 31, 2013 for the portion of the effort corresponding to Stanford University (SU). SU has partnered with Sandia National Laboratories (PI: Mike S. Eldred) and Purdue University (PI: Dongbin Xiu) to complete this research project and this final report includes those contributions made by the members of the team at Stanford. Dr. Eldred is continuing his contributions to this project under a no-cost extension and his contributions to the overall effort will be detailed at a later time (once his effort has concluded) on a separate project submitted by Sandia National Laboratories. At Stanford, the team is made up of Profs. Alonso, Iaccarino, and Duraisamy, post-doctoral researcher Vinod Lakshminarayan, and graduate student Santiago Padron. At Sandia National Laboratories, the team includes Michael Eldred, Matt Barone, John Jakeman, and Stefan Domino, and at Purdue University, we have Prof. Dongbin Xiu as our main collaborator. The overall objective of this project was to develop a novel, comprehensive methodology for uncertainty quantification by combining stochastic expansions (nonintrusive polynomial chaos and stochastic collocation), the adjoint approach, and fusion with experimental data to account for aleatory and epistemic uncertainties from random variable, random field, and model form sources. The expected outcomes of this activity were detailed in the proposal and are repeated here to set the stage for the results that we have generated during the time period of execution of this project: 1. The rigorous determination of an error budget comprising numerical errors in physical space and statistical errors in stochastic space and its use for optimal allocation of resources; 2. A considerable increase in efficiency when performing uncertainty quantification with a large number of uncertain variables in complex non-linear multi-physics problems; 3. A solution to the long-time integration problem of spectral chaos approaches; 4. A rigorous methodology to account for aleatory and epistemic uncertainties, to emphasize the most important variables via dimension reduction and dimension-adaptive refinement, and to support fusion with experimental data using Bayesian inference; 5. The application of novel methodologies to time-dependent reliability studies in wind turbine applications including a number of efforts relating to the uncertainty quantification in vertical-axis wind turbine applications. In this report, we summarize all accomplishments in the project (during the time period specified) focusing on advances in UQ algorithms and deployment efforts to the wind turbine application area. Detailed publications in each of these areas have also been completed and are available from the respective conference proceedings and journals as detailed in a later section.

  19. Digital time delay

    DOE Patents [OSTI]

    Martin, A.D.

    1986-05-09

    Method and apparatus are provided for generating an output pulse following a trigger pulse at a time delay interval preset with a resolution which is high relative to a low resolution available from supplied clock pulses. A first lumped constant delay provides a first output signal at predetermined interpolation intervals corresponding to the desired high resolution time interval. Latching circuits latch the high resolution data to form a first synchronizing data set. A selected time interval has been preset to internal counters and corrected for circuit propagation delay times having the same order of magnitude as the desired high resolution. Internal system clock pulses count down the counters to generate an internal pulse delayed by an internal which is functionally related to the preset time interval. A second LCD corrects the internal signal with the high resolution time delay. A second internal pulse is then applied to a third LCD to generate a second set of synchronizing data which is complementary with the first set of synchronizing data for presentation to logic circuits. The logic circuits further delay the internal output signal with the internal pulses. The final delayed output signal thereafter enables the output pulse generator to produce the desired output pulse at the preset time delay interval following input of the trigger pulse.

  20. Parallel time integration software

    Energy Science and Technology Software Center (OSTI)

    2014-07-01

    This package implements an optimal-scaling multigrid solver for the (non) linear systems that arise from the discretization of problems with evolutionary behavior. Typically, solution algorithms for evolution equations are based on a time-marching approach, solving sequentially for one time step after the other. Parallelism in these traditional time-integrarion techniques is limited to spatial parallelism. However, current trends in computer architectures are leading twards system with more, but not faster. processors. Therefore, faster compute speeds mustmore » come from greater parallelism. One approach to achieve parallelism in time is with multigrid, but extending classical multigrid methods for elliptic poerators to this setting is a significant achievement. In this software, we implement a non-intrusive, optimal-scaling time-parallel method based on multigrid reduction techniques. The examples in the package demonstrate optimality of our multigrid-reduction-in-time algorithm (MGRIT) for solving a variety of parabolic equations in two and three sparial dimensions. These examples can also be used to show that MGRIT can achieve significant speedup in comparison to sequential time marching on modern architectures.« less

  1. Code for Calculating Regional Seismic Travel Time

    Energy Science and Technology Software Center (OSTI)

