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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Microgravity research opportunities for the 1990s

The Committee on Microgravity Research (CMGR) was made a standing committee of the Space Studies Board (SSB) and charged with developing a long-range research strategy. The scientific disciplines contained within the microgravity program, and covered in this report, include fluid mechanics and transport phenomena, combustion, biological sciences and biotechnology, materials science, and microgravity physics. The purpose of this report is to recommend means to accomplish the goal of advancing science and technology in each of the component disciplines. Microgravity research should be aimed at making significant impacts in each discipline emphasized. The conclusions and recommendations presented in this report fall into five categories: (1) overall goals for the microgravity research program; (2) general priorities among the major scientific disciplines affected by gravity; (3) identification of the more promising experimental challenges and opportunities within each discipline; (4) general scientific recommendations that apply to all microgravity-related disciplines; and (5) recommendations concerning administrative policies and procedures that are essential to the conduct of excellent laboratory science.

Source record↗
NASA NTRS1998

Introduction of International Microgravity Strategic Planning Group

Established in May 6, 1995, the purpose of this International Strategic Planning Group for Microgravity Science and Applications Research is to develop and update, at least on a biennial basis, an International Strategic Plan for Microgravity Science and Applications Research. The member space agencies have agreed to contribute to the development of a Strategic Plan, and seek the implementation of the cooperative programs defined in this Plan. The emphasis of this plan is the coordination of hardware construction and utilization within the various areas of research including biotechnology, combustion science, fluid physics, materials science and other special topics in physical sciences. The Microgravity Science and Applications International Strategic Plan is a joint effort by the present members - ASI, CNES, CSA, DLR, ESA, NASA, and NASDA. It represents the consensus from a series of discussions held within the International Microgravity Strategic Planning Group (IMSPG). In 1996 several space agencies initiated multilateral discussions on how to improve the effectiveness of international microgravity research during the upcoming Space Station era. These discussions led to a recognition of the need for a comprehensive strategic plan for international microgravity research that would provide a framework for cooperation between international agencies. The Strategic Plan is intended to provide a basis for inter-agency coordination and cooperation in microgravity research in the environment of the International Space Station (ISS) era. This will be accomplished through analysis of the interests and goals of each participating agency and identification of mutual interests and program compatibilities. The Plan provides a framework for maximizing the productivity of space-based research for the benefit of our societies.

Rhome, Robert↗
NASA NTRS2009

International Space Station: A System of Systems

The ISS will be discussed from inception to date. What the different partners have contributed, the systems they have been responsible for, how we've been able to integrate those different systems into one cohesive International Space Station. And in the process supporting sophisticated research in weightlessness, accommodating thousands of experiments in life sciences, fluid physics, material sciences and a host of other disciplines. The presentation will start with an overview of KSC, then move into an explanation of ISS in detail, spending time in how all the pieces have come together. Will discuss years spent designing, testing, manufacturing and integrating the different elements throughout the different sites and shipped to the United States at NASA's Kennedy Space Center (KSC) from where it was planned to be launched in the Space Shuttle. A brief Constellation Overview will be provided as well.

Nunez, Jose↗
NASA NTRS2015

Investigations of Physical Processes in Microgravity Relevant to Space Electrochemical Power Systems

NASA has performed physical science microgravity flight experiments in the areas of combustion science, fluid physics, material science and fundamental physics research on the International Space Station (ISS) since 2001. The orbital conditions on the ISS provide an environment where gravity driven phenomena, such as buoyant convection, are nearly negligible. Gravity strongly affects fluid behavior by creating forces that drive motion, shape phase boundaries and compress gases. The need for a better understanding of fluid physics has created a vigorous, multidisciplinary research community whose ongoing vitality is marked by the continuous emergence of new fields in both basic and applied science. In particular, the low-gravity environment offers a unique opportunity for the study of fluid physics and transport phenomena that are very relevant to management of fluid - gas separations in fuel cell and electrolysis systems. Experiments conducted in space have yielded rich results. These results provided valuable insights into fundamental fluid and gas phase behavior that apply to space environments and could not be observed in Earth-based labs. As an example, recent capillary flow results have discovered both an unexpected sensitivity to symmetric geometries associated with fluid container shape, and identified key regime maps for design of corner or wedge-shaped passive gas-liquid phase separators. In this presentation we will also briefly review some of physical science related to flight experiments, such as boiling, that have applicability to electrochemical systems, along with ground-based (drop tower, low gravity aircraft) microgravity electrochemical research. These same buoyancy and interfacial phenomena effects will apply to electrochemical power and energy storage systems that perform two-phase separation, such as water-oxygen separation in life support electrolysis, and primary space power generation devices such as passive primary fuel cell.

