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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Thermal Stratification Analysis for Sodium Fast Reactors

The sodium fast reactor (SFR) is the most mature reactor concept of all the generation-IV nuclear systems and is a promising reactor design that is currently under development by several organizations. The majority of sodium fast reactor designs utilize a pool type arrangement which incorporates the primary coolant pumps and intermediate heat exchangers within the sodium pool. These components typically protrude into the pool thus reducing the risk and severity of a loss of coolant accidents. To further ensure safe operation under even the most severe transients a more comprehensive understanding of key thermal hydraulic phenomena in this pool is desired. One of the key technology gaps identified for SFR safety is determining the extent and the effects of thermal stratification developing in the pool during postulated accident scenarios such as a protected or unprotected loss of flow incident. In an effort to address these issues, detailed flow models of transient stratification in the pool during an accident can be developed. However, to develop the calculation models, and ensure they can reproduce the underlying physics, highly spatially resolved data is needed. This data can be used in conjunction with advanced computational fluid dynamic calculations to aid in the development of simple reduced dimensional models for systems codes such as SAM and SAS4A/SASSYS-1.

Schneider, James↗
DOE OSTI2018

Thermal Stratification Analysis for Sodium Fast Reactors

The sodium fast reactor (SFR) is the most mature reactor concept of all the generation-IV nuclear systems and is a promising reactor design that is currently under development by several organizations. The majority of sodium fast reactor designs utilize a pool type arrangement which incorporates the primary coolant pumps and intermediate heat exchangers within the sodium pool. These components typically protrude into the pool thus reducing the risk and severity of a loss of coolant accidents. To further ensure safe operation under even the most severe transients a more comprehensive understanding of key thermal hydraulic phenomena in this pool is desired. One of the key technology gaps identified for SFR safety is determining the extent and the effects of thermal stratification developing in the pool during postulated accident scenarios such as a protected or unprotected loss of flow incident. In an effort to address these issues, detailed flow models of transient stratification in the pool during an accident can be developed. However, to develop the calculation models, and ensure they can reproduce the underlying physics, highly spatially resolved data is needed. This data can be used in conjunction with advanced computational fluid dynamic calculations to aid in the development of simple reduced dimensional models for systems codes such as SAM and SAS4A/SASSYS-1.

Schneider, James↗
DOE OSTI2018

What is Machine Learning?

Have you ever been unsure what people mean when they talk about 'machine learning'? What machines? And what are they learning? This video is for you! For more information about Machine Learning at LLNL and other Data Science projects, visit the LLNL Data Science Institute website at data-science.llnl.gov

3-D PRINTING↗
DOE OSTI2018

Data Mining Critical Infrastructure Systems: Models and Tools

Critical infrastructures (CIs) such as power, water, transportation, and telecommunication are highly complex interacting systems that are vital to national security, economy and public life. They play an important role in several core urban computing challenges. Advances in computing resources and techniques have led to enormous progress in developing intelligent frameworks for analyzing these large heterogeneous systems. In this article, we survey state-of-the-art and foundational work in this upcoming area from a data mining perspective. We discuss basic concepts of CIs, their properties, impacts on them due to natural or human-caused disturbances and different computational methodologies used for modeling and understanding their behavior. We also discuss recent work that specifically deals with two critical sectors of CIs, namely power and transportation systems. Lastly, we also describe several existing tools and methods that are used to facilitate decision making for domain operators, enable efficient and faster disaster response for federal and state agencies and help improve the security and resiliency of these CIs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Measuring pristine air to reveal pollution's impact

Data collected in flights above the Amazon rainforest shows a greater human impact on the atmosphere than previously thought. Researchers spent hours packed aboard a steamy Gulfstream-1 research aircraft as it zig-zagged between pristine air over the Amazon rainforest and polluted air nearby. It was like a trip through time, as scientists weaved between the two vastly different settings, snagging air samples characteristic of today’s industrial environment as well as samples of unpolluted air, like that before the industrial age.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

MACHINE LEARNING BASED CHEMICAL EXPLOSIVE MODE ANALYSIS (ML-CEMA)

