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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Long-term hydro-economic analysis tool for evaluating global groundwater cost and supply: Superwell v1.1

Abstract. Groundwater plays a key role in meeting water demands, supplying over 40 % of irrigation water globally, with this role likely to grow as water demands and surface water variability increase. A better understanding of the future role of groundwater in meeting sectoral demands requires an integrated hydro-economic evaluation of its cost and availability. Yet substantial gaps remain in our knowledge and modeling capabilities related to groundwater availability, recharge, feasible locations for extraction, extractable volumes, and associated extraction costs, which are essential for large-scale analyses of integrated human–water system scenarios, particularly at the global scale. To address these needs, we developed Superwell, a physics-based groundwater extraction and cost accounting model that operates at sub-annual temporal and at the coarsest 0.5° (≈50 km × 50 km) gridded spatial resolution with global coverage. The model produces location-specific groundwater supply–cost curves that provide the levelized cost to access different quantities of available groundwater. The inputs to Superwell include recent high-resolution hydrogeologic datasets of permeability, porosity, aquifer thickness, depth to water table, recharge, and hydrogeological complexity zones. It also accounts for well capital and maintenance costs, as well as the energy costs required to lift water to the surface. The model employs a Theis-based scheme coupled with an amortization-based cost accounting formulation to simulate groundwater extraction and quantify the cost of groundwater pumping. The result is a spatiotemporally flexible, physically realistic, economics-based model that produces groundwater supply–cost curves. We show examples of these supply–cost curves and the insights that can be derived from them across a set of scenarios designed to explore model outcomes. The supply–cost curves produced by the model show that most (90 %) nonrenewable groundwater in storage globally is extractable at costs lower than USD 0.57 m−3, while half of the volume remains extractable at under USD 0.108 m−3. The global unit cost is estimated to range from a minimum of USD 0.004 m−3 to a maximum of USD 3.971 m−3. We also demonstrate and discuss examples of how these cost curves could be used by linking Superwell's outputs with other models to explore coupled human–environmental system challenges, such as water resources planning and management, or broader analyses of multisectoral feedbacks.

Global Change Analysis Model (GCAM)↗
DOE OSTI2021

Application of the FaMUS Methodology to the NSUF Research Outputs Through End of 2019

The Nuclear Science User Facilities (NSUF) is one of a diverse group of DOE user facilities. It is focused on advancing the understanding of radiation effects in nuclear fuels and materials in support of nuclear energy applications. The NSUF has been operating since 2007 and has developed a significant portfolio of supported research. Therefore, it is appropriate to consider its achievements and to determine its successes and shortfalls. As part of this analysis of the NSUF research program, the NSUF has developed a novel and elegant formalism for assessing the current level of understanding of nuclear fuels and materials for use in nuclear environments: the NSUF Fuels and Materials Understanding Scale (FaMUS). The FaMUS methodology is being applied to the NSUF portfolio on an ongoing basis to quantify the progress made. This report summarizes the status of the assessment exercise and highlights general lessons learned. This examination will facilitate capability balancing of future research by identifying gaps in knowledge, understanding, testing, and demonstration allowing the NSUF program office in conjunction with DOE-NE to direct resources to important aspects of the DOE-NE mission using “emphasize & enhance”, “maintain”, and “encourage excellence” classifications as well as providing increased transparency to researchers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

AL4GAP: Active learning workflow for generating DFT-SCAN accurate machine-learning potentials for combinatorial molten salt mixtures

