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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Intricate short-range order in GeSn alloys revealed by atomistic simulations with highly accurate and efficient machine-learning potentials

GeSn alloys hold promise for silicon-compatible integrated applications in electronics, photonics, and topological quantum devices. However, understanding their intricate structures using density functional theory (DFT) calculations is hindered by spatiotemporal constraints. To overcome this limitation, we develop highly accurate and efficient machine-learning interatomic potentials based on a neuroevolution potential approach with farthest point sampling on a comprehensive DFT data set. The application of the developed machine-learning potential in large-scale atomistic simulations bridges the spatiotemporal gap between modeling and advanced characterizations, and facilitates the discovery of structural intricacies in GeSn alloys. Through extensive statistical sampling, we identify a type of short-range order (SRO) that is distinguished by both its structural signature and electronic band gap from the SRO structure previously predicted. Modeling based on a large simulation cell reveals the coexistence of nano SRO domains with various degrees of ordering, demonstrating a complex spatial heterogeneity of SRO structure. Finally, our study not only reinforces the significance of fine-level structural information in alloys, but it also constitutes an effective framework for exploring SRO in a broad range of complex alloys based on highly accurate and effective machine-learning potentials.

36 MATERIALS SCIENCE↗
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↗
NASA NTRS1995

Emergence of Relations and the Essence of Learning: A Review of Sidman's Equivalence Relations and Behavior: A Research Story

Sidman addresses two very important questions in Equivalence Relations and Behavior: A Research Story: What are the bases of behavioral competence? And how do units of learning become related? The book recounts the story of how an understanding of emergent relations and competencies was achieved through studies in his teaching-research program with mentally retarded subjects. Although children normally accrue vast networks of relations between stimuli and events, those with mental retardation typically do not. Consequently, by learning how to establish those networks, Sidman and his students contribute richly both to the cultivation of competencies by their subjects and, more generally, to an understanding of real-world human behavior. The basic equivalence paradigm affords the subject feedback and reinforcement for very specific choices during training, but the test is not for those choices! Rather, tests for equivalence look for new choices, ones seemingly quite foreign to the training regimen. The tests for equivalence relations entail presentations of stimuli that were the options for conditional choice during reinforced training. In tests of equivalence, correct choices are novel; hence, they have never been reinforced during training. The study of equivalence relations can encourage the emergence of new perspectives that are more symbiotic than competitive. In full acknowledgment of the important role and contributions made by those who identify themselves as experimental analysts of behavior, it is timely that rapprochements be worked toward, as indeed they are, to meld that perspective with others of our time. Both our research methods and our expectations about the nature of the learning process and the abilities of our subjects can delimit what they might learn and what we, in turn, learn about their learning. The text will be of great value for instruction at the upper-division and graduate levels. Its impact will be substantial, for it defines an important advance in our efforts to understand the richness of behavior in both humans and nonhuman animals. Although not presented to that end, the book might also serve to bridge communications with other groups of animal researchers whose interests lie more in a comparative or ethological framework.

Rumbaugh, Duane M.↗
DOE OSTI2026

Statistical and Machine Learning Approaches to Analyzing Pipeline Incidents in the United States (2010–2024)

This study applies machine learning methods to analyze natural gas pipeline incidents in the United States using the Pipeline and Hazardous Materials Safety Administration (PHMSA) Gas Distribution Incident Dataset (2010–2024). The dataset includes over 600 variables describing incident characteristics, infrastructure attributes, and contributing factors associated with unintentional gas releases. The objective is to assess whether these features can reliably predict the underlying cause of pipeline failures. Multinomial logistic regression and Random Forest models were developed to classify incident causes, including excavation damage, corrosion, equipment failure, and natural forces. Results show that excavation damage is both the most frequent and most predictable cause, with models achieving strong performance for this category. However, when excavation damage is excluded, model accuracy declines significantly, with some models performing near random levels. Across all approaches, severe class imbalance and limited variability in key predictors constrain predictive performance. Pipeline age and diameter emerge as the most influential variables, but they provide insufficient discriminatory power to distinguish among less frequent failure types. These findings indicate that non-excavation-related incidents are rare, heterogeneous, and weakly represented in the dataset, limiting the effectiveness of machine learning classification. Overall, this study highlights the structural limitations of the PHMSA dataset for predictive modeling and underscores the need for improved data balance and feature enrichment. The results reinforce excavation damage prevention as the most impactful strategy for reducing pipeline incidents.

