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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Supportability Technologies for Future Exploration Missions

Future long-duration human exploration missions will be challenged by resupply limitations and mass and volume constraints. Consequently, it will be essential that the logistics footprint required to support these missions be minimized and that capabilities be provided to make them highly autonomous from a logistics perspective. Strategies to achieve these objectives include broad implementation of commonality and standardization at all hardware levels and across all systems, repair of failed hardware at the lowest possible hardware level, and manufacture of structural and mechanical replacement components as needed. Repair at the lowest hardware levels will require the availability of compact, portable systems for diagnosis of failures in electronic systems and verification of system functionality following repair. Rework systems will be required that enable the removal and replacement of microelectronic components with minimal human intervention to minimize skill requirements and training demand for crews. Materials used in the assembly of electronic systems (e.g. solders, fluxes, conformal coatings) must be compatible with the available repair methods and the spacecraft environment. Manufacturing of replacement parts for structural and mechanical applications will require additive manufacturing systems that can generate near-net-shape parts from the range of engineering alloys employed in the spacecraft structure and in the parts utilized in other surface systems. These additive manufacturing processes will need to be supported by real-time non-destructive evaluation during layer-additive processing for on-the-fly quality control. This will provide capabilities for quality control and may serve as an input for closed-loop process control. Additionally, non-destructive methods should be available for material property determination. These nondestructive evaluation processes should be incorporated with the additive manufacturing process - providing an in-process capability to ensure that material deposited during layer-additive processing meets required material property criteria.

Watson, Kevin↗
NASA NTRSDate not supplied

Close out the Critical Commitment for System Wide Safety’s Technical Challenge 3 (TC 3)

- Deliver validated methods and recommended practices to reduce certification costs for ground and onboard vehicle guidance and control systems through expanded acceptability of analysis as evidence for certification FY 2022. - Communicate results to maximize public awareness of the research and transfer capabilities via a set of on-demand training seminars and supporting materials including reports, publications and open source software codes where applicable.

Joseph C Coughlan↗
DOE OSTI2022

Energy efficiency for the growing South Carolina manufacturing industries. Final Report

The overall goals of the proposed project were to establish a DOE Industrial Assessment Center with Clemson University as the main center and to cement the Clemson IAC as the "go to" resource for energy efficiency expertise and assistance to manufacturing businesses in the state of South Carolina. The Clemson IAC included a satellite support center through partnership with the SCE&G Energy Innovation Center at the Clemson University Restoration Institute (CURI) in North Charleston, SC. The Clemson IAC was the first IAC based in the state with the goal to serve the entire state. The Clemson IAC provided SMEs with no-cost energy assessments, trained the next generation of energy-savvy engineers, and provided outreach to non-participating SMEs. Project objectives were structured into five 12-month Budget Periods. In the first Budget Period, the primary objective was to establish methods and materials for Clemson IAC assessments, training, and outreach. In the remaining Budget Periods, the primary objectives were to perform assessments, train students, and pursue outreach to hard-to-reach SMEs that did not participate in energy assessments. Expected outcomes in the first Budget Period included the development of standard procedures for performing energy assessments, including data acquisition, processing, and communication both within the team and in assessment reports prepared for SMEs. Outcomes related to student training in the first Budget Period included detailed curriculum and internship plans for students pursuing the DOE IAC Certificate and energy-related Clemson University certificates. Outcomes in the first Budget Period also included the development of outreach materials and plans for outreach events. In Budget Periods 2 through 5, major expected outcomes are trained students and completed energy assessments and reports for SMEs. Decision points and Go-No Go criteria for the end of each Budget Period, presented in more detail below, are based on the number of students in training and the number of energy assessments performed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction

Deep learning models have proven to be a powerful tool for the prediction of molecular properties for applications including drug design and the development of energy storage materials. However, in order to learn accurate and robust structure–property mappings, these models require large amounts of data which can be a challenge to collect given the time and resource-intensive nature of experimental material characterization efforts. Additionally, such models fail to generalize to new types of molecular structures that were not included in the model training data. The acceleration of material development through uncertainty-guided experimental design has the promise to significantly reduce the data requirements and enable faster generalization to new types of materials. To evaluate the potential of such approaches for electrolyte design applications, we perform comprehensive evaluation of existing uncertainty quantification methods on the prediction of two relevant molecular properties - aqueous solubility and redox potential. We develop novel evaluation methods to probe the utility of the uncertainty estimates for both in-domain and out-of-domain data sets. Finally, we leverage selected uncertainty estimation methods for active learning to evaluate their capacity to support experimental design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Virtual Reality for Shoot/No-Shoot Decision Training in Law Enforcement: A Literature Review and Research Agenda