Machine learning interatomic potentials have emerged as a powerful tool for bypassing the spatiotemporal limitations of ab initio simulations, but major challenges remain in their efficient parameterization. We present AL4GAP, an ensemble active learning software workflow for generating multicomposition Gaussian approximation potentials (GAP) for arbitrary molten salt mixtures. The workflow capabilities include: (1) setting up user-defined combinatorial chemical spaces of charge neutral mixtures of arbitrary molten mixtures spanning 11 cations (Li, Na, K, Rb, Cs, Mg, Ca, Sr, Ba and two heavy species, Nd, and Th) and 4 anions (F, Cl, Br, and I), (2) configurational sampling using low-cost empirical parameterizations, (3) active learning for down-selecting configurational samples for single point density functional theory calculations at the level of Strongly Constrained and Appropriately Normed (SCAN) exchange-correlation functional, and (4) Bayesian optimization for hyperparameter tuning of two-body and many-body GAP models. Here, we apply the AL4GAP workflow to showcase high throughput generation of five independent GAP models for multicomposition binary-mixture melts, each of increasing complexity with respect to charge valency and electronic structure, namely: LiCl–KCl, NaCl–CaCl 2 , KCl–NdCl 3 , CaCl 2 –NdCl 3 , and KCl–ThCl 4 . Our results indicate that GAP models can accurately predict structure for diverse molten salt mixture with density functional theory (DFT)-SCAN accuracy, capturing the intermediate range ordering characteristic of the multivalent cationic melts.

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

How Can Floor Covering Influence Buildings’ Demand Flexibility?

Although the thermal mass of floors in buildings has been demonstrated to help shift cooling load, there is still a lack of information about how floor covering can influence the floor’s load shifting capability and buildings’ demand flexibility. To fill this gap, we estimated demand flexibility based on the daily peak cooling load reduction for different floor configurations and regions, using EnergyPlus simulations. As a demand response strategy, we used precooling and global temperature adjustment. The result demonstrated an adverse impact of floor covering on the building’s demand flexibility. Specifically, under the same demand response strategy, the daily peak cooling load reductions were up to 20–34% for a concrete floor whereas they were only 17–29% for a carpet-covered concrete floor. This is because floor covering hinders convective coupling between the concrete floor surface and the zone air and reduces radiative heat transfer between the concrete floor surface and the surrounding environment. In hot climates such as Phoenix, floor covering almost negated the concrete floor’s load shifting capability and yielded low demand flexibility as a wood floor, representing low thermal mass. Sensitivity analyses showed that floor covering’s effects can be more profound with a larger carpet-covered area, a greater temperature adjustment depth, or a higher radiant heat gain. With this effect ignored for a given building, its demand flexibility would be overestimated, which could prevent grid operators from obtaining sufficient demand flexibility to maintain a grid. Our findings also imply that for more efficient grid-interactive buildings, a traditional standard for floor design could be modified with increasing renewable penetration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Adaptive Data-Driven Model Predictive Control for Heat Pipe Microreactors

To establish a technical basis for self-regulating microreactors, a model predictive control (MPC) system is investigated to proactively respond to anomalies and disturbances in anticipation of potential deviations from operating setpoints. Due to the difficulty of developing a physics-based surrogate model that can accurately match plant data in various operating conditions, machine learning algorithms are used in MPC, which allow for learning from both simulation and operation data, thus efficiently describing the targeted transient with arbitrary accuracy. However, one of the biggest concerns in applying ML algorithms like artificial neural networks (ANNs) is that the predictive capabilities of ANN are limited by training data. If there are gaps between the training and target domain, the accuracy of an ANN can degrade significantly when it is used to predict unseen data. To improve the predictive capability of ANN and enable a confident use of data-driven MPCs outside the training data, this study proposes an adaptive data-driven MPC framework. The system will monitor the discrepancy between plant responses and surrogate predictions, fine-tune the ANN-based surrogate when a large discrepancy is detected, and continue MPC operation with updated surrogates. The framework is demonstrated on a point kinetic model for microreactors. The hyperparameters of the update strategy, including layers to update, error thresholds, learning rate discount, and number of data points used for fine-tuning, are optimized so the simulated microreactor is able to follow changes in setpoint with the smallest of deviations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Adaptable Data Driven Model Predictive Control for Heat Pipe Microreactors

