How Do THey DeciDe wHaT To Do aT THe iNL? nuclear energy Nuclear energy is a clean, safe, vital part of this country's energy mix. S takeholders frequently tell us they're impressed by all the nuclear research we do at the idaho National Laboratory, but they wonder why we don't do more work on renewable energy, like wind, solar and hydro. well, the answer is, we do research in those areas, but our history and our expertise is in nuclear energy research. we don't apologize for that: nuclear
Site Environmental Report Paducah Site 2011 PAD-REG-1012 BACK TABLE OF CONTENTS FORWARD Fractions and Multiples of Units Multiple Decimal Equivalent Prefix Symbol Engineering Format 10 6 1,000,000 mega- M E+06 10 3 1,000 kilo- k E+03 10 2 100 hecto- h E+02 10 10 deka- da E+01 10 -1 0.1 deci- d E-01 10 -2 0.01 centi- c E-02 10 -3 0.001 milli- m E-03 10 -6 0.000001 micro- Î¼ E-06 10 -9 0.000000001 nano- n E-09 10 -12 0.000000000001 pico- P E-12 10 -15 0.000000000000001 femto- F E-15 10 -18
... Special thanks to Sprint, ReliOn, Altergy, Cummins Generator, Deka Batteries, AC Systems, ... K.; Judd, K.; Stone, H.; Zewatsky, J.; Thomas, A.; Mahy, H.; Paul, D. Identification ...
Conference: Machine Learning for the Grid Citation Details In-Document Search Title: Machine Learning for the Grid Authors: Deka, Deepjyoti  ; Backhaus, Scott N.  ; Chertkov, Michael  ; Lokhov, Andrey  ; Misra, Sidhant  ; Vuffray, Marc Denis  ; Dvijotham, Krishnamurthy  + Show Author Affiliations Los Alamos National Laboratory Publication Date: 2016-02-02 OSTI Identifier: 1237248 Report Number(s): LA-UR-16-20576 DOE Contract Number: AC52-06NA25396 Resource Type: Conference
SciTech Connect Technical Report: Structure Learning in Power Distribution Networks Citation Details In-Document Search Title: Structure Learning in Power Distribution Networks Authors: Deka, Deepjyoti  ; Chertkov, Michael  ; Backhaus, Scott N.  + Show Author Affiliations Electrical & Computer Engineering, University of Texas at Austin Los Alamos National Laboratory Publication Date: 2015-01-13 OSTI Identifier: 1167238 Report Number(s): LA-UR-15-20213 DOE Contract Number:
... used and the resulting fa- cility ranking. ... Docu- mentation and Critical Deci- sions DOE O 413.3B, Program and Project ... 460.2A Departmental Materials Transportation and ...
as Facility Support Services Contract Award Fee Plan Contract Number DE-CI0000004 3 editorial or personnel changes may be made and implemented without being provided to the...
6 Determination Scorecard Contractor: Wastren-EnergX Mission Support, LLC Contract: DE-CI0000004 Award Fee Evaluation Period: Fiscal Year 2015 (October 1, 2014 to September 30, ...
Kibanova, Daria; Cervini-Silva, Javiera; Destaillats, Hugo
Clay-supported TiO2 photocatalysts can potentially improve the performance of air treatment technologies via enhanced adsorption and reactivity of target volatile organic compounds (VOCs). In this study, a bench-top photocatalytic flow reactor was used to evaluate the efficiency of hectorite-TiO2 and kaolinite-TiO2, two novel composite materials synthesized in our laboratory. Toluene, a model hydrophobic VOC and a common indoor air pollutant, was introduced in the air stream at realistic concentrations, and reacted under UVA (gamma max = 365 nm) or UVC (gamma max = 254 nm) irradiation. The UVC lamp generated secondary emission at 185 nm, leading to the formation of ozone and other short-lived reactive species. Performance of clay-TiO2 composites was compared with that of pure TiO2 (Degussa P25), and with UV irradiation in the absence of photocatalyst under identical conditions. Films of clay-TiO2 composites and of P25 were prepared by a dip-coating method on the surface of Raschig rings, which were placed inside the flow reactor. An upstream toluene concentration of ~;;170 ppbv was generated by diluting a constant flow of toluene vapor from a diffusion source with dry air, or with humid air at 10, 33 and 66percent relative humidity (RH). Toluene concentrations were determined by collecting Tenax-TA (R) sorbent tubes downstream of the reactor, with subsequent thermal desorption -- GC/MS analysis. The fraction of toluene removed, percentR, and the reaction rate, Tr, were calculated for each experimental condition from the concentration changes measured with and without UV irradiation. Use of UVC light (UV/TiO2/O3) led to overall higher reactivity, which can be partially attributed to the contribution of gas phase reactions by short-lived radical species. When the reaction rate was normalized to the light irradiance, Tr/I gamma, the UV/TiO2 reaction under UVA irradiation was more efficient for samples with a higher content of TiO2 (P25 and Hecto-TiO2), but not for Kao-TiO2. In all cases, reaction rates peaked at 10percent RH, with Tr values between 10 and 50percent higher than those measured under dry air. However, a net inhibition was observed as RH increased to 33percent and 66percent, indicating that water molecules competed effectively with toluene for reactive surface sites and limited the overall photocatalytic conversion. Compared to P25, inhibition by co-adsorbed water was less significant for Kao-TiO2 samples, but was more dramatic for Hecto-TiO2 due to the high water uptake capacity of hectorite.
