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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Black-Box Neural System Identification and Differentiable Programming to Improve Earth System Model Predictions February

Focal Area(s): Focal Area 2: AI for predictive modeling, including AI-derived model components, and AI-enabled applications including parameter optimization, data assimilation, and uncertainty quantification. Focal Area 1: AI-assisted data assimilation using numerical Earth system models. Science Challenge: Earth system models have structural biases that lead to uncertain predictions, and their complexity and expense makes it difficult constraining the models with data or improved physical understanding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Sensitivity and Uncertainty Analysis of Generator Failures under Extreme Temperature Scenarios in Power Systems

This report summarizes work done under the Verification, Validation, and Uncertainty Quantification (VVUQ) thrust area of the North American Energy Resilience Model (NAERM) Program. The specific task of interest described in this report is focused on sensitivity analysis of scenarios involving failures of both wind turbines and thermal generators under extreme cold-weather temperature conditions as would be observed in a Polar Vortex event.

17 WIND ENERGY↗
DOE OSTI2021

SUPCRTNE Thermodynamic Database Engine

This progress report (Level 4 Milestone Number M4SF-21LL010301052) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite Activity Number SF-21LL01030105. LLNL is leading efforts in the development of thermodynamic databases in support of the Spent Fuel and Waste Science Technology (SFWST) program. Thermodynamic models provide the basis for understanding the stability of solid phases and speciation of aqueous species and modeling the evolution of repository conditions. The LLNL effort is being performed in coordination with other US database development efforts. The effort includes a review of available thermochemical databases and a path forward for database integration. International coordination with the NEA-TDB is supported through crystalline international work package.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Artificial Intelligence/Machine Learning Technology in Power System Applications

The primary purpose of this report is to provide an overview of the advancement in artificial intelligence and machine learning (AI/ML) technologies and their applications in power systems. It offers a foundation for understanding the transformative role of AI/ML in power systems and aims to stimulate further research and development in this area. This report begins with a historical perspective of AI/ML technologies, then explores their advancement to today’s prominence. The document highlights key contributors to the success of AI/ML technologies, including increased computational power, greater data availability, innovative algorithms, and advanced tools. It further introduces various AI/ML techniques, including supervised, unsupervised and reinforcement learning, graph neural networks, and generative AI. It also emphasizes the critical importance of ensuring the safety, security, and trustworthiness of these AI/ML techniques within this sector. The report reviews the recent representative advancements in various power system applications enhanced by AI/ML techniques, underscoring key developments and their transformative impact as evidenced by numerous studies. It also explores both the opportunities and challenges associated with the application of AI/ML technologies to improve power system applications. While the report extensively covers AI/ML applications in power systems, focusing primarily on the technical and operational aspects, it may not thoroughly explore the sociopolitical, economic, and broader regulatory implications of AI/ML integration in power systems. AI/ML techniques hold significant potential for enhancing power system applications; however, they are not omnipotent. It is crucial to acknowledge their limitations and understand that they may not be able to address all challenges in the power system domain. Various factors must be considered that influence the implementation, adoption, and effectiveness of AI/ML solutions, including but not limited to safety, security, transparency, and trustworthiness. Additionally, the incorporation of advanced human–machine interfaces is essential, as it enables humans to validate the effectiveness of AI/ML solutions while remaining actively engaged, fostering trust in AI/ML deployment. Finally, the report summarizes AI/ML research activities supported by the Department of Energy (DOE) Office of Electricity (OE) through the Advanced Grid Modeling (AGM) program. The work aligns with the interests and mission of DOE-OE AGM, with the report serving as a resource for identifying existing progress and for pinpointing future applications within AI/ML that need further exploration and support.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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 OSTI2022

Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution

The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in the automotive industry and academia for many years. Their real-time application in modern vehicles is, however, still lagging behind. While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. This paper demonstrates that Neuroevolution associated drive cycle classification algorithms can infer optimal control strategies for any system complexity and environment, hence streamlining and speeding up the control development process. Neuroevolution also circumvents the integration of low fidelity online plant models, further avoiding prohibitive embedded computing requirements and fidelity loss. This brings the prospect of optimal control to complex multi-physics system applications. The methodology presented here covers the development of the drive cycles used to train and validate the neurocontrollers and classifiers, as well as the application of the Neuroevolution process.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6

