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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Earth observations and global change decision making: A special bibliography, 1991

The first section of the bibliography contains 294 bibliographic citations and abstracts of relevant reports, articles, and documents announced in 'Scientific and Technical Aerospace Reports (STAR)' and 'International Aerospace Abstracts (IAA)'. These abstracts are categorized by the following major subject divisions: aeronautics, astronautics, chemistry and materials, engineering, geosciences, life sciences, mathematical and computer sciences, physics, social sciences, space sciences and general. Following the abstract section, seven indexes are provided for further assistance.

Source record↗
NASA NTRS1993

NASA aerospace database subject scope: An overview

Outlined here is the subject scope of the NASA Aerospace Database, a publicly available subset of the NASA Scientific and Technical (STI) Database. Topics of interest to NASA are outlined and placed within the framework of the following broad aerospace subject categories: aeronautics, astronautics, chemistry and materials, engineering, geosciences, life sciences, mathematical and computer sciences, physics, social sciences, space sciences, and general. A brief discussion of the subject scope is given for each broad area, followed by a similar explanation of each of the narrower subject fields that follow. The subject category code is listed for each entry.

Source record↗
NASA NTRS1993

Commonwealth of Independent States aerospace science and technology, 1992: A bibliography with indexes

This bibliography contains 1237 annotated references to reports and journal articles of Commonwealth of Independent States (CIS) intellectual origin entered into the NASA Scientific and Technical Information System during 1992. Representative subject areas include the following: aeronautics, astronautics, chemistry and materials, engineering, geosciences, life sciences, mathematical and computer sciences, physics, social sciences, and space sciences.

Source record↗
NASA NTRS2001

[Activities of ICASE}

This report summarizes research conducted at ICASE in applied mathematics, computer science, fluid mechanics, and structures and material sciences during the period October 1, 2000 through March 31, 2001.

Bushnell, Dennis M.↗
NASA NTRS2002

[Activities of ICASE]

This report summarizes research conducted at ICASE in applied mathematics, computer science, fluid mechanics, and structures and material sciences during the period April 1, 2001 through September 30, 2001.

Bushnell, Dennis M.↗
DOE OSTI2023

High throughput calculations for a dataset of bilayer materials

Bilayer materials made of 2D monolayers are emerging as new systems creating diverse opportunities for basic research and applications in optoelectronics, thermoelectrics, and topological science among others. Herein, we present a computational bilayer materials dataset containing 760 structures with their structural, electronic, and transport properties. Different stacking patterns of each bilayer have been framed by analyzing their monolayer symmetries. Density functional theory calculations including van der Waals interactions are carried out for each stacking pattern to evaluate the corresponding ground states, which are correctly identified for experimentally synthesized transition metal dichalcogenides, graphene, boron nitride, and silicene. Binding energies and interlayer charge transfer are evaluated to analyze the interlayer coupling strength. Our dataset can be used for materials screening and data-assisted modeling for desired thermoelectric or optoelectronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data are scarce and noisy, and monotonicity is supported by strong physical evidence.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Compare predictions of transient fission gas release by empirical and mechanistic models to experiments in high burnup UO 2 fuel

Understanding and predicting fuel performance at high burnup require improving our understanding of transient fission gas release. High-burnup operations enable new mechanisms of fission gas release, which affect fuel performance. The Nuclear Regulatory Commission has recently published its interpretation of existing fuel fragmentation, relocation, and dispersal data in a research information letter. There, transient fission gas release was identified as one of the main factors that contributes to fuel fragmentation, relocation, and dispersal, and therefore limits fuel extension to high burnup. However, transient fission gas release is a complex phenomenon that cannot be fully described by simple empirical descriptions. This report summarizes the development of a mechanistic model for high-burnup transient fission gas release in the fuel performance code BISON. This research was supported by the Nuclear Energy Advanced Modeling and Simulation program during fiscal year 2023 to improve our understanding of high-burnup transient fission gas release and ability to predict it as a function of operation history. To support the development of a mechanistic transient fission gas release model, the existing Simple Integrated Fission Gas Release and Swelling (Sifgrs) model in BISON has been completely refactored to make it more modular and extensible. This effort supports the model's application to high-burnup conditions, its extension to other fuel forms, and the continuous improvement of its current features. Once refactoring was completed, models for high-burnup structure formation, fission gas transfer from non-restructured fuel to high-burnup structure, high-burnup structure intragranular and intergranular fission gas behavior, high-burnup structure bubble evolution, fuel pulverization, and the resulting transient fission gas release were tested and implemented in the Simple Integrated Fission Gas Release and Swelling (Sifgrs) model or tightly coupled to it. The new mechanistic model was then compared to an empirical model developed in parallel by a Nuclear Energy University Program project using a Studsvik high-burnup loss-of-coolant-accident assessment case. Finally, the report details the preliminary BISON results for a benchmark activity organized by the Nuclear Energy Agency to evaluate fuel performance codes' predictive capabilities for burst fission gas release. This work represents an important step toward a mechanistic understanding of fission gas release in high-burnup conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Heterostructural Rocksalt-Wurtzite Interfaces