    2009-07-10

    The RSTT software computes predictions of the travel time of seismic energy traveling from a source to a receiver through 2.5D models of the seismic velocity distribution within the Earth. The two primary applications for the RSTT library are tomographic inversion studies and seismic event location calculations. In tomographic inversions studies, a seismologist begins with number of source-receiver travel time observations and an initial starting model of the velocity distribution within the Earth. A forwardmore » travel time calculator, such as the RSTT library, is used to compute predictions of each observed travel time and all of the residuals (observed minus predicted travel time) are calculated. The Earth model is then modified in some systematic way with the goal of minimizing the residuals. The Earth model obtained in this way is assumed to be a better model than the starting model if it has lower residuals. The other major application for the RSTT library is seismic event location. Given an Earth model, an initial estimate of the location of a seismic event, and some number of observations of seismic travel time thought to have originated from that event, location codes systematically modify the estimate of the location of the event with the goal of minimizing the difference between the observed and predicted travel times. The second application, seismic event location, is routinely implemented by the military as part of its effort to monitor the Earth for nuclear tests conducted by foreign countries.« less

  2. VARIABLE TIME DELAY MEANS

    DOE Patents [OSTI]

    Clemensen, R.E.

    1959-11-01

    An electrically variable time delay line is described which may be readily controlled simuitaneously with variable impedance matching means coupied thereto such that reflections are prevented. Broadly, the delay line includes a signal winding about a magnetic core whose permeability is electrically variable. Inasmuch as the inductance of the line varies directly with the permeability, the time delay and characteristic impedance of the line both vary as the square root of the permeability. Consequently, impedance matching means may be varied similariy and simultaneously w:th the electrically variable permeability to match the line impedance over the entire range of time delay whereby reflections are prevented.

  3. X Time Series

    Energy Savers [EERE]

    11 Figure 5-11: 1-Hour Ozone Time Series Observed (C506) v. Predicted (CAMx) for WRF AACOG Base Case Run 3, 2006 5-12 5.3.2 Hourly NO X Time Series Time series plots of modeled and predicted hourly NO X for each monitor located in the San Antonio MSA were constructed. The model over predicted NO X emissions at the C58 monitor on almost every day during the June 2006 episode. The average predicted hourly NO X was 7.3 ppb, while the average observed hourly NO X was only 3.9 ppb. Likewise, the

  4. Drug Retention Times

    SciTech Connect (OSTI)

    Center for Human Reliability Studies

    2007-05-01

    The purpose of this monograph is to provide information on drug retention times in the human body. The information provided is based on plausible illegal drug use activities that might be engaged in by a recreational drug user.

  5. Drug Retention Times

    SciTech Connect (OSTI)

    Center for Human Reliability Studies

    2007-05-01

    The purpose of this monograph is to provide information on drug retention times in the human body. The information provided is based on plausible illegal drug use activities that might be engaged in by a recreational drug user

  6. Time-Resolved

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

    Ultrafast Spectroscopy of Warm Dense Matter Real-Time Chemical Imaging of Bacterial Biofilm Development Using Light to Control How X Rays Interact with Matter X-Ray Imaging of...

  7. Emergence of pygmy dipole resonances: Magic numbers and neutron skins

    SciTech Connect (OSTI)

    Inakura, Tsunenori; Nakatsukasa, Takashi; Yabana, Kazuhiro

    2011-08-15

    The pygmy dipole resonances (PDR) for even-even nuclei in 8{<=}Z{<=}40 are studied performing a systematic calculation of the random-phase approximation with the Skyrme functional of SkM*. The calculation is fully self-consistent and does not assume any symmetry in the nuclear shape of the ground state. In every isotopic chain, the PDR emerges by showing a peak of the E1 strength at energies less than 10 MeV. The E1 strength of the PDR strongly depends on the position of the Fermi level and shows a clear correlation with the occupation of the orbits with the orbital angular momenta less than 3({h_bar}/2{pi}) (l{<=}2). We also found a strong correlation between the isotopic dependence of the neutron skin thickness and the pygmy dipole strength. The fraction of the energy-weighted strength exhausted by the PDR and the neutron skin thickness show a linear correlation with the universal rate of about 0.2 fm{sup -1}.

  8. Quarkyonic Matter and Quark Number Scaling of Elliptic Flow

    SciTech Connect (OSTI)

    Csernai, L. P.; Zschocke, S.; Horvat, Sz.; Cheng Yun; Mishustin, I. N.

