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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Visualization Within the Department of Energy: NREL IEEE VIS Application Spotlight

This presentation highlights the role of advanced visualization techniques at the National Renewable Energy Laboratory (NREL) in supporting cutting-edge research across diverse energy domains. From immersive analytics and uncertainty visualization to high-resolution and real-time data analysis, NREL's visualization capabilities enable scientists to explore complex datasets more effectively. These tools are critical for advancing research in materials science, renewable energy technologies, biofuels, electric vehicle infrastructure, energy efficiency - from industrial processes to entire communities - and then bringing these innovations to practice through energy systems integration. NREL's visualization tools drive innovation across renewable energy and grid modernization efforts by providing deeper insights and improving decision-making.

grid modernization↗
DOE OSTI2024

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics–Informed Neural Networks

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data-driven models for learning full-field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics-informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. Furthermore, the improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Design, Control and Application of Next Generation Qubits

Design, Control and Application of Next Generation Qubits Arun Bansil, Northeastern University (Principal Investigator) Claudio Chamon, Boston University (Co-Investigator) Adrian Feiguin, Northeastern University (Co-Investigator) Liang Fu, MIT (Co-Investigator) Eduardo Mucciolo, Univ. of Central Florida (Co-Investigator) Qimin Yan, Temple University (Co-Investigator) The quest for developing technologies for manipulating and storing information quantum mechanically is currently led by approaches that include Josephson-junctions, ion-traps, and qubits generated by defect spins in solids. Topological qubits, however, are inherently more robust to decoherence by environmental effects, and should be able to sprint ahead once practical barriers have been overcome. At the present stage of the development of the field, it is important to explore a variety of architectures and materials beyond the conventional paradigms in order to seed breakthroughs toward building a scalable quantum computer. Our comprehensive theoretical research program involved four interconnected thrusts as follows. • A materials discovery effort in two-dimensional compounds in search of materials to support Majorana zero modes and defect structures suitable as qubits. • Exploration of architectures for topological quantum computation by investigating both superconducting Majorana qubits, and robust platforms for braiding with new “meta-materials” built of arrays of Majorana qubits. • Investigation of properties of hybrid metal-organic qubits based on transition-metal centers in graphene, and molecular crystals of polyaromatic complexes with embedded transition-metal atoms. • Development of tensor-network and semiclassical approaches to study decoherence in the presence of random and dispersive spin baths, and NV centers in diamond. The full spectrum of theoretical and numerical approaches was used to address the goals of this project including first-principles, density-matrix-renormalization group, tensor networks, and data-driven high-throughput approaches using materials database and machine-learning.

36 MATERIALS SCIENCE↗
NASA NTRS2012

NASA Thesaurus Data File

The NASA Thesaurus contains the authorized NASA subject terms used to index and retrieve materials in the NASA Aeronautics and Space Database (NA&SD) and NASA Technical Reports Server (NTRS). The scope of this controlled vocabulary includes not only aerospace engineering, but all supporting areas of engineering and physics, the natural space sciences (astronomy, astrophysics, planetary science), Earth sciences, and the biological sciences. The NASA Thesaurus Data File contains all valid terms and hierarchical relationships, USE references, and related terms in machine-readable form. The Data File is available in the following formats: RDF/SKOS, RDF/OWL, ZThes-1.0, and CSV/TXT.