Conceptual Design Review Module March 2010 CD-0 O 0 OFFICE OF C CD-1 F ENVIRO Standard R Concep Rev Critical Decis CD-2 M ONMENTAL Review Plan ptual De view Module sion (CD) Ap CD March 2010 L MANAGE n (SRP) sign e pplicability D-3 EMENT CD-4 Post Ope eration Standard Review Plan, 2 nd Edition, March 2010 i FOREWORD The Standard Review Plan (SRP) 1 provides a consistent, predictable corporate review framework to ensure that issues and risks that could challenge the success of Office of
The ability to collect key system level information is critical to the safe, efficient and reli- able operation of advanced energy systems. With recent advances in sensor development, it is now possible to push some level of decision making directly to computationally sophisticated sensors, rather than wait for data to arrive to a massive centralized location before a decision is made. This type of approach relies on networked sensors (called “agents” from here on) to actively collect and process data, and provide key control deci- sions to significantly improve both the quality/relevance of the collected data and the as- sociating decision making. The technological bottlenecks for such sensor networks stem from a lack of mathematics and algorithms to manage the systems, rather than difficulties associated with building and deploying them. Indeed, traditional sensor coordination strategies do not provide adequate solutions for this problem. Passive data collection methods (e.g., large sensor webs) can scale to large systems, but are generally not suited to highly dynamic environments, such as ad- vanced energy systems, where crucial decisions may need to be reached quickly and lo- cally. Approaches based on local decisions on the other hand cannot guarantee that each agent performing its task (maximize an agent objective) will lead to good network wide solution (maximize a network objective) without invoking cumbersome coordination rou- tines. There is currently a lack of algorithms that will enable self-organization and blend the efficiency of local decision making with the system level guarantees of global decision making, particularly when the systems operate in dynamic and stochastic environments. In this work we addressed this critical gap and provided a comprehensive solution to the problem of sensor coordination to ensure the safe, reliable, and robust operation of advanced energy systems. The differentiating aspect of the proposed work is in shift- ing the focus towards “what to observe” rather than “how to observe” in large sensor networks, allowing the agents to actively determine both the structure of the network and the relevance of the information they are seeking to collect. In addition to providing an implicit coordination mechanism, this approach allows the system to be reconfigured in response to changing needs (e.g., sudden external events requiring new responses) or changing sensor network characteristics (e.g., sudden changes to plant condition). Outcome Summary: All milestones associated with this project have been completed. In particular, private sensor objective functions were developed which are aligned with the global objective function, sensor effectiveness has been improved by using “sensor teams,” system efficiency has been improved by 30% using difference evaluation func- tions, we have demonstrated system reconfigurability for 20% changes in system con- ditions, we have demonstrated extreme scalability of our proposed algorithm, we have demonstrated that sensor networks can overcome disruptions of up to 20% in network conditions, and have demonstrated system reconfigurability to 20% changes in system conditions in hardware-based simulations. This final report summarizes how each of these milestones was achieved, and gives insight into future research possibilities past the work which has been completed. The following publications support these milestones [6, 8, 9, 10, 16, 18, 19].