energy storage↗
NASA NTRS2001

The Science of Detached Bridgman Growth and Solutocapillary Convection in Solid Solution Crystals

Bridgman and Float-zone crystal growth experiments are planned for NASA's First Materials Science Research Rack using the European Space Agency's Materials Science Laboratory with the Low Gradient Furnace (LGF) and Float Zone Furnace with Rotating Magnetic Field (FMF) inserts, respectively. Samples will include germanium and germanium-silicon alloys with up to 10 atomic percent silicon. The Bridgman part of the investigation includes detached growth samples and so there will be a solid-liquid-gas tri-junction in those experiments just as there will be in all float-zone experiments. There are other similarities as well as significant differences between the types of growth that will be discussed. The presentation will call attention to the reasons that experiments in microgravity will provide information unattainable from Earth-based experiments.

Szofran, F. R.↗
NASA NTRS2000

Microgravity Disturbance Characterization of the Quench Module Insert (QMI) Phase Change Device (PCD)

The Materials Science Research Facility (MSRF) is a multi-user, multi-purpose facility for materials science research. One experiment within the MSRF will be the Quench Module Insert (QMI), a high-temperature furnace with unique capabilities for processing different classes of materials. The primary functions of the QMI furnace are to melt, directionally solidify, and quench metallic samples, providing data to aid in understanding the effects of the microgravity environment on the characteristics of these processed metals. The QMI houses sealed individual sample ampoules containing material to be processed. Quenching of the samples in the QMI furnace is accomplished by releasing low-melting-point metallic shoes into contact with the outside of the sample ampoule, dissipating heat and cooling the sample inside. The impact from this method of quench will induce sample vibrations which could be large enough to adversely affect sample quality. Utilizing breadboard hardware, the sample quench sequence, releasing the shoes, was conducted. Data was collected from accelerometers located on the breadboard sample cartridge, indicating the maximum acceleration achieved by the sample. The primary objective of the test described in this presentation was to determine the acceleration imparted on the sample by the shoe contact. From this information, the science community can better assess whether this method of quench will allow them to obtain the data they need.

Gattis, Christy↗
DOE OSTI2026

Freeze It or Leave It? Evaluating the Role of Cryo-Electron Microscopy in Battery Research

Cryogenic electron microscopy (cryo-EM) continues to gain prominence in materials science, particularly in battery research where it has enabled high-resolution, multimodal characterization of electrode materials and interfaces that otherwise degrade quickly under electron beam irradiation. But as anyone who has attempted cryo-EM techniques knows, freezing comes at a cost; cryo-EM experiments are time-consuming, highly sensitive, and carry an increased risk of artifacts due to issues such as frost contamination. Thus, when planning new characterization of battery materials or other beam-sensitive samples, it is critical to consider whether (and which) cryo-EM techniques are appropriate, based on study goals and an understanding of electron beam-sample interactions. Here we review such considerations for battery materials to elucidate the questions of when, why, and how to freeze to achieve high-quality characterization.

