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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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Green Propulsion : A NASA GSFC Assessment

In the ever-changing paradigm of efficient and capable spacecraft design, scientific missions continue pushing spacecraft subsystems to deliver effective solutions to meet challenging new mission/spacecraft applications. From an in-space storable liquid chemical propulsion perspective, monopropellant hydrazine is a dependable propellant. Bi-propellant architectures offer even superior performance, but add the complexity of a hypergolic fuel (hydrazine/ mono-methyl hydrazine) and oxidizer (Nitrogen Tetroxide/ mixed oxides of nitrogen) dual tank combination. These propulsion system designs (mono-propellant and bi-propellant) have high heritage, high propellant throughput qualified engines, widely tested material compatibility, qualified fluid delivery commercial-off-the-shelf components, known handling practices, and repeatable performance in successfully delivering on mission requirements. NASA and the broader propulsion community have historically selected hypergolic propellants for most mission applications. The space propulsion community has learned to successfully handle these highly toxic and hazardous materials, navigate the regulated use and the associated safety protocols, personnel protective equipment, and unique training standards – all requisite for loading spacecraft propulsion systems with hypergolic propellants. The question now arises as to what is next for in-space chemical propulsion? Is there an alternative, or even replacement, to the reliable hypergolic fluids, or propellant alternatives that promise increased mission benefits? With the evolution and proven advancements in innovative in-space green propellant technologies capable of delivering benefits to scientific missions, concern over the reliability and infusibility of this higher performing and safer to handle class of propellants is waning. As NASA science missions move forward with the potential flight infusion of green propulsion, NASA and the broader propulsion community are working to address remaining gaps in hardware development, reliability, performance, unique operational considerations, and risk mitigations for high value scientific assets.

Henry W. Mulkey↗
DOE OSTI2024

Machine learning in materials research: Developments over the last decade and challenges for the future

The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials science methods, and ML methods) used within papers that apply ML to materials science. The analysis demonstrates that despite the growth of deep learning techniques, the use of classical machine learning is still dominant as a whole. It also demonstrates how new research can effectively build upon past research, particular in the domain of ML models trained on density functional theory calculation data. Next, I present the progression of best scores as a function of time on the matbench materials science benchmark for formation enthalpy prediction. In particular, a dramatic improvement of 7 times reduction in error is obtained when progressing from feature-based methods that use conventional ML (random forest, support vector regression, etc.) to the use of graph neural network techniques. Finally, I provide views on future challenges and opportunities, focusing on data size and complexity, extrapolation, interpretation, access, and relevance.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Factors Governing Oxygen Vacancy Formation in Oxide Perovskites

The control of oxygen vacancy (V O ) formation is critical to advancing multiple metal-oxide-perovskite-based technologies. In this work, we report the construction of a compact linear model for the neutral V O formation energy in ABO 3 perovskites that reproduces, with reasonable fidelity, Hubbard-U-corrected density functional theory calculations based on the state-of-the-art, strongly constrained and appropriately normed exchange-correlation functional. We obtain a mean absolute error of 0.45 eV for perovskites stable at 298 K, an accuracy that holds across a large, electronically diverse set of ABO 3 perovskites. Our model considers perovskites containing alkaline-earth metals (Ca, Sr, and Ba) and lanthanides (La and Ce) on the A-site and 3d transition metals (Ti, V, Cr, Mn, Fe, Co, and Ni) on the B-site in six different crystal systems (cubic, tetragonal, orthorhombic, hexagonal, rhombohedral, and monoclinic) common to perovskites. Physically intuitive metrics easily extracted from existing experimental thermochemical data or via inexpensive quantum mechanical calculations, including crystal bond dissociation energies and (solid phase) reduction potentials, are key components of the model. Beyond validation of the model against known experimental trends in materials used in solid oxide fuel cells, the model yields new candidate perovskites not contained in our training data set, such as (Bi,Y)(Fe,Co)O 3 , which we predict may have favorable thermochemical water-splitting properties. The confluence of sufficient accuracy, efficiency, and interpretability afforded by our model not only facilitates high-throughput computational screening for any application that requires the precise control of V O concentrations but also provides a clear picture of the dominant physics governing V O formation in metal-oxide perovskites.

08 HYDROGEN↗
DOE OSTI2024

Machine learning assisted search for Fe–Co–C ternary compounds with high magnetic anisotropy

We employ a machine learning (ML)-guided framework to explore rare earth free magnetic materials, specifically focusing on Fe–Co–C ternary compounds for potential use in permanent magnets. Utilizing a specifically trained crystal graph convolutional neural network model, we efficiently screen a vast space of nearly a million substitutional structures to select 620 promising structures for further investigation by first-principles calculation. We predict five low-energy metastable Fe–Co–C compounds with formation energy less than 150 meV/atom above the convex hull. These compounds exhibit high magnetization (Js > 1.0 T) and significant magnetic anisotropy (K1 > 1.0 MJ/m3), making them promising candidates for permanent magnet applications. The phonon calculations indicate these compounds are dynamically stable. Our ML-guided framework demonstrates the utility of rapidly identifying novel materials with tailored magnetic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys

The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.