The software consists of a Machine Learning based Chemical Explosive Mode Analysis (ML-CEMA) tool for advanced computational flame diagnostics. CEMA, originally based on eigen-analysis of the local thermochemical system, is capable of identifying reaction fronts and limit phenomena such as auto-ignition and extinction in practical combustion systems, such as internal combustion engines and gas turbine combustors. However, the original CEMA is computationally expensive for large reaction mechanisms that are typically needed to describe fuel chemistry of practical large-hydrocarbon fuels. This novel ML-CEMA tool employs a ML technique to accelerate the eigen-analysis of the basic CEMA approach by orders of magnitude, thus making it suitable for practical fuels. In ML-CEMA,zero-dimensional (0D) reactors and one-dimensional (1D) premixed flames are first used to generate a large number of data points for neural network based ML training. The trained ML model is then used to perform CEMA prediction. This ML-CEMA tool has been demonstrated in canonical 0D and 1D configurations as well as highly-transient three-dimensional spray flames exhibiting multi-mode turbulent combustion, showing promising results. ML-CEMA, as a standalone tool, can be used for computationally-efficient diagnostics of massive datasets generated from both experiments and simulations. For example, based on spatially resolved measurements of a small set of reactive scalars(such as temperature, hydroxyl radical and formaldehyde), ML-CEMA can effectively identify flame fronts and rare events. ML-CEMA also provides a robust online or offline flame feature detection tool. When used for on-the-fly simulations, ML-CEMA further enables zone-adaptive combustion modeling, in which the predicted eigenvalue is used as a robust mode indicator for judicious assignment of locally-valid combustion models. This ML-CEMA based zone-adaptive model can lead to substantial computational cost savings when used for large-scale simulations of multi-mode combustion systems. Third Party Code Web Page to Download Code Web Page Location of Third Party License

Xu, Chao↗
DOE OSTI2021

Economic and Environmental Assessment of Biological Conversions of Agile BioFoundry (ABF) Bio-Derived Chemicals

Bio-derived chemicals are an essential part of the growing bioeconomy. They possess the potential to boost a new domestic bioproduct industry, improve the sustainability of integrated biomanufacturing, and reduce U.S. dependence on fossil energy. The Agile BioFoundry (ABF) consortium, a Department of Energy (DOE)-sponsored collaboration, is investigating biobased pathways to produce advantaged products including advanced biofuels, fuel intermediates, and bioproducts. ABF is integrating advanced computational tools for biological engineering, process design and data analysis, and economic/sustainability modeling tools into a comprehensive and dynamic platform for biomanufacturing of microbes and using them to produce key metabolic intermediates or beachhead molecules which may be derivatized into several distinct bioproducts of industrial interest. In this presentation, we first discuss a methodology to select a single exemplar product molecule to represent each beachhead pathway based on similarities with other end-molecule options (yields/titers/rates, fermentation operation mode, oxygen requirements, general separations challenges, etc.), in order to maintain a reasonable number of cases for rigorous process simulation. We then use techno-economic analysis (TEA) and life-cycle analysis (LCA) to highlight sensitivities and trends around economic performance and environmental footprint, reflecting examples for two selected ABF technology pathways to bio-derived chemicals: 1) adipic acid production via muconic acid fermentation from mixed sugars with Pseudomonas putida and 2) cineole via geranyl diphosphate with Rhodosporidium toruloides. We present multi-variable scan plots highlighting key drivers on costs and greenhouse gas emissions in order to identify the parameter space in which each pathway could ultimately achieve economic and environmental sustainability meeting or exceeding commodity product benchmarks, thus prioritizing future R&D focus areas.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Demonstration of Bison/TRACE Coupling (CRAB) Through Validation Case LOFT L2-5