25 ENERGY STORAGE↗
DOE OSTI2022

On the frontiers of coupled extreme environments

Coupled extreme environments pose among the highest demands on materials, stressing the limits of temperature, pressure, electric and magnetic fields, atomic displacement rates, and chemical potentials that can be simultaneously sustained. Here, this issue highlights exciting new materials science methods that will enable rapid progress in what has traditionally been the most difficult materials arena. New opportunities in artificial intelligence, multi-physics simulations capabilities, the use of extreme conditions to synthesize novel materials, and our ability to interrogate materials under relevant conditions provide new avenues to understand and design new materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Moving closer to experimental level materials property prediction using AI

Abstract While experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, experiments are expensive and DFT-computations are time-consuming and have significant discrepancies against experiments. Currently, predictive modeling based on DFT-computations have provided a rapid screening method for materials candidates for further DFT-computations and experiments; however, such models inherit the large discrepancies from the DFT-based training data. Here, we demonstrate how AI can be leveraged together with DFT to compute materials properties more accurately than DFT itself by focusing on the critical materials science task of predicting “formation energy of a material given its structure and composition”. On an experimental hold-out test set containing 137 entries, AI can predict formation energy from materials structure and composition with a mean absolute error (MAE) of 0.064 eV/atom; comparing this against DFT-computations, we find that AI can significantly outperform DFT computations for the same task (discrepancies of $$>0.076$$ > 0.076 eV/atom) for the first time.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electrical control of magnetism by electric field and current-induced torques

The remanent magnetization of ferromagnets has long been studied and used to store binary information. While early magnetic memory designs relied on magnetization switching by locally generated magnetic fields, key insights in condensed matter physics later suggested the possibility of doing it by electrical means instead. In the 1990s, Slonczewski and Berger formulated the concept of current-induced spin torques in magnetic multilayers through which a spin-polarized current generated by a first ferromagnet may be used to switch the magnetization of a second one. This discovery drove the development of spin-transfer-torque magnetic random-access memories (MRAMs). More recent fundamental research revealed other types of current-induced torques named spin-orbit torques (SOTs) and will lead to a new generation of devices including SOT MRAMs and skyrmion-based devices. Parallel to these advances, multiferroics and their magnetoelectric coupling, first investigated experimentally in the 1960s, experienced a renaissance. Dozens of multiferroic compounds with new magnetoelectric coupling mechanisms were discovered and high-quality multiferroic films were synthesized (notably of BiFeO 3 ), also leading to novel device concepts for information and communication technology such as the magnetoelectric spin-orbit (MESO) transistor. The story of the electrical switching of magnetization, which is discussed in this review, is that of a dance between fundamental research (in spintronics, condensed matter physics, and materials science) and technology (MRAMs, MESO transistors, microwave emitters, spin diodes, skyrmion-based devices, components for neuromorphics, etc.). This pas de deux has led to major scientific and technological breakthroughs in recent decades (such as the conceptualization of pure spin currents, the observation of magnetic skyrmions, and the discovery of spin-charge interconversion effects). As a result, this field has not only propelled MRAMs into consumer electronics products but also fueled discoveries in adjacent research areas such as ferroelectrics or magnonics. Here, in this review, recent advances in the control of magnetism by electric fields and by current-induced torques are covered. Fundamental concepts in these two directions are reviewed first, their combination is then discussed, and finally current various families of devices harnessing the electrical control of magnetic properties for various application fields are addressed. The review concludes by giving perspectives in terms of both emerging fundamental physics concepts and new directions in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning-based microstructure prediction during laser sintering of alumina

Abstract Predicting material’s microstructure under new processing conditions is essential in advanced manufacturing and materials science. This is because the material’s microstructure hugely influences the material’s properties. We demonstrate an elegant machine learning algorithm that faithfully predicts the microstructure under new conditions, without the need of knowing the governing laws. We name this algorithm, RCWGAN-GP, which is regression-based conditional generative adversarial networks with Wasserstein loss function and gradient penalty. This algorithm was trained with experimental SEM micrographs from laser-sintered alumina under various laser powers. The RCWGAN-GP realistically regenerates the SEM micrographs under the trained laser powers. Impressively, it also faithfully predicts the alumina’s microstructure under unexplored laser powers. The predicted microstructure features, including the morphology of the sintered particles and the pores, match the experimental SEM micrographs very well. We further quantitatively examined the prediction accuracy of the RCWGAN-GP. We trained the algorithm with computer-created micrograph datasets of secondary-phase growth governed by the well-known Johnson–Mehl–Avrami (JMA) equation. The RCWGAN-GP accurately regenerates the micrographs at the trained time series, in terms of the grains’ shapes, sizes, and spatial distributions. More importantly, the predicted secondary phase fraction accurately follows the JMA curve.