Virtual reality (VR) can materially improve “shoot / no-shoot” (SNS) training by giving officers realistic, repeatable practice making high-stakes decisions under pressure. Traditional tools—live-fire ranges and video simulators—build basics, but they cannot adapt to each officer in real time or fully mirror the complexity of the field. VR closes that gap by creating immersive scenarios that are safer, more flexible, easier to scale across units, and able to capture objective performance data. SNS decisions are not just about marksmanship; they rely on perception, judgment, memory, and the ability to hold fire when a threat is uncertain. Effective training therefore needs realism, decision complexity, and branching outcomes that reflect the true consequences of choices. These elements strengthen recognition of hostile intent while reducing false positives and building the self-control required in ambiguous situations. VR brings specific advantages: dynamic environments, full-body interaction, and the ability to measure performance with precision—enabling targeted feedback and better transfer of learning to the street. At the same time, responsible deployment must address scenario quality (credible environments and behaviors), lawful decision models, and user wellbeing (appropriate stress levels, comfort, and safety). Sandia’s VIPER Lab is positioned to lead this work. The team combines human-performance science, AI/ML, and VR/AR development with a deep equipment bench (e.g., omnidirectional treadmill, eye-tracking, haptics, multiple HMDs). This ecosystem supports building and validating next-generation SNS training that is immersive, measurable, and trustworthy. Bottom line: Investment in VR-enabled SNS training that blends evidence-based design with careful validation and legal safeguards is expected to pay off in safer, more consistent decision-making and improved community trust, delivered through training that is practical to deploy at scale.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Simplified inelastic constitutive models for ASME Section III, Division 5 design by inelastic analysis

This report describes the development of simplified, universal constitutive model that captures the high temperature monotonic and cyclic behavior of a range of commonly-used high temperature materials. The goal of the work is to provide a simple, universal constitutive model to replace the current bespoke models for Grade 91, 316H, and Alloy 617 included in Nonmandatory Appendix HBB-Z of the ASME Boiler & Pressure Vessel Code, and to extend this model to cover Alloy 800H. We initiated this work in response to feedback from reactor vendors and other Code users requesting simplified models, compared to the current models, that are easier to implement and use in commercial finite element analysis software. This report describes the completion of this effort by developing a model to correct the defects in standard model forms presently used for high temperature material modeling, described in past work, developing and implementing new numerical methods to train this model against test data, and then actually training the model for the four materials. The report provides a complete mathematical description of the model along with the tabulated material coefficients for the four materials. The final step will be to formulate an ASME Code change to introduce the new models into the Code.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Exploring Actinide Nanocrystal Growth towards Defining 5f Surface Chemistry (Final Technical Report)

When particles are very small, down to the nanometer length scale, they display unusual properties not typical of larger materials. Most of the atoms in these structures are at the surface of the particles, which give them different electronic properties. While these unusual properties have been studied in most of the periodic table, little is known about how the electronic properties of the actinides, such as neptunium and plutonium, behave when they become so small. This planned research will study growth pathways of actinide nanoscale particles. Interestingly, the shapes of the particles change depending on the specific actinide species, spanning from thorium through americium, despite having otherwise identical arrangements of atoms. This project will use differences in the shapes and growth pathways of different actinide particles to learn about trends in electronic properties at surfaces across the actinide row. Growth pathways will be determined using a combination of synthesis and the use of advanced X-ray characterization tools. These X-ray characterization tools can provide information about electronic properties and also local bonding characteristics. This project will also inform how to make unprecedented actinide oxide nanoparticles using very small quantities of material. This will enable safe working with radioactive materials and the study of materials that never before have been made into nanoparticles. Meanwhile, students will be trained in working with these exotic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Improving deep learning model performance under parametric constraints for materials informatics applications