The postulated pressurized water reactor (PWR) accident known as a large-break loss of coolant accident (LOCA) is still of great concern to the current nuclear reactor fleet. Research through experiments, modeling, and simulation is active in the field of LOCAs and new modeling tools are under continuous development. The BISON fuel performance code, developed at the Idaho National Laboratory (INL), is one such tool that has been very active in the development of models and the simulation of accidents. With recent model developments, Bison has been used to simulate LOCA cladding burst experiments and full integral LOCA experiments with favorable results. Bison is a thermomechanical code that must be fed boundary conditions (BC) for phenomena occurring outside of the fuel rod. These BCs include moderator or outer cladding temperature, coolant pressure, and rod linear power or neutron flux. These BCs can be supplied from a function or, preferably, another coupled code. Bison is built on the Multiphysics Object Oriented Simulation Environment (MOOSE) framework which allows for ease of code coupling. A framework is currently in place that allows codes external to MOOSE to communicate with MOOSE applications as if they were a MOOSE application. Through a recent MOOSE/BISON development, with collaboration from the U.S. Nuclear Regulatory Commission (NRC), BISON has been coupled with the NRC system code TRAC/RELAP Advanced Computational Engine (TRACE). Simulation results of the initial validation loss of flow test (LOFT) L2-5 case from this research will be presented.

42 ENGINEERING↗
DOE OSTI2022

UPDATES FROM THE INVOLUTE WORKING GROUP

The HFIR, RHF, and FRM II reactors represent a particular class of Research and Test Reactors that provide some of the most intense and continuous neutron fluxes for science, industry, and medical applications. These high-performance reactors have achieved compact cores by operating with Highly Enriched Uranium fuel (HEU, 235U/U ≥ 20 wt. %) and utilizing fuel plates curved as an involute. Due to the proliferation risks, the international community aims to reduce or eliminate, when possible, the use of HEU fuel in civilian facilities by converting them to a Low-Enriched Uranium fuel (LEU, 235U/U < 20 wt. %). Conversion of these reactors without significantly compromising their performance or safety is a challenging endeavor that can tremendously benefit from advanced computational tools and thus, eliminate unnecessary conservatism to ensure sufficient thermal margins. Therefore, models are being developed using modern Computational Fluid Dynamics (CFD) and Computational Structural Mechanics (CSM) software to evaluate the steady-state safety margins of various LEU designs instead of being reliant on the more traditional, conservative methods. To gain the confidence and acceptance of high-fidelity modeling by the nuclear regulators, Argonne National Laboratory (ANL) and the involute reactors have formed an informal scientific group, the Involute Working Group (IWG). The IWG facilitates inter-organizational collaboration on experimental benchmarking, code-to-code comparisons, and Verification and Validation (V&V). This paper describes some of the recent IWG efforts in validating software against the existing experimental data, as well as code-to-code comparisons of different software used by the IWG members.

Bergeron, Aurelien↗
DOE OSTI2022

NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning

Integrating cloud services into advanced computing facilities provides significant new capabilities over focusing solely on traditional high performance computing (HPC) workloads. This brings complementary capabilities as well as enabling new focused roles for HPC. They are especially potent for workflows that fuse data streams, modeling and simulation ('modsim') and machine learning. A key challenge to adopting a hybrid edge-cloud-HPC model is to align optimal capability, data, and user intent on the right resources for each step in a workflow.?The NREL Stratus service provides a basis for this: Stratus layers capabilities needed to make?cloud services accessible to a lab-based scientific community on commercial offerings, and; currently supports upwards of 200 projects ranging from IOT integration to traditional modeling and simulation. This provides a real-world inventory of scientific workflow elements. A growing knowledge base enables placing these elements appropriately between the edge, cloud, and traditional HPC. This paper outlines a vision via reference architecture and the application of that architecture in a typical workflow highlighting multiple components: sensor data intake, cleaning and transforming (edge/cloud suitable); generation of synthetic data through modsim, computationally heavy ML training and hyperparameter optimization (HPC suitable), and; inference and deployment (cloud ideal). Every step in such a workflow involves a cost-benefit analysis regarding the data movement, computational efficiency, availability, latency, and resource capabilities. The reference architecture and examples outlined allow for understanding new opportunities in the context of emerging workflows that combine IOT, cloud, and HPC to bolster scientific productivity.