08 HYDROGEN↗
DOE OSTI2025

Materials Research at Idaho National Laboratory

I am going to spent 8 mins to introduce materials science related research at INL to the student and early career researchers who attend MRS Falls 2025. I am one of 10 panelists, all of whom are from different national labs.

36 - MATERIALS SCIENCE↗
NASA NTRS1992

NASA-UVA light aerospace alloy and structures technology program (LA2ST)

The NASA-UVa Light Aerospace Alloy and Structure Technology (LAST) Program continues to maintain a high level of activity, with projects being conducted by graduate students and faculty advisors in the Departments of Materials Science and Engineering, Civil Engineering and Applied Mechanics, and Mechanical and Aerospace Engineering at the University of Virginia. This work is funded by the NASA-Langley Research Center under Grant NAG-1-745. Here, we report on progress achieved between January 1 and June 30, 1992. The objectives of the LA2ST Program is to conduct interdisciplinary graduate student research on the performance of the next generation, light weight aerospace alloys, composites and thermal gradient structures in collaboration with Langley researchers. Technical objectives are established for each research project. We aim to produce relevant data and basic understanding of material mechanical response, corrosion behavior, and microstructure; new monolithic and composite alloys; advanced processing methods; new solid and fluid mechanics analyses; measurement advances; and critically, a pool of educated graduate students for aerospace technologies. The accomplishments presented in this report cover topics including: (1) Mechanical and Environmental Degradation Mechanisms in Advance Light Metals and Composites; (2) Aerospace Materials Science; (3) Mechanics of Materials and Composites for Aerospace Structures; and (4) Thermal Gradient Structures.

Gangloff, Richard P.↗
DOE OSTI2025

Keeping LAMMPS cutting edge

Since its inception 30 years ago, LAMMPS has grown to be a world-class molecular dynamics code and a cornerstone of computational materials science research. This project aimed to keep LAMMPS at the forefront of molecular dynamics simulations by adapting LAMMPS to the latest developments in machine learning technology and hardware. Initially, the project set out to provide a unified implementation of active learning for efficient training data generation in LAMMPS, but the research trajectory pivoted to address more immediate and impactful opportunities. On the hardware side, recent record-breaking molecular dynamics simulations were developed on the Cerebras wafer-scale AI chip, and this project has developed an interface between LAMMPS and the hardware-specific molecular dynamics code to accelerate and simplify development and user adoption. On the software side, PyTorch’s Ahead-of-Time (AOT) compilation features promised increased performance for state-of-the-art equivariant neural network potentials, and this project laid the groundwork for their adoption in LAMMPS, resulting in a nearly 20x acceleration in extreme cases. Combined with a comprehensive benchmark study of LAMMPS across all current exascale systems, this project has reinforced LAMMPS’s role as a versatile, high-performance tool for current and future materials science applications.

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↗
DOE OSTI2022

Exploiting Machine Learning in Multiscale Modelling of Materials

Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials Science and Engineering pose a formidable challenge. The present scenario of machine learning research in Materials Science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘black-box’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed.

42 ENGINEERING↗
DOE OSTI2020

Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys

The development of machine learning (ML) approaches in materials science offers the opportunity to exploit existing engineering and developmental alloy datasets, such as Oak Ridge National Laboratory (ORNL)’s consistently measured creep-rupture dataset for alumina-forming austenitic (AFA) alloys, to accelerate their further development. As a first step toward achieving ML insights for improved alloy design, the potential sources of uncertainty and their impacts on ML output are examined. It is observed that the selection of algorithms and features as well as data sampling significantly affects the performance of ML models, either positively or negatively. Further, the performance of various ML models in predicting the creep properties of AFA alloys is compared, with further evaluation by assessment of a small set of new developmental AFA alloys that were not part of the training dataset. The present study demonstrates that uncertainty quantification (UQ) is essential in materials science for evaluating the performance of ML algorithms with specifically selected feature sets and obtaining a comprehensive understanding of their limitations and the resultant capability of effective prediction in complex materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