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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Probabilistic Nanomagnetic Memories for Uncertain and Robust Machine Learning

This project evaluated the use of emerging spintronic memory devices for robust and efficient variational inference schemes. Variational inference (VI) schemes, which constrain the distribution for each weight to be a Gaussian distribution with a mean and standard deviation, are a tractable method for calculating posterior distributions of weights in a Bayesian neural network such that this neural network can also be trained using the powerful backpropagation algorithm. Our project focuses on domain-wall magnetic tunnel junctions (DW-MTJs), a powerful multi-functional spintronic synapse design that can achieve low power switching while also opening the pathway towards repeatable, analog operation using fabricated notches. Our initial efforts to employ DW-MTJs as an all-in-one stochastic synapse with both a mean and standard deviation didn’t end up meeting the quality metrics for hardware-friendly VI. In the future, new device stacks and methods for expressive anisotropy modification may make this idea still possible. However, as a fall back that immediately satisfies our requirements, we invented and detailed how the combination of a DW-MTJ synapse encoding the mean and a probabilistic Bayes-MTJ device, programmed via a ferroelectric or ionically modifiable layer, can robustly and expressively implement VI. This design includes a physics-informed small circuit model, that was scaled up to perform and demonstrate rigorous uncertainty quantification applications, up to and including small convolutional networks on a grayscale image classification task, and larger (Residual) networks implementing multi-channel image classification. Lastly, as these results and ideas all depend upon the idea of an inference application where weights (spintronic memory states) remain non-volatile, the retention of these synapses for the notched case was further interrogated. These investigations revealed and emphasized the importance of both notch geometry and anisotropy modification in order to further enhance the endurance of written spintronic states. In the near future, these results will be mapped to effective predictions for room temperature and elevated operation DW-MTJ memory retention, and experimentally verified when devices become available.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Posterior Regularized Bayesian Neural Network

Traditional NNs often lack the ability for uncertainty quantification. Bayesian NNs(BNNs) could help measure the confidence level by using distributions in NNs modeling. Besides, knowledge is commonly available and could improve the performance of BNNs if it can be properly incorporated. In this work, we propose a novel Posterior-Regularized BNN(PR-BNN) model by incorporating soft and hard constraints as a posterior regularization term. We also propose an augmented Lagrangian method and stochastic optimization algorithm for efficient updating via Monte Carlo sampling. The simulations and case studies for solar PV plants have shown the performance improvement of the proposed model over traditional BNNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Open-source release of CGMF 1.1 and Integration into the MCNP6.3 ® Code [Slides]

As a result of a multi-year NA-22 project, CGMF was integrated into MCNP6.2 and publicly released. CGMF was open-sourced and publicly released and MCNP6.3 was updated to include the latest version and is in the process of being publicly released. Current and future plans include global optimization and uncertainty quantification within CGMF, model parameter fitting such that CGMF may be used in ENDF/B evaluations, and improving both standalone and MCNP-integrated CGM (non-fission) simulations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Decision Science for Machine Learning (DeSciML)

The increasing use of machine learning (ML) models to support high-consequence decision making drives a need to increase the rigor of ML-based decision making. Critical problems ranging from climate change to nonproliferation monitoring rely on machine learning for aspects of their analyses. Likewise, future technologies, such as incorporation of data-driven methods into the stockpile surveillance and predictive failure analysis for weapons components, will all rely on decision-making that incorporates the output of machine learning models. In this project, our main focus was the development of decision scientific methods that combine uncertainty estimates for machine learning predictions, with a domain-specific model of error costs. Other focus areas include uncertainty measurement in ML predictions, designing decision rules using multiobjecive optimization, the value of uncertainty reduction, and decision-tailored uncertainty quantification for probability estimates. By laying foundations for rigorous decision making based on the predictions of machine learning models, these approaches are directly relevant to every national security mission that applies, or will apply, machine learning to data, most of which entail some decision context.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Status, Challenges, and Plans for Consistent Modeling and Evaluation of Fission Data: Nubar, PFNS, and FPY [Slides]