Chemistry↗
DOE OSTI2022

Learning two-phase microstructure evolution using neural operators and autoencoder architectures

Abstract Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and generalizable surrogate models are needed to alleviate the cost of computationally taxing processes such as in optimization and design of materials. The intrinsic discontinuous nature of the physical phenomena incurred by the presence of sharp phase boundaries makes the training of the surrogate model cumbersome. We develop a framework that integrates a convolutional autoencoder architecture with a deep neural operator (DeepONet) to learn the dynamic evolution of a two-phase mixture and accelerate time-to-solution in predicting the microstructure evolution. We utilize the convolutional autoencoder to provide a compact representation of the microstructure data in a low-dimensional latent space. After DeepONet is trained in the latent space, it can be used to replace the high-fidelity phase-field numerical solver in interpolation tasks or to accelerate the numerical solver in extrapolation tasks.

36 MATERIALS SCIENCE↗
NASA NTRS2002

Fatigue Crack and Porosity Measurement in Composite Materials by Thermographic and Ultrasonic Methods

Many nondestructive methods exist for the detection of localized material anomalies in an otherwise good composite structure. The problem arises when the material system as a whole has degraded during service or was improperly manufactured. Porosity and intra-ply microcracking are two such conditions that in unlined composite pressure vessels can be very troublesome to detect and when linked through the thickness can be critical to mission success. These leak paths may lead to loss of pressure/propellant, increased risk of explosion and possible cryo-pumping. Research sought nondestructive methods for quantifying porosity and microcracking in composite tankage. Both thermographic and resonance ultrasound methods have been utilized with artificial neural network and statistical approaches to analyze the data. Resonant ultrasound spectroscopy provides measurements, which are sensitive to fine details in the materials character, such as micro-cracking and porosity. Here, the higher frequency (shorter wavelength) components of the signal train provide more significant interaction with the defects causing the spectral characteristics to shift toward lower amplitudes at the higher frequencies. As the density of the defects increases more interactions occur and more drastic amplitude changes are observed. From a thermal perspective, the higher the defect density the lower the through thickness thermal diffusivity will be. Utilizing a point heat source, and thermographically recording the heat profile with time, diffusivity calculations can be made which in turn can be related to the relative quality of the material. Preliminary experiments to verify the measurable effect on the resonance spectrum of the ultrasonic data to detect microcracking and for porosity detection thermographically are presented. Methods involving supervised and unsupervised artificial neural networks as well as other clustering algorithms are developed for signal identification.

Walker, James L.↗
NASA NTRS2009

Use of DSC and DMA to Study Rubber Crystallization as a Possible Cause for a Tear in a Neoprene Glove Used in a Space Shuttle Pressurized Astronaut Suit

The Advanced Crew Escape Suit (ACES) is a pressurized suit normally worn by astronauts during launch and landing phases of Space Shuttle operations. In 2008, a large tear (0.5 -1 in. long, between the pinky and ring finger) in the ACES left-hand glove made of neoprene latex rubber was found during training for Shuttle flight STS-124. An investigation to help determine the cause(s) of the glove tear was headed by the NASA Johnson Space Center (JSC) in Houston, Texas. Efforts at JSC to reproduce the actual glove tear pattern by cutting/tearing or rupturing were unsuccessful. Chemical and material property data from JSC such as GC-MS, FTIR, DSC and TGA mostly showed little differences between samples from the torn and control gloves. One possible cause for the glove tear could be a wedding ring/band worn by a male astronaut. Even with a smooth edge, such a ring could scratch the material and initiate the tear observed in the left-hand glove. A decision was later made by JSC to not allow the wearing of such a ring during training or actual flight. Another possible cause for the ACES glove tear is crystallinity induced by strain in the neoprene rubber over a long period of time and use. Neoprene is one several elastomeric materials known to be susceptible to crystallization, and such a process is accelerated with exposure of the material to cold temperatures plus strain. When the temperature is lowered below room temperature, researchers have shown that neoprene crystallization may be maintained at temperatures as high as 45-50 F, with a maximum crystallization rate near 20-25 F (1). A convenient conditioning temperature for inducing neoprene crystallization is a typical freezer that is held near 0 F. For work at the NASA Marshall Space Flight Center (MSFC), samples were cut from several areas/locations (pinky/ring finger crotch, index finger and palm) on each of two pairs of unstrained ACES gloves for DSC and DMA thermal analysis testing. The samples were conditioned in a freezer for various times up to about 14 days. Some rectangular conditioned samples were unstrained, while most were subjected to strains up to 250% with the aid of two slotted aluminum blocks and two aluminum clamps per sample. Trends were observed to correlate DSC data (heat of fusion) and DMA data (linear CTE and stress for iso-strain testing) with: (a) sample location on each glove; and (b) level of strain during conditioning. Control samples cut as is from each glove location were also tested by DSC and DMA.

