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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

NEAMS Advanced Reactor Model Contributions to the NRIC Virtual Test Bed

The U.S. Department of Energy (DOE) Office of Nuclear Energy’s Advanced Modeling and Simulation (NEAMS) Program develops models of advanced reactor phenomena to demonstrate code applicability to challenging physics problems, drive code development through user assessment, and perform code verification and validation. Meanwhile, the U.S. DOE’s National Reactor Innovation Center (NRIC) hosts an open-source website and associated GitHub repository called the Virtual Test Bed (VTB) on which computational models for advanced reactors are documented and shared with the reactor community. Several NEAMS advanced reactor models (including input files, documentation, and discussion of results) have recently been contributed to the NRIC VTB. The open sharing of these models benefits the reactor community by providing “best practice” examples using NEAMS tools for advanced reactor physics problems. This report summarizes and provides links to these new models, which include modeling phenomena important to liquid metal cooled fast reactors, molten salt reactors, high temperature gas-cooled reactors, and microreactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

NEAMS Model Contributions in 2023 to the National Reactor Innovation Center Virtual Test Bed for Use by Industry and Other Stakeholders

The U.S. Department of Energy (DOE) Office of Nuclear Energy’s Advanced Modeling and Simulation (NEAMS) Program develops models of advanced reactor phenomena to demonstrate code applicability to challenging physics problems, drive code development through user assessment, and perform code verification and validation. Meanwhile, the U.S. DOE’s National Reactor Innovation Center (NRIC) hosts an open-source website and associated GitHub repository called the Virtual Test Bed (VTB) on which computational models for advanced reactors are documented and shared with the reactor community. This work documents NEAMS efforts to support industry adoption of advanced modeling tools through contribution of 10 NEAMS models to the NRIC Virtual Test Bed including models for the High Temperature Test Facility (HTTF), TRISO fuel failure in a microreactor, and multiphysics models of a molten chloride fast reactor, among others. The open sharing of these models benefits the reactor community by providing “best practice” examples using NEAMS tools for advanced reactor physics problems. In particular, the HTTF model is being used for code validation and benchmarking activities. The microreactor and molten chloride fast reactor models are representative of analysis that may be useful for current candidates of DOME and LOTUS, NRIC’s physical testbeds. This report summarizes and provides links to these new models, among others.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

ML4Geo: Machine Learning based Well Design to Enhance Unconventional Energy Production

This project aimed at using artificial intelligence and machine learning methods to improve oil and gas operations. On the machine learning front, this focused on the development of differentiable programming methods that are relevant in this application area, such as incorporating physical simulations into machine learning workflows. On the oil and gas front, the focus was on optimizing well performance.

02 PETROLEUM↗
DOE OSTI2024

Towards a NEAMS-based high-fidelity model of the MARVEL reactor

This report outlines the progress of Idaho National Laboratory in developing a high-fidelity and high-resolution model of the Microreactor Applications Research Validation and Evaluation reactor. The model was developed under the Nuclear Energy Advanced Modeling and Simulation microreactor application driver at Idaho National Laboratory. The overarching objective of this activity is the development of a high-fidelity multiphysics MARVEL model using NEAMS tools, and to verify and validate NEAMS tools against MARVEL reference simulation and experimental data, respectively. This is a unique opportunity to conduct multiphysics analysis on a soon-to-be-deployed microreactor. This multiphysics model developed under the NEAMS-funded INL microreactor application driver leverages three single-physics models coupled via the MOOSE’s MultiApp and Transfer systems. The latter systems enable in-memory data transfer between MOOSE-based and MOOSE-wrapped applications. The first single-physics model, that functions as main application, leverages Griffin to model the neutron transport in the core through the discontinuous finite element (DFEM) discrete ordinates solver (SN). Several optimization flags that were developed by the Griffin developer team were beta-tested to enhance the solver’s performance. These include the combined use of using_average_xs and update_averaged_xs_on that enable to avoid expensive on-the-fly cross sections evaluations at each linear iterations in favor of evaluations of the macroscopic cross sections at each Picard iteration. The second single-physics model uses BISON to handle solid heat transfer and asymptotic hydrogen redistribution analysis in the fuel. While the model returns consistent results for the temperature and hydrogen distribution in the fuel, a mismatch was noticed in the calculated temperature in the reflector due to the value of the gap conductance used in our model. Ongoing investigations are being performed to assess the origin of this discrepancy. Finally, the System Analysis Module (SAM) was used to model the flow of the sodium-potassium eutectic in the primary loop. A first verification was also performed showing good agreement in terms of mass flow rate and inlet temperature. All mesh files were generated using the MOOSE Reactor module, removing the need for external meshing tools. Notably, this workscope represents one of the initial applications of the MOOSE Reactor module for modeling highly irregular geometries. The use of the reactor module significantly streamlined the mesh generation process. The full multiphysics mode, that combines all the single physics models, was leveraged to conduct initial steady-state multiphysics simulations to compute power, and temperature distribution in the reactor. Initial testing was performed for transient simulations as well. In this case, the new checkpoint restart capability for eigenvalue calculations was tested showing the capability for streamlined restart of transient calculations. Future work will focus on improving the fidelity of the model by performing comprehensive code-to-code comparisons. For instance, the full-core Griffin neutronics model will be benchmarked against MCNP reference results, that were provided by the MARVEL design team. Additionally, the SAM T/H model will be verified against reference RELAP-5 results for selected accident scenarios. Besides code-to-code verification exercises, the model fidelity will be improved by replacing the single-channel SAM model with a more complex SAM-Pronghorn coupled model, in which the sub-channel capability is deployed to obtain radial temperature resolution in the coolant. This model will be developed in synergy with the NEAMS thermal hydraulics team.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Extending SST vanadis to Add SIMT Functional Units