This presentation discusses the importance of consistent evaluations for fission observables. Currently, prompt fission quantities (e.g., neutron multiplicity, neutron energy spectrum, and fission product yields) are evaluated independently of one another, either with separate models or data-only/non-model evaluations (few – if any – shared model inputs) and inconsistencies can arise in evaluated data (e.g. NSE 190, 258 (2018)). They have found that consistent, model-based evaluations lead to more physical constraints on free parameters in the model, more robust predictions for other observables and other isotopes, that there are correlations between the uncertainties on different observables, and that uncertainties can be propagated to unmeasured quantities. They are working on developing models to the point where fission observables are calculated at the quality needed for an evaluation. They are striving to consistently calculate fission observables, such as $\bar{v}$, PFNS, and FPY, to a quality suitable for an evaluation. Current evaluations for fission observables use separate models for each observable (with minimal shared input) or are purely data driven. LANL models, such as the Monte Carlo CGMF and deterministic BeoH, have different strengths; the similar fission fragment initial conditions and decay models leads to consistency between the two codes (FPY can be connected to $\bar{v}$ and PFNS, even when calculated by different codes). Work is underway by Lovell to optimize the models and Neudecker is working to perform detailed uncertainty quantification. The PFNS, in particular, presents a significant challenge.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

TEDS Test Plan for Performance Characterization of Packed-bed Thermal Energy Storage Unit

This report proposes a set of test plans for performance measurement and characterization of the packed-bed thermal energy storage (TES) unit for TEDS. The main goals of this test plan are three-fold: (i) support the high-fidelity data measurement for TEDS, (ii) characterize thermal and structural performance of the TEDS TES unit, and (iii) produce high quality validation-oriented data. To achieve the goals, it is first suggested to revisit the instrument calibration and data sampling protocols applied during the startup and commissioning tests of TEDS, based on the observation from the recent CFD validation study using the TEDS TES operational data. The uncertainty quantification of the thermal and hydraulic measurements is also suggested. Lastly, test matrices are provided for charge, discharge, and standby modes of the packed-bed TES unit, respectively, to characterize the thermal and structural performances during the envisioned TEDS operations.

25 ENERGY STORAGE↗
DOE OSTI2023

Performance Assessment for the E-Area Low-Level Radioactive Waste Disposal Facility at the Savannah River Site: Chapter 4

This chapter describes the GW flow and transport conceptual models in the VZ for both generic and special waste forms in STs, ETs, LAWV, ILV, and NRCDAs. The development and implementation of the GoldSim® system model for trenches is also introduced. The Trench System Model is used for sensitivity analysis and uncertainty quantification as reported in Chapter 6.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Performance Assessment for the E-Area Low-Level Radioactive Waste Disposal Facility at the Savannah River Site: Chapter 6

This chapter provides a description of the methods used for the sensitivity and uncertainty quantification analyses and identifies the parameters and assumptions found to be most important in the determination of compliance with PA performance objectives, development of WAC, establishment of individual DU inventory limits, and other regulatory decisions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Performance Assessment for the E-Area Low-Level Radioactive Waste Disposal Facility at the Savannah River Site: Appendix I

As stated in Section 9.1.2.2, a final inventory of parent radionuclides is projected for every DU at the time of facility operational closure in 2065. These final closure inventories are upper-bound estimates wherein each DU is assumed to reach its activity capacity. Composition vectors based on existing DU inventories are employed to estimate the final compositions of all existing and future DUs. Specifically, (1) the existing DU compositions are assumed to remain constant; (2) future DUs are assigned averaged existing compositions based on appropriately averaged DUs. This information is detailed in Appendix H, Section H.7. In addition, the variability in composition among existing DUs is used to generate log-normal distributions for uncertainty quantification, which is detailed in Section I.1.1.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Thermal Neutron Scattering Research and Development at Oak Ridge National Laboratory [Slides]

This presentation provides an overview of the project, specifically describing how neutron cross sections can be broken up into several regions. It also discusses how thermal scattering law (TSL) $\textit{S(α, β)}$ is related to dynamic structure factor $\textit{S(q, E)}$ and how $\textit{S(q, E)}$ can be broken up into 2 primary components, incoherent and coherent, and how it can be calculated in several ways, including density functional theory (DFT) and molecular dynamics (MT). Additionally, it states that ENDF currently stores coherent elastic (Bragg edges), incoherent elastic and incoherent inelastic. Also discussed are the experimental capabilities, evaluations, and uncertainty quantification of the project.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Risk-Informed MultiPphysics BestEestimate Plus Uncertainties (BEPU) Application Development of RELAP5-3D Perturbation Model