Wingard, Doug↗
DOE OSTI2024

Initial Alloy 709 constitutive models for use with the ASME design by inelastic analysis and EPP+SMT design methods

This report details a preliminary inelastic constitutive model describing the behavior of Alloy 709. This model will serve two purposes: (1) integration into Nonmandatory Appendix HBB-Z of the ASME Boiler & Pressure Vessel Code Section III, Division 5 and (2) extrapolating cyclic test data to difficult to measure conditions for formulating improved creep-fatigue design methods. For both applications, the model must accurately capture the material behavior across a wide range of temperatures and a variety of test conditions, both monotonic and cyclic. For this purpose we adopt a universal model form under consideration to standardize the description of high temperature constitutive models in the ASME Code. This report briefly restates that model form and how we calibrate the model against the test data, summarizes the test database, and validates the final, trained model by comparison to the experimental tests.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗
DOE OSTI2020

Neural network potential from bispectrum components: A case study on crystalline silicon

In this article, we present a systematic study on developing machine learning force fields (MLFFs) for crystalline silicon. While the main-stream approach of fitting a MLFF is to use a small and localized training set from molecular dynamics simulations, it is unlikely to cover the global features of the potential energy surface. Additionally, to remedy this issue, we used randomly generated symmetrical crystal structures to train a more general Si-MLFF. Furthermore, we performed substantial benchmarks among different choices of material descriptors and regression techniques on two different sets of silicon data. Our results show that neural network potential fitting with bispectrum coefficients as descriptors is a feasible method for obtaining accurate and transferable MLFFs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Neutron Source Facility of the National Science Center “Kharkiv Institute of Physics and Technology” at Kharkiv, Ukraine

Argonne National Laboratory developed, designed, and supported the construction of a state-of-the-art Neutron Source Facility (NSF) at the Kharkov Institute of Physics and Technology (KIPT) in Kharkov Ukraine, under the U.S. DOE NNSA Russian Research Reactor Fuel Return (RRRFR) program. Ukraine approved the return of all highly enriched uranium in the country to Russia prior to the 2012 Nuclear Security Summit in return for the RRRFR program to fund the development, the design, and the construction of the NSF. The facility is designed to produce medical isotopes, train nuclear professionals, support the Ukrainian nuclear industry, and provide experimental capabilities for performing reactor physics, materials, and basic science research. The NSF was successfully started and operated in August 2021. The NSF is the first facility of this type in the world, and it will be used to understand the physics of driven systems for energy production and the disposal of spent nuclear fuels. The NSF consists of a subcritical assembly using low enriched uranium (LEU) fuel driven with an electron accelerator. The NSF target design utilizes tungsten or natural uranium for producing neutrons through photonuclear reactions using 100-MeV electrons. The accelerator power is 100 KW, which produces 3x10 14 neutrons per second from the uranium target. The subcritical assembly is designed to obtain the highest possible neutron flux intensity with an effective neutron multiplication factor of less than 0.98. Passive safety, reliability, and environmental considerations were included in the NSF design. The NSF utilization study shows that this neutron source has the capability of producing different medical isotopes. Several horizontal neutron channels are incorporated for performing applied and basic research. The NSF is configured to accommodate future design upgrades and new missions. The facility design was approved by the Ukraine Academy of Sciences before starting the NSF construction. The construction, the equipment installation, and the operation of the different systems of the NSF were completed and approved by the Ukrainian regulators. In July 2020, the State Nuclear Regulatory Inspectorate of Ukraine (SNRIU) issued a license for the physical startup of the NSF. The construction of facility was completed in early 2021.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1989

Human factors issues in performing life science experiments in a 0-G environment

An overview of the environmental conditions within the Spacelab and the planned Space Station Freedom is presented. How this environment causes specific Human Factors problems and the nature of design solutions are described. The impact of these problems and solutions on the performance of life science activities onboard Spacelab (SL) and Space Station Freedom (SSF) is discussed. The first area highlighted is contamination. The permanence of SSF in contrast to the two-week mission of SL has significant impacts on crew and specimen protection requirements and, thus, resource utilization. These requirements, in turn impose restrictions on working volumes, scheduling, training, and scope of experimental procedures. A second area is microgravity. This means that all specimens, materials, and apparatus must be restrained and carefully controlled. Because so much of the scientific activity must occur within restricted enclosures (gloveboxes), the provisions for restraint and control are made more complex. The third topic is crewmember biomechanics and the problems of movement and task performance in microgravity. In addition to the need to stabilize the body for the performance of tasks, performance of very sensitive tasks such as dissection is difficult. The issue of space sickness and adaption is considered in this context.