Source record↗
NASA NTRS2002

Onboard Processor for Compressing HSI Data

With EO-1 Hyperion and MightySat in orbit NASA and the DoD are showing their continued commitment to hyperspectral imaging (HSI). As HSI sensor technology continues to mature, the ever-increasing amounts of sensor data generated will result in a need for more cost effective communication and data handling systems. Lockheed Martin, with considerable experience in spacecraft design and developing special purpose onboard processors, has teamed with Applied Signal & Image Technology (ASIT), who has an extensive heritage in HSI, to develop a real-time and intelligent onboard processing (OBP) system to reduce HSI sensor downlink requirements. Our goal is to reduce the downlink requirement by a factor greater than 100, while retaining the necessary spectral fidelity of the sensor data needed to satisfy the many science, military, and intelligence goals of these systems. Our initial spectral compression experiments leverage commercial-off-the-shelf (COTS) spectral exploitation algorithms for segmentation, material identification and spectral compression that ASIT has developed. ASIT will also support the modification and integration of this COTS software into the OBP. Other commercially available COTS software for spatial compression will also be employed as part of the overall compression processing sequence. Over the next year elements of a high-performance reconfigurable OBP will be developed to implement proven preprocessing steps that distill the HSI data stream in both spectral and spatial dimensions. The system will intelligently reduce the volume of data that must be stored, transmitted to the ground, and processed while minimizing the loss of information.

Cook, Sid↗
DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Uncertainty-Driven Rapid Thermodynamic Assessment of Nb-Ta-Zr System and Effects of C impurities (L25GF9298S): Annual Progress Report

An integrated computational materials engineering (ICME) method is in development for rapid thermodynamic experimental investigation and high-fidelity computational modeling of refractory multi-principal element alloys (RMPEAs). These ultra-high-temperature (UHT) alloys are of interest for structural applications in extreme environments, but deficiency of reliable data, especially melting temperatures, impedes the prediction of alloys with favorable properties. The method leverages UHT capabilities and computational expertise of LLNL’s Materials Science Division and the McCormack Lab’s UHT conical nozzle levitation (CNL) system to iteratively map the Nb-Ta-Zr phase space, with focus on the liquidus surface, through targeted experiments selected by quantifying uncertainty in the thermodynamic model fitting parameters. This method will reduce the time to map uncharted RMPEA phase space and thereby accelerate discovery and development of advanced materials for applications in extreme environments.

36 MATERIALS SCIENCE↗
NASA NTRS1990

A flammability study of thin plastic film materials

The Materials Science Laboratory at the Kennedy Space Center presently conducts flammability tests on thin plastic film materials by using a small needle rake method. Flammability data from twenty-two thin plastic film materials were obtained and cross-checked by using three different testing methods: (1) the presently used small needle rake; (2) the newly developed large needle rake; and (3) the previously used frame. In order to better discern the melting-burning phenomenon of thin plastic film material, five additional specific experiments were performed. These experiments determined the following: (1) the heat sink effect of each testing method; (2) the effect of the burn angle on the burn length or melting/shrinkage length; (3) the temperature profile above the ignition source; (4) the melting point and the fire point of each material; and (5) the melting/burning profile of each material via infrared (IR) imaging. The results of these experimentations are presented.

Skinner, S. Ballou↗
NASA NTRSDate not supplied

Field Tests With Trident Drill in Bishop Tuff Help Prepare for Future Missions to Moon and Mars

We performed drilling in volcanic deposits near Bishop California using an engineering model of the Honeybee Robotics TRIDENT (The Regolith and Ice Drill for Exploration of New Terrains) drill [1] a rotary percussive 1-meter class drill that is carried on the PRIME1and VIPER (Volatiles Investigating Polar Exploration Rover)[2] missions that launch in 2024. A similar drilling system was planned for the proposed Icebreaker Discovery class mission to Mars [3] and the Mars Life Explorer mission recommended by the 2020 Decadal Survey of planetary science [4]. The objectives of the project were (1) to use data collected by the drill for operational purposes as a probe of subsurface material properties in formations that are analogous to those that may be encountered on planetary surfaces; (2) correlate subsurface structures with those deduced from Ground Penetrating Radar (GPR); and (3) inspect the boreholes after they were drilled to test PERISCOPE (Probe for Exploring Regolith and Ice by Subsurface Classification of Organics, polycyclic aromatic hydrocarbons (PAHs), and Elements), a newly developed downhole UV fluorescence spectrometer [5].