Abstract Modern machine learning (ML) and deep learning (DL) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input representations to output properties for a better understanding of the scientific phenomenon. While a deep neural network comprised of fully connected layers has been widely used for materials property prediction, simply creating a deeper model with a large number of layers often faces with vanishing gradient problem, causing a degradation in the performance, thereby limiting usage. In this paper, we study and propose architectural principles to address the question of improving the performance of model training and inference under fixed parametric constraints. Here, we present a general deep-learning framework based on branched residual learning (BRNet) with fully connected layers that can work with any numerical vector-based representation as input to build accurate models to predict materials properties. We perform model training for materials properties using numerical vectors representing different composition-based attributes of the respective materials and compare the performance of the proposed models against traditional ML and existing DL architectures. We find that the proposed models are significantly more accurate than the ML/DL models for all data sizes by using different composition-based attributes as input. Further, branched learning requires fewer parameters and results in faster model training due to better convergence during the training phase than existing neural networks, thereby efficiently building accurate models for predicting materials properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chapter 4: Physically informed deep learning networks for simulating microstructure evolution of 3D polycrystals

As discussed in the previous chapter, high energy diffraction microscopy (HEDM) is used to study the micromechanical evolution of a material during in situ loading. HEDM experiments have been used to verify crystal plasticity (CP) simulations [119, 91, 90, 120], for experimental planning, material design, and to further analyze experimental results. However, Fast Fourier transform-based CP (CP-FFT) or finite element-based CP (CP-FE) methods are often too slow to be used in real-time during an experiment. CP-FFT is faster than CP-FE simulations due to the absence of meshing, but can still take hours to simulate the response of a single volume depending on the size and number of strain steps [127]. Reducing computation time would create a larger exploration space in planning and design, and enable faster analysis of experimental results and real-time feedback during an experiment. This research expands upon previous works to develop a workflow for predicting the full-field evolution of a 3D polycrystal. The workflow is simplified from previous works to predict only orientation and elastic strain tensors (from which stress tensors are calculated). The network is physically informed through loss functions and network architecture for a more robust model. The orientation predictions are informed about the cubic crystal symmetry of the material by incorporating disorientation and misorientation information into the network architecture and loss. The Von Mises stress is used to enforce the correct stress-strain trends in the strain tensor predictions. Additional total strain steps from the elastic and elastoplastic region are included to better capture the stress-strain evolution at smaller total strain steps. Material and hardening parameters are additional inputs into the networks to further inform the network and to study the network’s ability to predict different materials other than those used for training.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Learning constitutive relations using symmetric positive definite neural networks

In this work, we present a new neural-network architecture, called the Cholesky-factored symmetric positive definite neural network (SPD-NN), for modeling constitutive relations in computational mechanics. Instead of directly predicting the stress of the material, the SPD-NN trains a neural network to predict the Cholesky factor of the tangent stiffness matrix, based on which the stress is calculated in incremental form. As a result of this special structure, SPD-NN weakly imposes convexity on the strain energy function, satisfies the second order work criterion (Hill's criterion) and time consistency for path-dependent materials, and therefore improves numerical stability, especially when the SPD-NN is used in finite element simulations. Depending on the types of available data, we propose two training methods, namely direct training for strain and stress pairs and indirect training for loads and displacement pairs. We demonstrate the effectiveness of SPD-NN on hyperelastic, elasto-plastic, and multiscale fiber-reinforced plate problems from solid mechanics. The generality and robustness of SPD-NN make it a promising tool for a wide range of constitutive modeling applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1999