AI↗
DOE OSTI2022

28 NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning: Preprint

Integrating cloud services into advanced computing facilities provides significant new capabilities over focusing solely on traditional high performance computing (HPC) workloads. This brings complementary capabilities as well as enabling new focused roles for HPC. They are especially potent for workflows that fuse data streams, modeling and simulation ('modsim') and machine learning. A key challenge to adopting a hybrid edge-cloud-HPC model is to align optimal capability, data, and user intent on the right resources for each step in a workflow.?The NREL Stratus service provides a basis for this: Stratus layers capabilities needed to make?cloud services accessible to a lab-based scientific community on commercial offerings, and; currently supports upwards of 200 projects ranging from IOT integration to traditional modeling and simulation. This provides a real-world inventory of scientific workflow elements. A growing knowledge base enables placing these elements appropriately between the edge, cloud, and traditional HPC. This paper outlines a vision via reference architecture and the application of that architecture in a typical workflow highlighting multiple components: sensor data intake, cleaning and transforming (edge/cloud suitable); generation of synthetic data through modsim, computationally heavy ML training and hyperparameter optimization (HPC suitable), and; inference and deployment (cloud ideal). Every step in such a workflow involves a cost-benefit analysis regarding the data movement, computational efficiency, availability, latency, and resource capabilities. The reference architecture and examples outlined allow for understanding new opportunities in the context of emerging workflows that combine IOT, cloud, and HPC to bolster scientific productivity.

AI↗
DOE OSTI2015

Next-generation system analysis model for studying alternative Waste Management Systems - 15232

The U.S. Department of Energy's Office of Nuclear Energy (DOE-NE) is conducting planning activities within the Nuclear Fuels Storage and Transportation Planning Project (NFST). The plans involve (1) laying the groundwork for implementing interim storage (including dealing with any associated transportation that is needed) per the Administration's Strategy for the Management and Disposal of Used Nuclear Fuel and High-Level Radioactive Waste of January 2013 and (2) developing a foundation for a new nuclear waste management organization. An overarching goal is to develop options for designing an integrated system that will be considered by decision-makers. Based on the dynamic system analysis and software specification requirements that must be met, Argonne National Laboratory (Argonne) is leading the development of a consolidated waste management system tool known as the Next-Generation System Analysis Model (NGSAM). The development of NGSAM leverages the approaches and methods used previously to develop the Transportation Storage Logistics (TSL) and Total System Model (TSM) system analysis tools, but the new tool takes advantage of advanced computational capabilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Effective Long-term Monitoring Strategies by Integrating Reactive Transport Models with In situ Geochemical Measurements - 16212

Long-term monitoring of contaminant transport in groundwater is expected to account for a large fraction of future life-cycle cleanup costs at the DOE sites. The Attenuation-Based Remedies in the Subsurface Applied Field Research Initiative (ABRS AFRI) is developing an innovative approach for cost-effective in situ long-term monitoring. The approach is based on strategically adding the measurements of controlling variables (such as pH, redox potential, electrical conductivity, and groundwater level), which control the plume mobility and its spatial and temporal distribution. In situ measurements of these variables - supplemented with a reduced number of standard periodic groundwater samples - are expected to lead to more cost-effective monitoring, and can also serve as an early warning system for detecting unexpected plume migration. The objective of this study is to support the development of such a long-term monitoring approach through the application of advanced computational methods, including (1) statistical data mining and analysis, and (2) three-dimensional flow and reactive chemical transport modeling. We have performed the statistical data analysis of historical and current monitoring data at the Savannah River Site (SRS) F-Area to quantify the correlations between the controlling variables and radioactive contaminant concentrations. In parallel, we have used 3D modeling of flow and contaminant transport to provide the prediction of the contaminant plume evolution. The results of both data analysis and modeling confirmed that the correlations between controlling variables and contaminant concentrations are significant. Modeling results also suggest that the correlation parameters will change in the future, which is important to assess the long-term efficacy of the proposed monitoring approach. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Assessment of boric acid accumulation in a reactor core blocked by debris