Carol R. Stoker↗
NASA NTRS2001

Space Science Payloads Optical Properties Monitor (OPM) Mission Flight Anomalies Thermal Analyses

The OPM was the first space payload that measured in-situ the optical properties of materials and had data telemetered to ground. The OPM was EVA mounted to the Mir Docking Module for an eight-month stay where flight samples were exposed to the Mir induced and natural environments. The OPM was comprised of three optical instruments; a total hemispherical spectral reflectometer, a vacuum ultraviolet spectrometer, and a total integrated scatterometer. There were also three environmental monitors; an atomic oxygen monitor, solar and infrared radiometers, and two temperature-controlled quartz crystal microbalances (to monitor contamination). Measurements were performed weekly and data telemetered to ground through the Mir data system. This paper will describe the OPM thermal control design and how the thermal math models were used to analyze anomalies which occurred during the space flight mission.

Schmitz, Craig P.↗
DOE OSTI2024

Feature Based Qualification (FBQ) of Wire Arc Additively Manufactured (WAAM) 17-4PH Martensitic Stainless Steels

The Department of Defense (DOD) programs of records desire to reduce the time and cost of the development and delivery loop in metal additive manufacturing (AM), including establishing forwarded AM capabilities. The success of these efforts relies on a robust and qualified process. To achieve this, the United States Army Combat Capabilities Development Command Ground Vehicle Systems Center Materials Engineering (GVME) needs to be able to quickly evaluate, test, and develop feedstocks, processes, and parts. This report is directed towards demonstrating the need for a framework for metal AM processes, defining and exploring geometries for metal AM process qualification, testing resultant deposition, and delivering actionable data.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

The Road to Endurance: Outcomes From the Endurance Science Workshop

Endurance is a mission concept for a long-range, highly autonomous lunar rover (Fig. 1) designed to explore the farside of the Moon and the largest and oldest (undisputed) impact crater in the Solar System: South Pole–Aitken basin (SPA) (Fig. 2). Endurance would traverse nearly 2,000 kilometers, acquiring a large volume of in situ data, and a large mass of samples (up to 100 kg of material from 12 sites), before rendezvousing with Artemis astronauts at the south pole of the Moon. The Endurance concept was developed during the recent Origins, Worlds, and Life Planetary Science and Astrobiology Decadal Survey [1–2], which recommended Endurance as the highest priority new strategic mission for NASA’s Lunar Discovery and Exploration Program. Since the release of the Decadal Survey in 2022, NASA has been further studying this concept, and investigating the range of implementation options [3–5].

J. T. Keane↗
NASA NTRS2022

Science Objectives for Flagship-Class Mission Concepts for the Search for Evidence of Life at Enceladus

Cassini revealed that Saturn's Moon Enceladus hosts a subsurface ocean that meets the accepted criteria for habitability with bio-essential elements and compounds, liquid water, and energy sources available in the environment. Whether these conditions are sufficiently abundant and collocated to support life remains unknown and cannot be determined from Cassini data. However, thanks to the plume of oceanic material emanating from Enceladus’ south pole, a new mission to Enceladus could search for evidence of life without having to descend through kilometers of ice. In this article, we outline the science motivations for such a successor to Cassini, choosing the primary science goal to be determining whether Enceladus is inhabited and assuming a resource level equivalent to NASA's Flagship-class missions. We selected a set of potential biosignature measurements that are complementary and orthogonal to build a robust case for any life detection result. This result would be further informed by quantifications of the habitability of the environment through geochemical and geophysical investigations into the ocean and ice shell crust. This study demonstrates that Enceladus’ plume offers an unparalleled opportunity for in situ exploration of an Ocean World and that the planetary science and astrobiology community is well equipped to take full advantage of it in the coming decades.