03 NATURAL GAS↗
NASA NTRS1992

Thermal conductance of two interface materials and their applications in space systems

The temperature control of spacecraft and instrument systems and subsystems requires heat transfer interface materials that possess good thermal and structural characteristics, among other properties, to respond to the vacuum environment of space. These materials must be easy to apply to, and remove from, the surfaces where they are applied, and must be able to withstand power dissipation extremes, and be used for different clamping configurations and pressures. Silicone based greases, used in the past, tend to migrate and to contaminate nearby surfaces. Bare metal to metal contact offers low thermal conductance and difficulties in estimating the actual heat transfer. Several polymeric materials containing different thermal conductive compounds and structural reinforcements were prepared to overcome grease and metal problems. Two polymeric materials were evaluated: Cho-Therm 1671 elastomer; and the CV-2946, a conductive RTV silicone. Tests were done to learn more about these products. Results indicate that the tightly bolted, torqued fixtures did not buckle or distort, and provided optimum thermal conductance. Fixtures simulating actual spacecraft configuration suffered bowing and separating.

Scialdone, J. J.↗
NASA NTRS1999

Wilberforce Power Technology in Education Program

The Wilberforce Power Technology in Education Program is a multipart program. Three key parts of this program will be described. They are: (1) WISE-The Wilberforce Summer Intensive Experience. This annual offering is an educational program which is designed to provide both background reinforcement and a focus on study skills to give the participants a boost in their academic performance throughout their academic careers. It is offered to entering Wilberforce students. Those students who take advantage of WISE learn to improve important skills which enable them to work at higher levels in mathematics, science and engineering courses throughout their college careers, but most notably in the first year of college study. (2) Apply technology to reaming. This is being done in several ways including creating an electronic chemistry text with hypertext links to a glossary to help the students deal with the large new vocabulary required to describe and understand chemistry. It is also being done by converting lecture materials for the Biochemistry class to PowerPoint format. Technology is also being applied to learning by exploring simulation software of scientific instrumentation. (3) Wilberforce participation in collaborative research with NASA's John H. Glenn Research Center at Lewis Field. This research has focused on two areas in the past year. The first of these is the deposition of solar cell materials. A second area involves the development of polymeric materials for incorporation into thin film batteries.

Gordon, Edward M.↗
NASA NTRS2002

Structural Acoustic Response of a Shape Memory Alloy Hybrid Composite Panel (Lessons Learned)

This study presents results from an effort to fabricate a shape memory alloy hybrid composite (SMAHC) panel specimen and test the structure for dynamic response and noise transmission characteristics under the action of thermal and random acoustic loads. A method for fabricating a SMAHC laminate with bi-directional SMA reinforcement is described. Glass-epoxy unidirectional prepreg tape and Nitinol ribbon comprise the material system. Thermal activation of the Nitinol actuators was achieved through resistive heating. The experimental hardware required for mechanical support of the panel/actuators and for establishing convenient electrical connectivity to the actuators is presented. Other experimental apparatus necessary for controlling the panel temperature and acquiring structural acoustic data are also described. Deficiency in the thermal control system was discovered in the process of performing the elevated temperature tests. Discussion of the experimental results focuses on determining the causes for the deficiency and establishing means for rectifying the problem.