    2011-05-23

    The constituent quark number scaling of elliptic flow is studied in a non-equilibrium hadronization and freeze-out model with rapid dynamical transition from ideal, deconfined and chirally symmetric Quark Gluon Plasma, to final non-interacting hadrons. In this transition a Bag model of constituent quarks is considered, where the quarks gain constituent quark mass while the background Bag-field breaks up and vanishes. The constituent quarks then recombine into simplified hadron states, while chemical, thermal and flow equilibrium break down one after the other. In this scenario the resulting temperatures and flow velocities of baryons and mesons are different. Using a simplified few source model of the elliptic flow, we are able to reproduce the constituent quark number scaling, with assumptions on the details of the non-equilibrium processes.

  9. Table B6. Building Size, Number of Buildings, 1999

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

    B6. Building Size, Number of Buildings, 1999" ,"Number of Buildings (thousand)" ,"All Buildings ","Building Size" ,,"1,001 to 5,000 Square Feet","5,001 to 10,000 Square Feet","10,001 to 25,000 Square Feet","25,001 to 50,000 Square Feet","50,001 to 100,000 Square Feet","100,001 to 200,000 Square Feet","200,001 to 500,000 Square Feet","Over 500,000 Square Feet" "All Buildings

  10. Regulation Indentifier Number Title/Subject/Purpose Rule Type

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    as of 10/26/2015 Regulation Indentifier Number Title/Subject/Purpose Rule Type Status 1990-AA40 Adminstrative Requirements for Other Transactions: revise requirements for technology investment agreements to broaden to support all types of other transactions. NOPR Federal Register notice drafted and under review for significance determination per EO 12866 1991-AB73 Property Management Regulation: Update and eliminate inconsistencies and redundancies in DOE's personal property management

  11. Regulation Indentifier Number Title/Subject/Purpose Rule Type

    Energy Savers [EERE]

    as of 3/10/2016 The information contained herein is current as of its last update. Updates are only accomplished periodically for the entire list. For the official status of a specific rule, please see the Federal Register at https://www.regulations.gov/. Regulation Indentifier Number Title/Subject/Purpose Rule Type Status 1990-AA40 Adminstrative Requirements for Other Transactions: revise requirements for technology investment agreements to broaden to support all types of other transactions.

  12. FINAL MECHANICAL EXAMINATION FORM PS-6 Pressure System Number:

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

    MECHANICAL EXAMINATION FORM PS-6 Pressure System Number: Pressure System Name: Design Authority: CHECK IF COMPLETE, N/A IF NOT APPLICABLE: Materials, components and products meet specifications and the requirements of engineering design Applicable procedures for assembly, glue bonding, etc. Assembly of threaded, bolted and other joints conforms to Code and engineering design Alignment, supports and/or cold spring meet engineering design Dimensional checks of components and materials meet Code

  13. Inventive Thinkers at NREL Reach Record Number - News Feature | NREL

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

    Inventive Thinkers at NREL Reach Record Number December 17, 2015 A man wearing safety glasses stands next to large solar panels. NREL engineer Chris Deline came up with an idea for a device that would allow firefighters to quickly power down solar panels in case of a fire. Photo by Dennis Schroeder The eureka moments can come fast and furious at the Energy Department's National Renewable Energy Laboratory (NREL), which last fiscal year racked up a record 169 inventions. Chris Deline's idea came

  14. Maria Goeppert Mayer, the Nuclear Shell Structure, and Magic Numbers

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

    Maria Goeppert-Mayer, the Nuclear Shell Model, and Magic Numbers Resources with Additional Information Maria Goeppert-Mayer Courtesy Argonne National Laboratory While working at Argonne National Laboratory (ANL) in 1948, physicist Maria Goeppert-Mayer developed the explanation of how neutrons and protons within atomic nuclei are structured. Called the "nuclear shell model," her work explains why the nuclei of some atoms are more stable than others and why some elements have many

  15. Chicago Office NEPA Tracking Number U. S. DEPARTMENT OF ENERGY

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    SFCH F 560-ACQ (11/05) Previous editions are obsolete. ~1,6~.. --1 b Chicago Office NEPA Tracking Number U. S. DEPARTMENT OF ENERGY OFFICE OF SCIENCE -- CHICAGO OFFICE NATIONAL ENVIRONMENTAL POLICY ACT (NEPA) ENVIRONMENTAL EVALUATION NOTIFICATION FORM To be completed by "financial assistance award" organization receiving Federal funding. For assistance (including a point of contact), see "Instructions for Preparing SC-CH F-560, Environmental Evaluation Notification Form ".