Enceladus↗
NASA NTRS1994

The 1994 research and technology report at the Goddard Space Flight Center

The breadth of subject material in this 1994 edition of the Research and Technology Report illustrates the broad scope of activities at the Goddard Space Flight Center. The numerous entries dealing with data processing and visualization show the strong emphasis on data and its interpretation. Reports are presented in the following sections: data processing and visualization; space sciences - high energy astronomy, solar system, and new techniques; earth system science - atmospheres, oceans and ice, solid earth, and soils and vegetation; networks, planning, and information systems - mission scheduling and operations, spacecraft operation and status, software engineering, and infrastructure support; engineering and materials - spacecraft subsystems, launch vehicles, thermal control, new mechanisms, and testing and evaluation; and flight projects.

Soffen, Gerald↗
NASA NTRSDate not supplied

Simulating Ordinary Chondrite (Tamdakht) Ablation at the Hypersonic Materials Environmental Test System (HyMETS) Facility.

NASA’s Science Mission Directorate (SMD) created the Asteroid Threat Assessment Project (ATAP) to inform decision-makers of risks associated with Potentially Hazardous Objects (PHOs), which may pose an existential threat to human civilization. ATAP assesses risk, in part, by developing analytical physics-based damage models which draw upon data collected during material property characterization efforts, entry simulations, hazard simulations, and ground-based arc-jet testing. Therefore, a pathfinder test campaign was con-ducted at the Hypersonic Materials Environmental Test System (HyMETS) at the NASA Langley Re-search Center to investigate the ablation mechanisms of an ordinary chondrite (Tamdakht H5) and a terrestrial analog (basalt). The HyMETS facility is a 400 kW constricted arc heater used to screen material performance under simulated aerothermal conditions of hypersonic flow.1 Facility conditions were chosen to simulate Earth entry conditions be-tween the upper-mesosphere and the lower thermo-sphere. The ablation mechanisms of Tamdakht and bas-alt are illustrated with still images collected from high-speed video cameras (fig. 1, Tamdakht). Tamdakht produces a relatively stable melt flow, as evidenced by the perpetual growth of a flange at the sidewall and the exiguous detachment of molten material into the flow. Furthermore, an inspection of high-speed video and chemical analysis of the post-test melt layer suggests that the dominant mode of mass loss for Tamdakht, at these test conditions, is expressed through the vaporization of volatiles (e.g., iron, potassium, sodium, phosphorus, and sulfur). The vaporization rates of Tamdakht are greater than basalt under the most extreme test conditions, which leads to an enhanced blowing layer, in-creased thermal shielding, and reduced recession. The ablation mechanisms of basalt are markedly different from Tamdakht and are attributed to the presence of hydrated minerals dispersed at irregular intervals throughout the silicate matrix. Rapid de-composition of secondary minerals and the subsequent formation of water lead to the ejection of the subsurface and overlying melt layer. Furthermore, the presence of water is suspected of lowering the viscosity of the melt layer resulting in increased mass loss near the edge of the test article where drag forces overcome rheological properties (fig. 1, bas-alt). Finally, the results will be discussed in the broader context of meteorite-based material response model development and the potential impact on the Asteroid Threat Assessment Project (ATAP).

Brody K. Bessire↗
DOE OSTI2025

Micrometer: Micromechanics transformer for predicting full field mechanical responses of heterogeneous materials

Predicting mechanical responses of heterogeneous materials across scales remains a significant challenge. Traditional computational methods often struggle with complex and multiscale nature of these materials, limiting their effectiveness in real-world applications. Here, in this paper, we introduce Micrometer, a vision transformer based deep learning model designed to predict full field mechanical responses of heterogeneous materials, bridging the gap between computer vision and solid mechanics problems. We show that Micrometer, trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of material properties and loading conditions. Our model demonstrates accuracy and computational efficiency in applications such as computational homogenization and multiscale modeling, reducing computational time by up to two orders of magnitude compared to conventional numerical solvers while maintaining less than 1 % errors in predicting macroscale stress fields. Furthermore, we showcase Micrometer’s adaptability through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in computational solid mechanics. These results represent a significant step towards AI-driven innovation in materials science, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Composite materials↗