Turner, Travis L.↗
DOE OSTI2024

Machine Learning-Driven Quantification of CO2 Plume Dynamics at Illinois Basin Decatur Project Sites Using Microseismic Data

This study utilizes machine learning to quantify CO2 plume extents by analyzing microseismic data from the Illinois Basin Decatur Project (IBDP). Leveraging a unique dataset of well logs, microseismic records, and CO2 injection metrics, this work aims to predict the temporal evolution of subsurface CO2 saturation plumes. The findings illustrate that machine learning can predict plume dynamics, revealing vertical clustering of microseismic events over distinct time periods within certain proximities to the injection well, consistent with an invasion percolation model. The buoyant CO2 plume partially trapped within sandstone intervals periodically breaches localized barriers or baffles, which act as leaky seals and impede vertical migration until buoyancy overcomes gravity and capillary forces, leading to breakthroughs along vertical zones of weakness. Between different unsupervised clustering techniques, K-Means and DBSCAN were applied and analyzed in detail, where K-means outperformed DBSCAN in this specific study by indicating the combination of the highest Silhouette Score and the lowest Davies–Bouldin Index. The predictive capability of machine learning models in quantifying CO2 saturation plume extension is significant for real-time monitoring and management of CO2 sequestration sites. The models exhibit high accuracy, validated against physical models and injection data from the IBDP, reinforcing the viability of CO2 geological sequestration as a climate change mitigation strategy and enhancing advanced tools for safe management of these operations.

Iyegbekedo, Ikponmwosa↗
NASA NTRS2019

Demisability of GFRP and CFRP Components of Reentering Orbital Debris: Phase I Test Results

Observations of surviving reentry debris on the ground and research performed by Hyperschall Technologie Göttingen (HTG) [1] indicated that significantly more glass fiber-reinforced polymer (GFRP) and carbon fiber-reinforced polymer (CFRP) components survive reentry than current models predict. NASA’s Orbital Debris Program Office conducted a series of tests to evaluate the accuracy of material demise models for reentering orbital debris used in NASA’s Object Reentry Survival Analysis Tool (ORSAT) and Debris Assessment Software (DAS). Testing is planned in a multi-phase series to allow for quick quantification of results as well as refinement of methods resulting from lessons learned during early phases. The Phase 1 tests discussed here validated ORSAT models for homogeneous metals, provided an efficient quantification of composite material demisability properties like mass loss rate and overall time to demise, and identified potential failure modes, which are currently not well understood. Phase 2 tests will be used to further understand mass loss rates and modes of both thermal and mechanical failure in composite materials. The authors exposed 95 samples of aluminum, CFRP, Kevlar fiber-reinforced polymer, GFRP, and sheets of G10 fiberglass to conditions approximating the reentry environment using an inductively coupled plasma (ICP) torch facility. The cylindrical CFRP samples were exposed to the atmospheric pressure plasma, at both the end and the midpoint, to investigate the difference in demisability between parts with exposed edges, like panels, and parts with no edges, such as carbonoverwrapped pressure vessels (COPVs). In a non-oxidative environment, no composite materials demised within the 5-minute test time. In the oxidative, elevated heat flux environment, CFRP samples demised between 210 s and 270 s. For the first 100 s of insertion time, most of the mass loss was due to pyrolysis of resin, creating an approximately bi-linear mass-loss rate curve with time. In a non-oxidative environment, carbon filaments were observed to unravel from some of the CFRP end-burned samples; however, this effect did not seem to affect the overall time to demise for the samples significantly. These results indicate that both GFRP and CFRP components survive reentry with significantly more remaining mass than current models predict.

Greene, Benton R.↗
NASA NTRS2007

Digital Learning Network Education Events for the Desert Research and Technology Studies

NASA s Digital Learning Network (DLN) reaches out to thousands of students each year through video conferencing and webcasting. As part of NASA s Strategic Plan to reach the next generation of space explorers, the DLN develops and delivers educational programs that reinforce principles in the areas of science, technology, engineering and mathematics. The DLN has created a series of live education videoconferences connecting the Desert Research and Technology Studies (RATS) field test to students across the United States. The programs are also extended to students around the world via live webcasting. The primary focus of the events is the Vision for Space Exploration. During the programs, Desert RATS engineers and scientists inform and inspire students about the importance of exploration and share the importance of the field test as it correlates with plans to return to the Moon and explore Mars. This paper describes the events that took place in September 2006.