  16. MENTEE QUESTIONNAIRE Name: Title: Email: Office Phone Number:

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

    MENTEE QUESTIONNAIRE Name: Title: Email: Office Phone Number: Office Address: Why are you interested in the mentoring program? (This information will be included with the invitation to your potential mentor.) What goals do you want to work on during your participation in the mentoring program? Is there someone you would like to be your mentor? Yes No If yes, please list their name and any other possible mentors in order of preference: Expectations of the Mentoring Program How long? 6-months

  17. NNSA Achievements: 2015 by the Numbers | National Nuclear Security

    National Nuclear Security Administration (NNSA)

    Administration Achievements: 2015 by the Numbers | National Nuclear Security Administration Facebook Twitter Youtube Flickr RSS People Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Countering Nuclear Terrorism About Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Library Bios Congressional Testimony Fact Sheets Newsletters Press Releases Photo Gallery Jobs Apply for

  18. VIDEO: 2015 By the Numbers | National Nuclear Security Administration

    National Nuclear Security Administration (NNSA)

    VIDEO: 2015 By the Numbers | National Nuclear Security Administration Facebook Twitter Youtube Flickr RSS People Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Countering Nuclear Terrorism About Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Library Bios Congressional Testimony Fact Sheets Newsletters Press Releases Photo Gallery Jobs Apply for Our Jobs Our Jobs Working

  19. SOLAR CYCLE 24: CURIOUS CHANGES IN THE RELATIVE NUMBERS OF SUNSPOT GROUP TYPES

    SciTech Connect (OSTI)

    Kilcik, A.; Yurchyshyn, V. B.; Ozguc, A.; Rozelot, J. P.

    2014-10-10

    Here, we analyze different sunspot group (SG) behaviors from the points of view of both the sunspot counts (SSCs) and the number of SGs, in four categories, for the time period of 1982 January-2014 May. These categories include data from simple (A and B), medium (C), large (D, E, and F), and decaying (H) SGs. We investigate temporal variations of all data sets used in this study and find the following results. (1) There is a very significant decrease in the large groups' SSCs and the number of SGs in solar cycle 24 (cycle 24) compared to cycles 21-23. (2) There is no strong variation in the decaying groups' data sets for the entire investigated time interval. (3) Medium group data show a gradual decrease for the last three cycles. (4) A significant decrease occurred in the small groups during solar cycle 23, while no strong changes show in the current cycle (cycle 24) compared to the previous ones. We confirm that the temporal behavior of all categories is quite different from cycle to cycle and it is especially flagrant in solar cycle 24. Thus, we argue that the reduced absolute number of the large SGs is largely, if not solely, responsible for the weak cycle 24. These results might be important for long-term space weather predictions to understand the rate of formation of different groups of sunspots during a solar cycle and the possible consequences for the long-term geomagnetic activity.

  20. RANDOM BALLISTIC INTERPRETATION OF NONLINEAR GUIDING CENTER THEORY

    SciTech Connect (OSTI)

    Ruffolo, D.; Pianpanit, T.; Matthaeus, W. H.; Chuychai, P. E-mail: th_ee@hotmail.com E-mail: p.chuychai@sci.mfu.ac.th

    2012-03-10

    Nonlinear guiding center (NLGC) theory has been used to explain the asymptotic perpendicular diffusion coefficient {kappa} of energetic charged particles in a turbulent magnetic field, which can be applied to better understand cosmic ray transport. Here we re-derive NLGC, replacing the assumption of diffusive decorrelation with random ballistic decorrelation (RBD), which yields an explicit formula for {kappa} . We note that scattering processes can cause a reversal of the guiding center motion along the field line, i.e., 'backtracking', leading to partial cancellation of contributions to {kappa}, especially for low-wavenumber components of the magnetic turbulence. We therefore include a heuristic backtracking correction (BC) that can be used in combination with RBD. In comparison with computer simulation results for various cases, NLGC with RBD and BC provides a substantially improved characterization of the perpendicular diffusion coefficient for a fluctuation amplitude less than or equal to the large-scale magnetic field.