Gonzalez, Wayne↗
NASA NTRS1991

NDE research efforts at the FAA Center for Aviation Systems Reliability

The Center for Aviation Systems Reliability (FAA-CASR), a part of the Institute for Physical Research and Technology at Iowa State University, began operation in the Fall of 1990 with funding from the FAA. The mission of the FAA-CASR is to develop quantitative nondestructive evaluation (NDE) methods for aircraft structures and materials including prototype instrumentation, software, techniques, and procedures and to develop and maintain comprehensive education and training programs in aviation specific inspection procedures and practices. To accomplish this mission, FAA-CASR brings together resources from universities, government, and industry to develop a comprehensive approach to problems specific to the aviation industry. The problem areas are targeted by the FAA, aviation manufacturers, the airline industry and other members of the aviation business community. This consortium approach ensures that the focus of the efforts is on relevant problems and also facilitates effective transfer of the results to industry.

Aging Aircraft↗
NASA NTRS2003

Using the Chandra Project to Communicate with Underdeveloped Constituencies

We have instituted a program whose goal is to better communicate astronomy among students selected from an economically underprivileged area of Huntsville, Alabama. To achieve this goal, six local teachers were trained as astronomy teacher resource agents. These teachers were teamed with six scientists; the teams presented materials for 29 teachers, mostly from the Southeastern United States Topics included the electromagnetic spectrum, X-ray optics, gratings and spectroscopy, supernovae (and remnants), black holes, and the tools and techniques to analyze Chandra X-ray data. Beginning in September our teacher-scientist teams, in collaboration with the Huntsville Housting Authority, will present programs to specially selected students at the rate of one or two per month, until April 2004. These young people will be mentored and encouraged to develop a research project. Students, which complete a project by April 2, will be paid for their work.

Adams, M. L.↗
NASA NTRS2004

A Bootstrap Approach to Martian Manufacturing

In-Situ Resource Utilization (ISRU) is an essential element of any affordable strategy for a sustained human presence on Mars. Ideally, Martian habitats would be extremely massive to allow plenty of room to comfortably live and work, as well as to protect the occupants from the environment. Moreover, transportation and power generation systems would also require significant mass if affordable. For our approach to ISRU, we use the industrialization of the U.S. as a metaphor. The 19th century started with small blacksmith shops and ended with massive steel mills primarily accomplished by blacksmiths increasing their production capacity and product size to create larger shops, which produced small mills, which produced the large steel mills that industrialized the country. Most of the mass of a steel mill is comprised of steel in simple shapes, which are produced and repaired with few pieces of equipment also mostly made of steel in basic shapes. Due to this simplicity, we expect that the 19th century manufacturing growth can be repeated on Mars in the 21st century using robots as the primary labor force. We suggest a "bootstrap" approach to manufacturing on Mars that uses a "seed" manufacturing system that uses regolith to create major structural components and spare parts. The regolith would be melted, foamed, and sintered as needed to fabricate parts using casting and solid freeform fabrication techniques. Complex components, such as electronics, would be brought from Earth and integrated as needed. These parts would be assembled to create additional manufacturing systems, which can be both more capable and higher capacity. These subsequent manufacturing systems could refine vast amounts of raw materials to create large components, as well as assemble equipment, habitats, pressure vessels, cranes, pipelines, railways, trains, power generation stations, and other facilities needed to economically maintain a sustained human presence on Mars.

Dorais, Gregory A.↗
NASA NTRS2019

AladynPi – Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application

This report provides an overview and description of commands used in the Computational Materials mini-application, AladynPi. AladynPi is an extension of a previously released mini-application, Aladyn (https://github.com/nasa/aladyn; Yamakov, V.I., and Glaessgen, E.H., NASA/TM-2018-220104). Aladyn and AladynPi are basic molecular dynamics codes written in FORTRAN 2003, which are designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory method. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. In Aladyn, the ANN gives directly the energy of interatomic interactions. In AladynPi, the ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). The parameterized BOP function is then used to calculate the energy. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing manycore architectures based on graphic processing units (GPUs). The effort is supported by the High Performance Computing incubator (HPCi) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