Abstract. Human land use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth's surface, with consequences for climate and other ecosystem services. In the future, land use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community has developed the next generation of advanced Earth system models (ESMs) to estimate the combined effects of human activities (e.g., land use and fossil fuel emissions) on the carbon–climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015–2100) from Integrated Assessment Model (IAM) teams, is required as input for these models. With most ESM simulations for CMIP6 now completed, it is important to document the land use patterns used by those simulations. Here we present results from the Land-Use Harmonization 2 (LUH2) project, which smoothly connects updated historical reconstructions of land use with eight new future projections in the format required for ESMs. The harmonization strategy estimates the fractional land use patterns, underlying land use transitions, key agricultural management information, and resulting secondary lands annually, while minimizing the differences between the end of the historical reconstruction and IAM initial conditions and preserving changes depicted by the IAMs in the future. The new approach builds on a similar effort from CMIP5 and is now provided at higher resolution (0.25∘×0.25∘) over a longer time domain (850–2100, with extensions to 2300) with more detail (including multiple crop and pasture types and associated management practices) using more input datasets (including Landsat remote sensing data) and updated algorithms (wood harvest and shifting cultivation); it is assessed via a new diagnostic package. The new LUH2 products contain > 50 times the information content of the datasets used in CMIP5 and are designed to enable new and improved estimates of the combined effects of land use on the global carbon–climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Prospective Design Method for Nuclear Power: The Evaluation, Requirements, and Goals Outline for Nuclear (ERGON) Method

Human factors researchers at Idaho National Laboratory (INL) have worked on projects spanning control room modernization, operator support systems, visualization design, and novel system creation. These projects demonstrated the need for an explicit design method for nuclear power. Human factors teams found a high standard in the Human Factors Engineering Program Review Model (NUREG-0711) and needed a design methodology which could be successful in gaining approval. Previous work has been synthesized as the Evaluation, Requirements, and Goals Outline for Nuclear (ERGON) method here. Design tasks are broken into four phases: Context and Orientation, Human Factors Review, Prototyping and Evaluation, Iteration and Improvement. ERGON is intended as a flexible and direct design method for many applications in nuclear power. ERGON has been vetted through collaborative research and development with nuclear utilities and as such, the ERGON method can assist utilities to achieve approval from a NUREG-0711 summative evaluation for HSI implementations.

42 ENGINEERING↗
NASA NTRS1971

Data catalog of satellite experiments

This catalog is divided into three sections: data description contains descriptions of data available at or through NSSDC as well as descriptions of the experiments and spacecraft from which the data originated; and supporting data contains brief descriptions of space environment models and programs distributed by NSSDC. Section 3 is a series of indexes that contain: (1) a listing of all spacecraft, experiment, and data descriptions presented in Section 1 plus tables indicating the period for which each spacecraft was operational; (2) an index of all spacecraft described here, identified by common names and alternate names; (3) a listing of the original experiment institutions for all experiments described; (4) a listing of the investigators associated with the experiments and their current affiliations; and (5) an index of all experiments sorted by phenomenon measured.

Source record↗
NASA NTRS1972

Remote manipulator dynamic simulation

A simulator to generate the real time visual scenes required to perform man in the loop investigations of remote manipulator application and design concepts for the space shuttle is described. The simulated remote manipulator consists of a computed display system that uses a digital computer, the electronic scene generator, an operator's station, and associated interface hardware. A description of the capabilities of the implemented simulation is presented. The mathematical models and programs developed for the simulation are included.

Wild, E. C.↗
NASA NTRS1972

Corn blight review: Sampling model and ground data measurements program

The sampling plan involved the selection of the study area, determination of the flightline and segment sample design within the study area, and determination of a field sample design. Initial interview survey data consisting of crop species acreage and land use were collected. On all corn fields, additional information such as seed type, row direction, population, planting date, ect. were also collected. From this information, sample corn fields were selected to be observed through the growing season on a biweekly basis by county extension personnel.

Allen, R. D.↗
NASA NTRS1972

Analysis of legged landers for the survivable soft landing of instrument payloads.

Two methods of analysis have been developed for legged planetary landers. The first of these, the Large Displacement Gear Analysis, is a design tool for statically determining the large displacement stroking behavior, energy absorption characteristics, and internal load distributions in a single gear. The second, the Landing Dynamics Analysis, predicts the spatial landing dynamics of a legged lander. Effects of structural flexibility, elastic-plastic gear load characteristics, and soil properties on the loads, motions, and stability of the lander may be determined. Validation of the analytical techniques is accomplished through comparison of predicted results and experimental data obtained during a model test program.

Laurenson, R. M.↗
NASA NTRS1972

Study of short haul high-density V/STOL transportation systems. Volume 2: Appendices

Essential supporting data to the short haul transportation study are presented. The specific appendices are arena characteristics, aerospace transportation analysis computer program, economics, model calibration, STOLport siting and services path selection, STOL schedule definition, tabulated California corridor results, and tabulated Midwest arena results.

Solomon, H. L.↗
NASA NTRS1972

Robot, computer problem solving system

The development of a computer problem solving system is reported that considers physical problems faced by an artificial robot moving around in a complex environment. Fundamental interaction constraints with a real environment are simulated for the robot by visual scan and creation of an internal environmental model. The programming system used in constructing the problem solving system for the simulated robot and its simulated world environment is outlined together with the task that the system is capable of performing. A very general framework for understanding the relationship between an observed behavior and an adequate description of that behavior is included.

Becker, J. D.↗