Data and code accompanying the following publication: Sharad Mahatara and Stephan Lany, "Heterostructural interface engineering for ultrawide gap nitrides from first principles: TaC/AlN and TaC/GaN rocksalt-wurtzite interfaces," Physical Review Applied 22, 054044 (2024). https://doi.org/10.1103/PhysRevApplied.22.054044

36 MATERIALS SCIENCE↗
DOE OSTI2022

CASM — A software package for first-principles based study of multicomponent crystalline solids

CASM is a software package that enables first-principles based studies of crystalline materials. It has been designed to treat coupled chemical, mechanical, vibrational, and magnetic degrees of freedom to determine ground state and finite temperature properties of crystals. The symmetry of the underlying parent crystal structure is used to enumerate perturbations of the parent crystal structure and generate derivative structures which can be input to first-principles calculations in order to explore the ground state energy landscape. CASM algorithmically constructs cluster expansions that fully couple discrete and continuous degrees of freedom, and generates highly efficient code to evaluate the cluster expansion basis functions. Widely used machine learning methods are integrated for fitting expansion coefficients to first-principle calculations. The fully parameterized cluster expansions can be combined with (kinetic) Monte Carlo methods to calculate finite temperature thermodynamic and kinetic properties. CASM Alloy Manager identifies distinct parent crystal structures in an alloy system, creating individual projects for each, and integrating the results. The integrated infrastructure facilitates the linkage between first-principles statistical mechanics predictions with higher length scale computational methods, such as phase field simulations. In conclusion, CASM projects are designed to be easy to share in repositories, to re-use, and to extend to include additional chemical species or types of degrees of freedom.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Coupling CFD and Material Response for Analysis of Mars Entry

In computing the response of an ablating thermal protection system during atmospheric entry, the aerothermal environment and material response are generally computed separately with a blowing correction term in the material response model to account for the blowing of char and pyrolysis gases. In this work, we apply a coupled approach in which pyrolysis blowing gases, computed in the PATO material response code, are used with a blowing boundary condition in the DPLR hypersonic CFD code. This leads to an iterative method in which blowing products from PATO are input into DPLR to update surface heating estimates. The full iterative method, with the addition of radiative heating estimates using the NEQAIR radiation solver, is shown in Fig. 1. The method is demonstrated on a sphere case with the environment and material properties based on the Mars Science Laboratory entry. Future work includes utilizing this method in computing full 3D material response during the Mars 2020 entry.

Ablation↗
NASA NTRSDate not supplied

Coupling CFD and Material Response for Analysis of Mars Entry

In computing the response of an ablating thermal protection system during atmospheric entry, the aerothermal environment and material response are generally computed separately with a blowing correction term in the material response model to account for the blowing of char and pyrolysis gases [1]. In this work, we apply a coupled approach in which pyrolysis blowing gases, computed in the PATO material response code [2], are used with a blowing boundary condition in the DPLR hypersonic CFD code [3]. This leads to an iterative method in which blowing products from PATO are input into DPLR to update surface heating estimates. The full iterative method, with the addition of radiative heating estimates using the NEQAIR radiation solver [4], is shown in Fig. 1. The method is demonstrated on a sphere case with the environment and material properties based on the Mars Science Laboratory entry. Future work includes utilizing this method in computing full 3D material response during the Mars 2020 entry.

Heat Transfer↗
DOE OSTI2025

Quantum Computing Technology Roadmaps and Capability Assessment for Scientific Computing - An analysis of use cases from the NERSC workload

The National Energy Research Scientific Computing Center (NERSC), as the high-performance computing (HPC) facility for the Department of Energy’s Office of Science, recognizes the essential role of quantum computing in its future mission. In this report, we analyze the NERSC workload and identify materials science, quantum chemistry, and high-energy physics as the science domains and application areas that stand to benefit most from quantum computers. These domains jointly make up over 50% of the current NERSC production workload, which is illustrative of the impact quantum computing could have on NERSC’s mission going forward. We perform an extensive literature review and determine the quantum resources required to solve classically intractable problems within these science domains. This review also shows that the quantum resources required have consistently decreased over time due to algorithmic improvements and a deeper understanding of the problems. At the same time, public technology roadmaps from a collection of ten quantum computing companies predict a dramatic increase in capabilities over the next five to ten years. Our analysis reveals a significant overlap emerging in this time frame between the technological capabilities and the algorithmic requirements in these three scientific domains. We anticipate that the execution time of large-scale quantum workflows will become a major performance parameter and propose a simple metric, the Sustained Quantum System Performance (SQSP), to compare system-level performance and throughput for a heterogeneous workload.

97 MATHEMATICS AND COMPUTING↗