To establish a technical basis for self-regulating microreactors, a model predictive control (MPC) system is investigated to proactively respond to anomalies and disturbances in anticipation of potential deviations from operating setpoints. Due to the difficulty of developing a physics-based surrogate model that can accurately match plant data in various operating conditions, machine learning algorithms are used in MPC, which allow for learning from both simulation and operation data, thus efficiently describing the targeted transient with arbitrary accuracy. However, one of the biggest concerns in applying ML algorithms like artificial neural networks (ANNs) is that the predictive capabilities of ANN are limited by training data. If there are gaps between the training and target domain, the accuracy of an ANN can degrade significantly when it is used to predict unseen data. To improve the predictive capability of ANN and enable a confident use of data-driven MPCs outside the training data, this study proposes an adaptive data-driven MPC framework. The system will monitor the discrepancy between plant responses and surrogate predictions, fine-tune the ANN-based surrogate when a large discrepancy is detected, and continue MPC operation with updated surrogates. The framework is demonstrated on a point kinetic model for microreactors. The hyperparameters of the update strategy, including layers to update, error thresholds, learning rate discount, and number of data points used for fine-tuning, are optimized so the simulated microreactor is able to follow changes in setpoint with the smallest of deviations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

A Review of Sandia Energy Storage Research Capabilities and Opportunities (2020 to 2030)

Large-scale integration of energy storage on the electric grid will be essential to enabling greater penetration of intermittent renewable energy sources, modernizing the grid for increased flexibility security, reliability, and resilience, and enabling cleaner forms of transportation. The purpose of this report is to summarize Sandia's research and capabilities in energy storage and to provide a preliminary roadmap for future efforts in this area that can address the ongoing program needs of DOE and the nation. Mission and vision statements are first presented followed by an overview of the organizational structure at Sandia that provides support and activities in energy storage. Then, a summary of Sandia's energy storage capabilities is presented by technology, including battery storage and materials, power conversion and electronics, subsurface-based energy storage, thermal/thermochemical energy storage, hydrogen storage, data analytics/systems optimization/controls, safety of energy storage systems, and testing/demonstrations/model validation. A summary of identified gaps and needs is also presented for each technology and capability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Distribution System Research Roadmap; Energy Efficiency and Renewable Energy

The scope of the U.S. Department of Energy's Energy Efficiency and Renewable Energy (EERE) office covers a number of distributed energy resource (DER) technologies, including distributed photovoltaics, smart buildings, wind, water, behind-the-meter-storage, and electric vehicles. The impact of these technologies on the distribution system is often assessed with an individual technology focus. Similarly, different technology offices often leverage different sets of tools, leading to analyses that are not comparable. EERE sought the ability to assess the impact of integrating multiple DER technologies, and to comprehensively address DER integration challenges across the portfolio of EERE technologies. This project built on existing work understanding technical challenges, mapped out the key research questions, assessed relevant capabilities across the national laboratory network, identified key gaps, and produced a research roadmap to inform EERE investment decisions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Accelerated Irradiation Testing and Post-Irradiation Characterization: U.S.-Based Capabilities for Advanced Nuclear Systems and Radioisotope Production

Irradiation experiments and post-irradiation examinations, together referred to as irradiation testing (IRT), are prerequisites for nuclear fuel and material qualification for the deployment of new and advanced reactors, as well as radioisotope production, thereby ensuring regulatory compliance. Qualified research and test reactors (RTRs) and testing facilities are essential to enable IRT to verify performance and safety under prototypical reactor conditions. In the past, qualification of new fuels or structural materials required about 20 years. Synergist strategies, advanced tools, and qualified methods are needed to greatly reduce this timeframe of IRT and radioisotope production. This study, termed accelerated-IRT, focuses on identifying gaps and leveraging U.S.-based RTRs and material testing capabilities, leveraging the preliminary evaluation and qualification of selected RTRs to provide a generic as well as specific-case solution paths forward, ensuring adherence to stringent regulatory standards. Furthermore, IRT and radioisotope production utilizing qualified RTRs necessarily includes modeling and simulation to support the design (i.e. neutronics, thermal, and structural aspects) and manufacturing of irradiation test specimens, vehicles, capsules, apparatuses, and flow loops. In addition, IRT can be improved by applying advanced manufacturing techniques and in-pile sensors and instrumentation, as discussed in this study.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Novel, active, and uncultured hydrocarbon-degrading microbes in the ocean