Electron Microscopy and Image Analysis for Selected Materials

This particular project was completed in collaboration with the metallurgical diagnostics facility. The objective of this research had four major components. First, we required training in the operation of the environmental scanning electron microscope (ESEM) for imaging of selected materials including biological specimens. The types of materials range from cyanobacteria and diatoms to cloth, metals, sand, composites and other materials. Second, to obtain training in surface elemental analysis technology using energy dispersive x-ray (EDX) analysis, and in the preparation of x-ray maps of these same materials. Third, to provide training for the staff of the metallurgical diagnostics and failure analysis team in the area of image processing and image analysis technology using NIH Image software. Finally, we were to assist in the sample preparation, observing, imaging, and elemental analysis for Mr. Richard Hoover, one of NASA MSFC's solar physicists and Marshall's principal scientist for the agency-wide virtual Astrobiology Institute. These materials have been collected from various places around the world including the Fox Tunnel in Alaska, Siberia, Antarctica, ice core samples from near Lake Vostoc, thermal vents in the ocean floor, hot springs and many others. We were successful in our efforts to obtain high quality, high resolution images of various materials including selected biological ones. Surface analyses (EDX) and x-ray maps were easily prepared with this technology. We also discovered and used some applications for NIH Image software in the metallurgical diagnostics facility.

Williams, George↗
NASA NTRS2004

Medical/Scientific Illustration And Production Of Otological Health Awareness Materials

Over the past year, I have worked for my mentor, Beth Cooper, on a large variety of projects. Beth is the Manager of the Acoustical Testing Laboratory, which tests the acoustical emissions of payloads destined for the International Space Station. She is also responsible for educating, and developing new methods of educating, people of all occupational and educational backgrounds in hearing conservation. Beth spends much of her time developing new materials and strategies with which to train people and teach other people to train people in hearing conservation and noise emissions control. I have been helping Beth develop and market these materials by way of graphic design and scientific illustration. Last summer, I spent much of my time creating educational illustrations that visually explained particular concepts in Beth's presentations. Sometimes these illustrations were small "comics" while, at other times, they were an instructional series of illustrations. Since then, Beth and her lab have been developing and updating some materials which will be distributed free to hearing conservation and noise control professionals and others in related fields. I have helped with these projects by designing their packaging. In each instance, it was my responsibility to develop an aesthetically appealing package that would also, through its imagery, describe or summarize the contents of the product. I did this for 3 CD's (Auditory Demonstrations 11, MACSUG, and JeopEARdy) and saw them through their actual production and distribution. In addition to working with Beth, I work with the Imaging Technology Center on various imaging projects. Some of my activities include photo retouching and manipulation for videos and print. This summer, I also had the opportunity to develop a screen saver that would show of some of the photography contained on the soon-to-be-released "Highlights of the GRC Image Archives, vol. 2". I was also able to utilize my medical training to help several of ITC s videographers identify the best histological examples of cancerous cells for incorporation in one of their videos. Over the last part of this summer and then throughout the school year, I will be working with Beth to develop a "pre-packaged" lecture series about the physics of acoustics in the context of hearing conservation. These lectures will be used to teach people of all backgrounds the fundamental concepts involved in acoustical physics so they might be better aware of their own and others auditory health in and out of the work place, and, in the case of payload developers, to design and build more quiet science experiments for the ISS. Even though it may not seem as such, this project is precisely what I am learning to do as a student of the Cleveland Institute of Art's Medical Illustration Department. From my perspective, this project is about taking technical information and translating it into terms that anyone, regardless of background, can understand.

Hawes, Nicholas E.↗
DOE OSTI2025

Support for Advanced Fuel Fabrication MC&A Approaches

This report summarizes the key activities of the Material Protection, Accounting, and Control Technologies (MPACT) program’s safeguards training work package for FY25. The work focused on two main areas: domestic safeguards training and the development of new technologies for nuclear material accounting and control (NMAC). A key development during this FY was the delivery of NMAC statistics for US Nuclear Regulatory Commission requirements refresher course. The course objective was to assist industry in meeting the MC&A program requirements for fuel cycle facilities; this training focused on front-end bulk fuel cycle processes. At the same time, a report was prepared on the research output of an effort that focused on the benefits of commercial fuel debundling for NMAC and waste management. The MPACT team evaluated various technologies and proposed a fuel debundling detector that integrates gamma spectroscopy and neutron detection as the most practical solution. The scope of activities under this work package for FY26 focuses on developing a fundamental nuclear material control plan, also known as MC&A plan, for tristructural isotropic (TRISO) fuel fabrication facilities. This plan will address the various NMAC requirements for facilities that handle Category II.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Fast and stable deep-learning predictions of material properties for solid solution alloys