The United States nuclear industry is facing a strong challenge to ensure maximum safety while enhancing economic benefit. Safety is a key parameter to all aspects related to light water reactor (LWR) nuclear power plants (NPPs), especially cost savings. Since the goal is to extend the lifetimes of these NPPs, the traditional deterministic safety concept may not guarantee a current economic asset. The Light Water Reactor Sustainability (LWRS) Program has been promoting a wide range of research and development (R&D) in this field to maximize the safety, economics, and performance of these NPPs through improved scientific understanding. One of the best practices to achieve this goal is to identify and optimize safety margins, which can lead to cost reduction. To do this, under the LWRS framework, the Risk-Informed Systems Analysis (RISA) Pathway will focus on the optimization of safety margin and minimization of uncertainties to ensure both safety and economics at the highest level. The RISA Pathway will provide enhanced capabilities for analyzing and characterizing LWR systems performance by developing and demonstrating methods, tools, and data to enable risk-informed margins management (RIMM). The goals of the RISA Pathway are twofold: (1) deploy the risk-informed tools and methods that enable better representation of safety margins and factors that contribute to cost and safety; and (2) conduct advanced risk assessment applications with industry to support margin management strategies that enable more cost-effective plant operation. The tools and methods provided by the RISA Pathway will support effective margin management for both active and passive safety systems, structures, and components (SSC) of an NPP. The tools and methods used in the RISA Pathway should have high confidence and highest technical maturity for and implementation to industry at its current setting. They should also have a capability to support risk-informed decision making for both probabilistic and deterministic elements of safety. The RISA Pathway will, therefore, perform a comprehensive assessment of verification and validation (V&V) status of RISA Toolkit to enhance credibility RISA Toolkit which be used by industry. This report summarizes RELAP5-3D development activities for best estimated and uncertainty (BEPU) capability to support ongoing RISA Pathway pilot projects for risk informed uncertainty quantification applications. Work scope includes (1) development of initial perturbation model for selected closure laws and (2) testing of suitable probability distribution functions (PDF) for the selected correlations. Developed BEPU modules are tested with selected separate effect cases and compared with conventional thermal-hydraulics model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Advancing the Theory of Nuclear Data Evaluations [Slides]

This presentation covers a few key topics in relation to advancing the theory of nuclear data evaluations. First it targets and highlights the NCSP mission and vision along with the five-year plan. This lecture focuses upon the uncertainty quantification and SAMMY modernization. This presentation covers theory, and a framework is provided. The presentation concludes with goals, outlooks, and accomplishments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Harnessing Uncertainty through Functional Data Analysis in Gas Breakthrough Data

Detecting subsurface explosions from radionuclide gas migration through rock fractures is an effective way to identify nuclear activity. Los Alamos National Laboratory (LANL) has developed simulation methods, based on data from the 1962 Hardhat underground nuclear test, to predict gas breakthrough times at the surface. However, these methods rely on an imperfect understanding of the relationship between rock damage and fracture permeability. Our clinic project studies methods for predicting breakthrough curves that characterize total mass produced as a function of time, as well as quantifying the uncertainty associated with these predictions. The model that is currently employed to relate damage to permeability uses an empirically motivated power-law expression, with a range of parameter values that are compatible with the experimental Hardhat data. We develop emulators, built from functional data analysis techniques and trained on simulation data, that rapidly predict the gas breakthrough curve given a damage field and given the parameter values of the power-law equation. Using Bayesian regression, we address the problem of uncertainty quantification in our emulators. Finally, in order to test the robustness of the model, we further validate it on a damage field representing different physical conditions.

58 GEOSCIENCES↗
DOE OSTI2022

Gaussian process for calibration and control of GlueX Central Drift Chamber

We have developed and implemented a machine learning based system to calibrate and control the GlueX Central Drift Chamber at Jefferson Lab, VA, in near real-time. The system monitors environmental and experimental conditions during data taking and uses those as inputs to a Gaussian process (GP) with learned prior. The GP predicts calibration constants in order to recommend a high voltage (HV) setting for the detector that maintains consistent detector performance (gain and resolution) throughout data taking. This approach is in stark contrast to traditional detector operations in which the detector operates at fixed HV and its calibration parameters vary quite considerably with time. Additionally, the ML based system utilizes uncertainty quantification to correct the recommended control parameters when appropriate. We will present results from the ML system autonomously during the Charged Pion Polarizability (CPP) experiment conducted in Hall D at Jefferson Lab.