Sandia National Laboratories is currently investigating scalable architectural simulation capabilities, with a focus on simulating and evaluating highly scalable supercomputers for high-performance computing applications. This exploration is driven by the shift toward more specialized forms of compute and the need for a more diverse set of accurate models. This project will explore the use of General-Purpose Graphical Processing Units (GPGPUs) in high-performance computing using both physical systems and new simulator models – traditional GPUs as well as tightly-coupled SIMT accelerators.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Nuclear cross sections for estimating secondary radiations produced in spacecraft

The physical theory underlying calculations of nuclear absorption and fragmentation cross sections and EM dissociation cross sections is reviewed; typical results are presented in graphs; and the adequacy of the experimental data bases for each type of cross section are discussed. The emphasis is on computations applicable to the estimation of secondary radiation in shielded spacecraft.

Townsend, L. W.↗
NASA NTRS1993

Damage detection using experimentally measured mass and stiffness matrices

A method is presented for locating physical damage or change in a structure using experimentally measured mass and stiffness matrices. The approach uses a recently developed algorithm for transforming a state-space realization into a second order structural model with physical displacements as the generalized coordinates. This is accomplished by first rotating a state-space model of the identified structural dynamics into modal coordinates and approximating the mass normalized modal vectors for the output measurement set. Next, the physical mass, damping and stiffness matrices are synthesized directly from the measured modal parameters. This yields experimental mass and stiffness matrices for the structure without the use of a finite element model or a numerical search. The computed mass and stiffness are asymptotically equivalent to a static condensation of the global physical coordinate model. Techniques for solving the inverse connectivity problem are then developed whereby it is possible to assess the stiffness in a region of the structure bounded by several sensors. Applications to both simulated data and experimental data are used to discuss the effectiveness of the approach.

Peterson, L. D.↗
NASA NTRS1994

Low-Flow-Rate Dry-Powder Feeder

Apparatus feeds small, precise flow of dry powder through laser beam of optical analyzer, measuring patterns of light created by forward scattering (Fraunhofer diffraction) of laser beam from powder particles. From measurement, statistical distribution of sizes of powder particles computed. Developed for analyzing particle-size distributions of solid-propellant powders. Also adapted to use in pharmaceutical industry, in manufacture of metal powder, and in other applications in which particle-size distributions of materials used to control rates of chemical reactions and/or physical characteristics of processes.

Ramsey, Keith E.↗
NASA NTRS1998

Computational Nanoelectronics and Nanotechnology at NASA ARC

Both physical and economic considerations indicate that the scaling era of CMOS will run out of steam around the year 2010. However, physical laws also indicate that it is possible to compute at a rate of a billion times present speeds with the expenditure of only one Watt of electrical power. NASA has long-term needs where ultra-small semiconductor devices are needed for critical applications: high performance, low power, compact computers for intelligent autonomous vehicles and Petaflop computing technology are some key examples. To advance the design, development, and production of future generation micro- and nano-devices, IT Modeling and Simulation Group has been started at NASA Ames with a goal to develop an integrated simulation environment that addresses problems related to nanoelectronics and molecular nanotechnology. Overview of nanoelectronics and nanotechnology research activities being carried out at Ames Research Center will be presented. We will also present the vision and the research objectives of the IT Modeling and Simulation Group including the applications of nanoelectronic based devices relevant to NASA missions.