We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between different physical properties in alloy systems to improve the prediction accuracy of neural network (NN) models. We use multitasking NN models to simultaneously predict the total energy, charge density and magnetic moment. These physical properties mutually serve as constraints during the training of the multitasking NN, resulting in more reliable DL models because multiple physics properties are correctly learned by a single model. Two binary alloys, copper–gold (CuAu) and iron–platinum (FePt), were studied. Our results show that once the multitasking NN's are trained, they can estimate the material properties for a specific configuration hundreds of times faster than first-principles density functional theory calculations while retaining comparable accuracy. We used a simple measure based on the root-mean-squared errors to quantify the quality of the NN models, and found that the inclusion of charge density and magnetic moment as physical constraints leads to more stable models that exhibit improved accuracy and reduced uncertainty for the energy predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1983

Polymeric Applications in Electronics

Training manual shows how to use polymeric materials in fabrication electronic components. Report discusses polymeric applications in fabrication of electronic components and equipment. Intended as training manual for 6-day quality assurance and technician certification course in use of polymeric materials.

Read, W. S.↗
DOE OSTI2025

The northeast materials database for magnetic materials

The discovery of magnetic materials with high operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are limited by the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by using Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 67,573 magnetic materials entries (www.nemad.org). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, we trained machine learning models to classify materials and predict transition temperatures. Our classification model achieved an accuracy of 90% in categorizing materials as ferromagnetic (FM), antiferromagnetic (AFM), and non-magnetic (NM). The regression models predict Curie (Néel) temperature with a coefficient of determination (R 2 ) of 0.87 (0.83) and a mean absolute error (MAE) of 56K (38K). These models identified 25 (13) FM (AFM) candidates with a predicted Curie (Néel) temperature above 500K (100K) from the Materials Project. This work shows the feasibility of combining LLMs for automated data extraction and machine learning models to accelerate the discovery of magnetic materials.

Ferromagnetism↗
DOE OSTI2022

Identification of high-dielectric constant compounds from statistical design

Abstract The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69 < ϵ < 101) and large band gaps (2.9 < E g (eV) < 5.5) obtained by screening materials databases using statistical optimization algorithms aided by artificial neural networks (ANN). Two of these new dielectrics are mixed-anion compounds (Eu 5 SiCl 6 O 4 and HoClO) and are shown to be thermodynamically stable against common semiconductors via phase diagram analysis. We also uncovered four other materials with relatively large dielectric constants (20 < ϵ < 40) and band gaps (2.3 < E g (eV) < 2.7). While the ANN training-data are obtained from the Materials Project, the search-space consists of materials from the Open Quantum Materials Database (OQMD)—demonstrating a successful implementation of cross-database materials design. Overall, we report the dielectric properties of 17 materials calculated using ab initio calculations, that were selected in our design workflow. The dielectric materials with high-dielectric properties predicted in this work open up further experimental research opportunities.

36 MATERIALS SCIENCE↗
NASA NTRS2013

Polymers Advance Heat Management Materials for Vehicles

For 6 years prior to the retirement of the Space Shuttle Program, the shuttles carried an onboard repair kit with a tool for emergency use: two tubes of NOAX, or "good goo," as some people called it. NOAX flew on all 22 flights following the Columbia accident, and was designed to repair damage that occurred on the exterior of the shuttle. Bill McMahon, a structural materials engineer at Marshall Space Flight Center says NASA needed a solution for the widest range of possible damage to the shuttle s exterior thermal protection system. "NASA looked at several options in early 2004 and decided on a sealant. Ultimately, NOAX performed the best and was selected," he says. To prove NOAX would work effectively required hundreds of samples manufactured at Marshall and Johnson, and a concerted effort from various NASA field centers. Johnson Space Center provided programmatic leadership, testing, tools, and crew training; Glenn Research Center provided materials analysis; Langley Research Center provided test support and led an effort to perform large patch repairs; Ames Research Center provided additional testing; and Marshall provided further testing and the site of NOAX manufacturing. Although the sealant never had to be used in an emergency situation, it was tested by astronauts on samples of reinforced carbon-carbon (RCC) during two shuttle missions. (RCC is the thermal material on areas of the shuttle that experience the most heat, such as the nose cone and wing leading edges.) The material handled well on orbit, and tests showed the NOAX patch held up well on RCC.

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