Paul, Heather L.↗
NASA NTRS1988

Biomedical support of man in space

In its broadest sense, biomedical support of man in space must not be limited to assisting spacecraft crew during the mission; such support should also ensure that flight personnel be able to perform properly during landing and after leaving the craft. Man has developed mechanisms that allow him to cope with specific stresses in his normal habitat; there is indisputable evidence that, in some cases, the space environment, by relieving these stresses, has also allowed the adaptive mechanisms to lapse, causing serious problems after re-entry. Inflight biomedical support must therefore include means to simulate some of the normal stresses of the Earth environment. In the area of cardiovascular performance, we have come to rely heavily on complex feedback mechanisms to cope with two stresses, often combined: postural changes, which alter the body axis along which gravitational acceleration acts, and physical exercise, which increases the total load on the system. Unless the appropriate responses are reinforced continuously during flight, crew members may be incapacitated upon return. The first step in the support process must be a study of the way in which changes in g, even of short duration, affect these responses. In particular we should learn more about effects of g on the "on" and "off" dynamics, using a variety of approaches: increased acceleration on one hand at recumbency, immersion, lower body positive pressure, and other means of simulating some of the effects of low g, on the other. Once we understand this, we will have to determine the minimal exposure dose required to maintain the response mechanisms. Finally, we shall have to design stresses that simulate Earth environment and can be imposed in the space vehicle. Some of the information is already at hand; we know that several aspects of the response to exercise are affected by posture. Results from a current series of studies on the kinetics of tilt and on the dynamics of readjustment to exercise in different postures will be presented and discussed.

NASA Discipline Cardiopulmonary↗
NASA NTRS1988

SiO overcoating and polishing of CFRP telescope panels

Development of carbon fiber reinforces plastic (CFRP) panel overcoating and polishing is structured in two parts. The first part utilized a short series of experiments to determine the feasibility of overcoating and polishing CFRP panels, and the second part employes a systematic approach to optimize techniques learned. Questions which required answers in the initial investigation are summarized. Tests were performedin the Steward Observatory's 2.2 Meter Vacuum Coating Chamber and began with 3 cm square pieces of CFRP facesheet material. Next, a 10 cm square and one-inch-thick CFPR-Aluminum core panel was tested. Tests were then conducted on a 0.5-meter-square Dornier panel (QUAD 4) with CFRP facesheets on two-inch aluminum Flexcore. To complete the initial study, a previously characterized 0.5 m Dornier panel (QUAD 23) was coated and hand polished. The mirror's optical performance was not affected by the SiO coating.

Woida, Patrick↗
NASA NTRS1995

Indicator Systems and Evaluation

Participants in the workshop session were actively engaged in a hands-on, minds-on approach to learning about indicators and evaluation processes. The six hour session was broken down into three two hour sessions. Each session was built upon an instructional model which moved from general understanding to specific IITA application. Examples and practice exercises served to demonstrate tand reinforce the workshop concepts. Each successive session built upon the previous session and addressed the major steps in the evaluation process. The major steps covered in the workshop included: project descriptions, writing goals and objectives for categories, determining indicators and indicator systems for specific projects, and methods and issues of data collection. The workshop served as a baseline upon which the field centers will build during the summer in undertaking a comprehensive examination and evaluation of their existing K-12 education projects.