ABSTRACT Given the vast quantity of oil and gas input to the marine environment annually, hydrocarbon degradation by marine microorganisms is an essential ecosystem service. Linkages between taxonomy and hydrocarbon degradation capabilities are largely based on cultivation studies, leaving a knowledge gap regarding the intrinsic ability of uncultured marine microbes to degrade hydrocarbons. To address this knowledge gap, metagenomic sequence data from the Deepwater Horizon (DWH) oil spill deep-sea plume was assembled to which metagenomic and metatranscriptomic reads were mapped. Assembly and binning produced new DWH metagenome-assembled genomes that were evaluated along with their close relatives, all of which are from the marine environment (38 total). These analyses revealed globally distributed hydrocarbon-degrading microbes with clade-specific substrate degradation potentials that have not been reported previously. For example, methane oxidation capabilities were identified in all Cycloclasticus . Furthermore, all Bermanella encoded and expressed genes for non-gaseous n -alkane degradation; however, DWH Bermanella encoded alkane hydroxylase, not alkane 1-monooxygenase. All but one previously unrecognized DWH plume member in the SAR324 and UBA11654 have the capacity for aromatic hydrocarbon degradation. In contrast, Colwellia were diverse in the hydrocarbon substrates they could degrade. All clades encoded nutrient acquisition strategies and response to cold temperatures, while sensory and acquisition capabilities were clade specific. These novel insights regarding hydrocarbon degradation by uncultured planktonic microbes provides missing data, allowing for better prediction of the fate of oil and gas when hydrocarbons are input to the ocean, leading to a greater understanding of the ecological consequences to the marine environment. IMPORTANCE Microbial degradation of hydrocarbons is a critically important process promoting ecosystem health, yet much of what is known about this process is based on physiological experiments with a few hydrocarbon substrates and cultured microbes. Thus, the ability to degrade the diversity of hydrocarbons that comprise oil and gas by microbes in the environment, particularly in the ocean, is not well characterized. Therefore, this study aimed to utilize non-cultivation-based ‘omics data to explore novel genomes of uncultured marine microbes involved in degradation of oil and gas. Analyses of newly assembled metagenomic data and previously existing genomes from other marine data sets, with metagenomic and metatranscriptomic read recruitment, revealed globally distributed hydrocarbon-degrading marine microbes with clade-specific substrate degradation potentials that have not been previously reported. This new understanding of oil and gas degradation by uncultured marine microbes suggested that the global ocean harbors a diversity of hydrocarbon-degrading bacteria, which can act as primary agents regulating ecosystem health.

Howe, Kathryn L.↗
DOE OSTI2021

Development of phenomena identification and ranking table for Westinghouse lead fast reactor’s safety

The Westinghouse Lead-cooled Fast Reactor (LFR) is a medium-size, passively safe, economic, Gen-IV nuclear reactor. An important effort within the Westinghouse LFR program is the development of the safety analysis methodology, which comprises computer code development, model development, and experimental testing. A key initial task associated with the development of the safety analysis methodology is the identification of processes and phenomena that affect the plant's capability to meet selected safety performance indicators. This is accomplished through the development of a Phenomena Identification and Ranking Table (PIRT) for selected accident scenarios which, for this specific PIRT effort, included selected postulated design basis accidents and hypothetical beyond design basis accidents in LFRs. This paper describes the role of PIRT in the development of the Westinghouse LFR safety analysis methodology and the process used in the PIRT development. Specifically, the Westinghouse LFR PIRT assessed importance of pertinent phenomena and identified gaps in their knowledge-base by evaluating current modeling capabilities and data available for validation. The ultimate goal was to provide guidance on computer code development and validation efforts and to prioritize testing to support LFR design and licensing. The key phenomena and processes that are deemed highly important for the safety performance indicators, but for which the state of knowledge is low, are presented. The testing program and analyses development are planned to address significant phenomena in the PIRT.