McSpadden, Helen↗
DOE OSTI2023

Assessing Low-Temperature Geothermal Play Types: Relevant Data and Play Fairway Analysis Methods

The U.S. Department of Energy (DOE) Geothermal Technologies Office (GTO) is supporting the Geothermal Heating and Cooling Geospatial Datasets and Analysis project conducted by the National Renewable Energy Laboratory (NREL) as part of a broader effort to demonstrate the multi-faceted value of integrating geothermal power and geothermal heating and cooling (GHC) technologies into national decarbonization plans and community energy plans. Currently, there is a need to establish baseline low-temperature geothermal resource datasets and evaluate methods for deploying these technologies to provide the basis for supporting private sector investment. This project is focused on collecting baseline datasets, updating conceptual models, and creating Play Fairway Analysis (PFA) workflows for low-temperature (<150 degrees Celsius) geothermal resources of different geothermal play types (i.e., sedimentary basin, orogenic belts, and radiogenic geothermal play types) that could be used for geothermal heating and cooling (GHC), combined heat and power (CHP), and other geothermal direct uses (GDU) applications. Low-temperature geothermal resources are defined as reservoirs - natural or engineered - with temperatures <150 degrees Celsius. While the focus in the NREL effort is on GHC, resources at the upper end of this temperature range can also be used for small-scale power generation. This project does not include Ground Source Heat Pumps (GSHPs) technologies because they can be effectively developed almost anywhere. Low-temperature geothermal resources have not been studied as extensively as higher- to medium-temperature geothermal resources, but there is recent interest in improving understanding of these types of resources with an uptick of interest in geothermal technologies for decarbonizing heating and cooling systems. In addition, Enhanced Geothermal Systems (EGS) and other emerging technologies for exploiting petrothermal resources have opened the possibility of utilizing deep sedimentary basin systems, where porous media provide permeability and high temperatures can be reached at great depths. This project takes the approach of classifying low- temperature geothermal resources by geothermal play type (GPT). We defined and characterized three major classes of low-temperature GPT: sedimentary basins, orogenic systems, and radiogenic systems. We develop methodologies for evaluating and analyzing the potential for these resources building off the PFA approach to de-risking geothermal exploration and characterization. The proposed PFA approach for low-temperature geothermal resources includes: 1) identifying relevant data (e.g., datasets such bottom-hole temperatures from oil and gas wells, heat flow data, Quaternary faults and stress field data, geophysical data, etc.); 2) grouping and weighting of relevant datasets into PFA criteria (e.g., geological, risk, and economic criteria); 3) uncertainty quantification; 4) developing favorability or common risk maps for low-temperature geothermal resources to identify potential locations for more focused data collection; and 5) estimating electric power generation and heating potential at those locations using the GeoRePORT Resource Size Assessment Tool (RSAT). This project should facilitate future deployment of GHC, CHP, and GDU by providing data, tools, and a workflow applicable to low-temperature geothermal resources. Increased deployment of GHC and GDU will help achieve national and local decarbonization goals.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

TRUST Sensors in Environments: Fiber Optic Displacement (SEFOD) Report

The sensors in environments for fiber optic displacement gages (SEFOD) testbed is a collaboration between W-13, and E-14. Testbeds in this project are focused on characterization and propagation of the uncertainty in experimental techniques used by E-14 in WR-like environments. Discussions with SMEs identified instrumentation as the best candidates for single feature testbeds addressing experimental uncertainty sources. Previous work and student projects have addressed instrumentation uncertainty quantification of thermocouples, load cells, and accelerometers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Hydrogen Diffusion Coefficient Measures on Thin Film Uranium Oxide

Thin films of uranium oxides, putatively UO 2 and U 3 O 8 , were deposited on palladium/silver (75/25) foil discs (10 μm thick, 10 mm diameter). Methodology for sealing these foils, applying a hydrogen pressure (~ 1 atm) to the oxide side, and measuring the pressure on the permeate side (opposite side of foil) is reported. The experimental apparatus is held at 100 °C to speed diffusion, aided by the pressure differential across the foil (~1 atm (750 torr) on oxide side, initially low vacuum on permeate side, ~1x 10 -4 torr). Early results indicate an effective diffusion coefficient of roughly 7.66 x 10 -17 +/- 2.22 x 10 -17 cm 2 /sec for UO 2 . These values are in line with expectations and prior measures relative to large lag times for system baseline (substrate only). Evaluation of the technique for U 3 O 8 thin films suggests further development will be needed to extend the technique to more oxidized films (U 3 O 8 , UO 3 ). Characterization of the foils (thickness and speciation) post diffusion experiments will be carried out by SIGMA (Eric Tegtmeier and Andy Richards) in FY24 which will sharpen the uncertainty quantification for UO 2 diffusion coefficients, aiding the nucleation model for DRACO.

36 MATERIALS SCIENCE↗