Saini, Subhash↗
NASA NTRS1998

Computational Nanoelectronics and Nanotechnology at NASA ARC

Both physical and economic considerations indicate that the scaling era of CMOS will run out of steam around the year 2010. However, physical laws also indicate that it is possible to compute at a rate of a billion times present speeds with the expenditure of only one Watt of electrical power. NASA has long-term needs where ultra-small semiconductor devices are needed for critical applications: high performance, low power, compact computers for intelligent autonomous vehicles and Petaflop computing technolpgy are some key examples. To advance the design, development, and production of future generation micro- and nano-devices, IT Modeling and Simulation Group has been started at NASA Ames with a goal to develop an integrated simulation environment that addresses problems related to nanoelectronics and molecular nanotecnology. Overview of nanoelectronics and nanotechnology research activities being carried out at Ames Research Center will be presented. We will also present the vision and the research objectives of the IT Modeling and Simulation Group including the applications of nanoelectronic based devices relevant to NASA missions.

Saini, Subhash↗
NASA NTRS2007

Satellite-Based Model Parameterization of Diabatic Heating

Future meteorological satellites are expected to provide much needed fine-scale information that can improve the accuracy of weather and climate models. As one application of this improved capability, we introduce the concept of a generalized parameterization framework using satellite datasets that will increase the accuracy and the computational efficiency of weather and climate modeling. In an atmospheric model, several different parameterizations usually are used to reproduce the various physical processes. However, it is generally unrealistic to separate the processes in this artificial way since the observations and physics make no such artificial separation. Thus, we propose a new unified parameterization framework to remove the unrealistic separation between parameterizations.

Pielke, Roger, Sr.↗
DOE OSTI2021

Modeling of a 1/20th Scaled-down Gas Reactor Using MOOSE-based Application Pronghorn

University of Idaho (UI) designed and built a 1/20th scaled-down HTGR system to examine the mixing and venting of helium and reactor cavity air after a loss-of-coolant, also known as LOCA, due to a break in the reactor pressure vessel. Computational fluid dynamics (CFD) models of the system were developed, but their computational cost is high for modeling long transient accident scenarios. The objective of the project is to develop a simulation of the scaled-down system using the MOOSE-based application Pronghorn. A numerical approach is needed to have a better understanding of the physics phenomena that takes place following a LOCA event, and it can guide plant designers to design parameters for the development of mitigation techniques. This scenario is applicable to all HTR designs including the pebble bed reactor concept.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Uncertainty quantification for molecular property predictions with graph neural architecture search

Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets, and generalizes well to out-of-distribution datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Exploratory analysis and performance prediction of big data transfer in High-performance Networks

Big data transfer in large-scale scientific and business applications is increasingly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) via advance bandwidth reservation. Provisioning agents need to carefully schedule data transfer requests, compute network paths, and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized, could be simply wasted due to the exclusive access during the approved time window, and cause extra overhead and complexity for resource management. This calls for accurate performance prediction to reserve bandwidths that match actual needs and avoid over-provisioning. We employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements collected in the past several years from data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated testbeds. We first analyze the performance patterns in response to a comprehensive list of parameters in end-host systems, network connections, and data transfer applications, which motivate the use of machine learning and also help us identify the effects of latent factors. We then propose threshold- and clustering-based methods to eliminate negative effects of latent factors in data preprocessing and build a robust performance predictor based on customized domain-oriented loss functions. The performance of the proposed methods is verified by extensive experiments using SVR and RFR as well as theoretical analysis of the general performance bound.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Accurate quantum chemical calculations

An important goal of quantum chemical calculations is to provide an understanding of chemical bonding and molecular electronic structure. A second goal, the prediction of energy differences to chemical accuracy, has been much harder to attain. First, the computational resources required to achieve such accuracy are very large, and second, it is not straightforward to demonstrate that an apparently accurate result, in terms of agreement with experiment, does not result from a cancellation of errors. Recent advances in electronic structure methodology, coupled with the power of vector supercomputers, have made it possible to solve a number of electronic structure problems exactly using the full configuration interaction (FCI) method within a subspace of the complete Hilbert space. These exact results can be used to benchmark approximate techniques that are applicable to a wider range of chemical and physical problems. The methodology of many-electron quantum chemistry is reviewed. Methods are considered in detail for performing FCI calculations. The application of FCI methods to several three-electron problems in molecular physics are discussed. A number of benchmark applications of FCI wave functions are described. Atomic basis sets and the development of improved methods for handling very large basis sets are discussed: these are then applied to a number of chemical and spectroscopic problems; to transition metals; and to problems involving potential energy surfaces. Although the experiences described give considerable grounds for optimism about the general ability to perform accurate calculations, there are several problems that have proved less tractable, at least with current computer resources, and these and possible solutions are discussed.