To examine the potential for boron precipitation in the core during sump recirculation when latent debris has formed a blockage at the core inlet, TRACE simulations of long term cooling following a loss-of-coolant accident (LOCA) in a Westinghouse four loop pressurized water reactor (PWR) were performed. The TRAC/RELAP Advanced Computational Engine (TRACE) was modified to account for the higher fluid densities and viscosities that occur when the coolant contains boric acid. Previous versions of TRACE calculated the transport of boron in the RCS, but assumed coolant properties to be the same as pure water. This was appropriate for very low concentrations of boric acid, but not when the concentrations approach those where precipitation is possible. These modifications allow TRACE to simulate natural circulation caused by a concentration difference in addition to the buoyancy induced by temperature differences. The results of the TRACE calculations suggest that there is margin to boric acid precipitation during long term cooling following a large break LOCA even if debris significantly affects flow at the core inlet. Significant blockages at either the lower core plate or bottom fuel nozzles did not result in boric acid concentrations likely to cause precipitation in the core. The calculations do show that precipitation can occur in the upper head where coolant can become trapped and in the steam generators where entrained fluid can evaporate and deposit the boron carried by the droplets. Of particular importance, is that the boric acid concentration and coolant temperature in the lower plenum and regions near debris blockage remained well below precipitation levels supporting a conclusion that precipitation is not likely to make the blockage more severe. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Validation Study in SAS4A Code in Simulated Mild TOP Condition

Model improvements and validation efforts for SAS4A code, which is the most advanced computer code for simulating the the primary phase of the Core Disruptive Accident (CDA) of MOX-fueled Sodium-cooled Fast Reactors, have been performed utilizing the experimental data from the CABRI programs by JAEA (Japan Atomic Energy Agency), IRSN (Institut de Radioprotection et de Surete Nucleaire), CEA (Commissariat a l'Energie Atomique et aux Energies Alternatives) and KIT (Karlsruhe Institute of Technology). In order to confirm validity of improved models of SAS4A, a systematic and comprehensive validation plan was defined. It was based on the PIRT (Phenomena Identification and Ranking Table) result for CDA of MOX-fueled fast reactors. The fuel failure position and time are the most important parameters, which dominate the analytical results in a reactor calculation. The failure position will affect the reactivity feedback due to the fuel distribution after failure and consequently would have an effect on the energy release in the initiating phase. This paper describes the preliminary result of validation study for SAS4A code in simulated Transient Over-Power (TOP) condition. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

NUCLEI (SciDAC-4) effort at The University of Tennessee, Knoxville

This Report summarizes the accomplishments of NUCLEI researchers at The University of Tennessee, Knoxville (UTK) and those supported via subcontracts at the University of North Carolina at Chapel Hill (UNC) and Iowa State University (ISU). The SciDAC-4 NUCLEI collaboration had the goal to advance the computation of atomic nuclei and to make reliable predictions including uncertainty estimates for observables of interest. These goals were achieved by developing improved nuclear interactions and currents that are input to the computations, and by advancing the algorithms and techniques used for computing nuclear observables of interest. The computed results advanced our understanding of how nuclei work; they helped to guide and interpret experiments at existing low-energy nuclear physics facilities, the Facility for Rare Isotope Beams, Jefferson Laboratory, and neutrino experiments. In addition, predictions were made that await confrontation with experimental results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Advanced Simulation and Computing (ASC) Software Quality Plan: ASC Software Quality Engineering Practices (V.4.0)

The purpose of the Sandia National Laboratories (SNL) Advanced Simulation and Computing (ASC) Software Quality Plan is to clearly identify the software quality engineering practices that are the basis for continually improving the quality of ASC software products. This plan defines the SNL ASC Program software quality engineering practices and provides a mapping of these practices to Laboratory Policy System IT008: Provide Quality Software Policy. This plan also identifies ASC Program Management and the software project teams' responsibilities in implementing quality software engineering practices and in assessing progress towards achieving their software quality goals.

97 MATHEMATICS AND COMPUTING↗