Lead fast reactor↗
DOE OSTI2024

Deployment of BISON models of fuel restructuring at high burnup and related fission gas behavior in UO 2

This milestone report details the advancements made in fiscal year 2024 under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to improve the modeling of fission gas behavior in high burnup UO 2 nuclear fuel in the BISON fuel performance code. As nuclear fuel is pushed to higher burnups, significant microstructural changes occur within the fuel, including the formation of a high burnup structure (HBS) on the pellet rim and a dark zone deeper within the pellet. These regions, characterized by subgrain formation and increased pore densities, have critical implications for fission gas behavior and release, which are not well understood. The modeling capabilities in BISON did not adequately predict these phenomena, leading to an underestimation of fuel restructuring and - potentially - of fission gas release. To address these gaps, this milestone focused on three key objectives: (1) reviewing and assessing Sifgrs's capabilities for low burnup fuel, on which high burnup capabilities rely, (2) validating and expanding HBS fission gas modeling capabilities, including investigating mechanisms for fission gas release from HBS, and (3) expanding Sifgrs to enable modeling of dark zone formation and its effects on fission gas behavior. These objectives were achieved and are described herein. The achievements of this NEAMS milestone are significant for the industry's goal of burnup extension. The improved predictive modeling capabilities for both low- and high-burnup conditions enhance our understanding of fuel performance under both normal operations and transient scenarios. Although goals were reached, future work is necessary to validate these models against experimental data and quantify their accuracy in different conditions. In parallel, mechanistic modeling efforts should continue to extend and refine these capabilities to increase accuracy while reducing reliance on empirical models. This will ensure robust performance across a broader range of conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Using Multiple Signatures to Improve Accuracy of Substorm Identification

Abstract We have developed a new procedure for combining lists of substorm onset times from multiple sources. We apply this procedure to observational data and to magnetohydrodynamic (MHD) model output from 1–31 January 2005. We show that this procedure is capable of rejecting false positive identifications and filling data gaps that appear in individual lists. The resulting combined onset lists produce a waiting time distribution that is comparable to previously published results, and superposed epoch analyses of the solar wind driving conditions and magnetospheric response during the resulting onset times are also comparable to previous results. Comparison of the substorm onset list from the MHD model to that obtained from observational data reveals that the MHD model reproduces many of the characteristic features of the observed substorms, in terms of solar wind driving, magnetospheric response, and waiting time distribution. Heidke skill scores show that the MHD model has statistically significant skill in predicting substorm onset times.

Haiducek, John D.↗
DOE OSTI2022

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

National Reactor Innovation Center (NRIC) Chobani

Authorized by the Nuclear Energy Innovation Capabilities Act (NEICA) Partner with industry to bridge the gap between research and commercial deployment Leverage national lab expertise and infrastructure Manage demonstrations to success

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Polyphase wireless power transfer systems, coil assemblies and resonant networks

Polyphase wireless power transfer systems are provided. The transfer system may be used for charging hybrid and electric vehicles. The systems are capable of transferring over 50 KW over an air gap of 15 cm. The systems use a rotating magnetic field to transfer power. The system may comprise transmitter coil assembly. The coil assembly may be one or more layers. The system may employ either unipolar or bipolar coils. The transmitter also comprises compensating capacitance connected in series with at least one coil for each phase. A value of the compensating capacitance for each phase is determined such that the transmitter has at least two independently excitable resonant modes at a resonant frequency. The transmitter is compatible with a plurality of different receivers including three-phase, single phase with a circular coil and single phase with DD coils.

Pries, Jason L.↗
DOE OSTI2025

Mind the gap: Bridging the divide between AI aspirations and the reality of autonomous microscopy

What does materials science look like in the “Age of Artificial Intelligence?” Each material’s domain—synthesis, characterization, and modeling—has a different answer to this question, motivated by unique challenges and constraints. This work focuses on the tremendous potential of autonomous characterization within electron microscopy. We present our recent advancements in developing domain-aware, multimodal models for microscopy analysis capable of describing complex atomic systems. We then address the critical gap between the theoretical promise of autonomous microscopy and its current practical limitations, showcasing recent successes while highlighting the necessary developments to achieve robust, real-world autonomy.

2D materials↗
DOE OSTI2021

National Ignition Facility 192 beam 3w power sensor

NIF requires additional laser diagnostic capabilities to support existing missions and drive performance of the laser to higher powers and energies. There exists a current gap in laser power data on the facility due to the subset of beamlines that are measurement capable. Additionally, as NIF increases the radiation yield on target experiments, the performance of the current laser diagnostic 3w power sensor system has been steadily decreasing. The degradation of the current system is not well understood and needs to be characterized to facilitate any new system design.

42 ENGINEERING↗