Bauschlicher, Charles W., Jr.↗
DOE OSTI2022

Progress on Demonstration of a MOOSE-Based Coupled Capability for Hot Channel Factors in Fast Reactors

Hot channel factors (HCFs) are computed values that account for the impact on predicted peak fuel, cladding, and coolant temperatures due to uncertainties in the as-built reactor’s material properties and geometry as well as uncertainties due to modeling approximations. Reduction in computed HCF values via reduction or elimination of modeling approximations may translate to significant economic savings if the reactor power can be raised due to the extra temperature margin gained. While limited historical datasets exist for sodium-cooled fast reactors (SFRs), there are no available HCF data for lead-cooled fast reactors (LFRs) outside of work generated previously within NEAMS. The computation of HCFs involves insights from reactor physics, thermal fluids and heat conduction calculations to determine how the peak temperatures respond to various uncertainties in the design. Due to the significant advantages for multi-physics coupling offered by the MOOSE framework, Griffin (MOOSE-based reactor physics code), MOOSE Heat Conduction Module, and Cardinal (MOOSE-wrapped multi-physics application which includes the NekRS thermal fluids code) are being coupled together using the MOOSE MultiApp System to develop a highfidelity multi-physics modeling capability for HCF simulations. This high-fidelity coupling workflow may also be beneficial for other fast reactor applications in the future. In previous work, Griffin and NekRS were individually assessed to ensure the necessary capabilities were in place. This work describes initial efforts to couple the codes (including folding in the MOOSE Heat Conduction Module) and determining the workflow for the perturbed calculations which will leverage the Stochastic Tools Module (STM). To our knowledge, this is the first coupling of Griffin and NekRS as well as the first exploratory use of Stochastic Tools Module for Cardinal. In this report, the neutronics code Griffin, the heat conduction solver in MOOSE, and the MOOSE-wrapped application containing NekRS (Cardinal) are linked together to demonstrate the coupled capability. Griffin and Cardinal are linked dynamically by specifying shared libraries. Different coupling hierarchies are tested for selecting the most appropriate coupling strategy. A coupling scheme is selected based on the efficiency of calculation and ease of data communication. Multiple tests are performed to choose suitable mesh structure, model configurations, scheme setup and boundary conditions to avoid loss of energy due to data interpolation between different modules or weak imposition of fluxes in finite element codes. Computational experiments are performed to study the tolerance control of each type of iteration to avoid false convergence. The coupled capability is demonstrated in both single pin and 7-pin models based on LFR materials and geometry. The study finds that the use of too large a time step size in the heat conduction module can lead to temperature oscillation even though the heat conduction equation does not have a time-derivative kernel, but only the time-dependent boundary condition. A 7-pin model without duct region achieved good convergence in the coupled calculation while a 7 pin model with duct region experienced data communication issues which need to be resolved.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2003

First Results from a Hardware-in-the-Loop Demonstration of Closed-Loop Autonomous Formation Flying

A closed-loop system for the demonstration of autonomous satellite formation flying technologies using hardware-in-the-loop has been developed. Making use of a GPS signal simulator with a dual radio frequency outlet, the system includes two GPS space receivers as well as a powerful onboard navigation processor dedicated to the GPS-based guidance, navigation, and control of a satellite formation in real-time. The closed-loop system allows realistic simulations of autonomous formation flying scenarios, enabling research in the fields of tracking and orbit control strategies for a wide range of applications. The autonomous closed-loop formation acquisition and keeping strategy is based on Lyapunov's direct control method as applied to the standard set of Keplerian elements. This approach not only assures global and asymptotic stability of the control but also maintains valuable physical insight into the applied control vectors. Furthermore, the approach can account for system uncertainties and effectively avoids a computationally expensive solution of the two point boundary problem, which renders the concept particularly attractive for implementation in onboard processors. A guidance law has been developed which strictly separates the relative from the absolute motion, thus avoiding the numerical integration of a target trajectory in the onboard processor. Moreover, upon using precise kinematic relative GPS solutions, a dynamical modeling or filtering is avoided which provides for an efficient implementation of the process on an onboard processor. A sample formation flying scenario has been created aiming at the autonomous transition of a Low Earth Orbit satellite formation from an initial along-track separation of 800 m to a target distance of 100 m. Assuming a low-thrust actuator which may be accommodated on a small satellite, a typical control accuracy of less than 5 m has been achieved which proves the applicability of autonomous formation flying techniques to formations of satellites as close as 50 m.

Gill, E.↗
DOE OSTI2022

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

Chemistry↗