Canright, Shelley↗
NASA NTRS1996

Video-task acquisition in rhesus monkeys (Macaca mulatta) and chimpanzees (Pan troglodytes): a comparative analysis

This study describes video-task acquisition in two nonhuman primate species. The subjects were seven rhesus monkeys (Macaca mulatta) and seven chimpanzees (Pan troglodytes). All subjects were trained to manipulate a joystick which controlled a cursor displayed on a computer monitor. Two criterion levels were used: one based on conceptual knowledge of the task and one based on motor performance. Chimpanzees and rhesus monkeys attained criterion in a comparable number of trials using a conceptually based criterion. However, using a criterion based on motor performance, chimpanzees reached criterion significantly faster than rhesus monkeys. Analysis of error patterns and latency indicated that the rhesus monkeys had a larger asymmetry in response bias and were significantly slower in responding than the chimpanzees. The results are discussed in terms of the relation between object manipulation skills and video-task acquisition.

NASA Discipline Space Human Factors↗
NASA NTRS1996

Toward a New Outlook on Primate Learning and Behavior: Complex Learning and Emergent Processes in Comparative Perspective

Primate research of the 20th century has established the validity of Darwin's postulation of psychological as well as biological continuity between humans and other primates, notably the great apes. Its data make clear that Descartes' view of animals as unfeeling 'beast-machines' is invalid and should be discarded. Traditional behavioristic frameworks, that emphasize the concepts of stimulus, response, and reinforcement and an 'empty-organism' psychology, are in need of major revisions. Revised frameworks should incorporate the fact that, in contrast to the lifeless databases of the 'hard' sciences, the database of psychology entails properties novel to life and its attendant phenomena. The contributions of research this century, achieved by field and laboratory researchers from around the world, have been substantial, indeed revolutionary. It is time to celebrate the progress of our field, to anticipate its significance, and to emphasize conservation of primates in their natural habitats.

Rumbaugh, Duane M.↗
DOE OSTI2025

VA EDH Advanced Software Pipeline Framework Report: Enhancing Automation and Scalability

The VA Environmental Determinants of Health (EDH) Advanced Software Pipeline Framework is designed to enhance the efficiency, scalability, and security of geospatial data processing workflows. This framework integrates modern data orchestration and containerization technologies, including Prefect for workflow automation, Docker for containerization, and PostgreSQL/PostGIS for geospatial data storage and analysis. It ensures standardized, reproducible, and automated data processing, supporting VA objectives related to substance use risk assessment and recovery research. The pipeline addresses key scalability and performance challenges through horizontal and vertical scaling, high-performance computing (HPC) integration, parallel processing, task caching, and dynamic resource allocation. These optimizations improve throughput and reduce latency, allowing the system to efficiently manage large and complex datasets. Additionally, security and compliance measures—such as data encryption (SSL), Role-Based Access Control (RBAC), and adherence to GDPR and HIPAA standards—safeguard sensitive information throughout data transmission and storage. A key implementation of this framework includes the automation of shelter list geolocation workflows, ensuring that up-to-date data is readily available for VA decision-making. Lessons learned from this project include the transition from in-memory processing to incremental storage writes, improving resource management and reliability. Future enhancements aim to expand automation, integrate AI-driven anomaly detection, and incorporate high-performance computing resources. This framework provides a scalable, secure, and adaptable solution for managing geospatial datasets, reinforcing the VA’s ability to support clinical and strategic initiatives through data-driven decision-making.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Statistical Classification of Biosignature Information using Multiple Instrument Observations

The accurate identification of biosignatures (indications of life) from data taken from remote or in situ planetary exploration is one of the most important challenges in astrobiology, the interdisciplinary field examining habitability and the potential for extraterrestrial life. This study employs machine learning algorithms to optimize the identification of biosignatures, with an emphasis on those which are agnostic to a specific biochemical basis. We exploit the wealth of terrestrial data available from biogenic and abiogenic systems to enhance efficient feature prioritization. Our dataset, pulled from public databases and laboratory recorded measurements, includes elemental abundance, isotopic fractionation, and VNIR/Raman spectra The data curation process included standardization for detection limits and ranges. Subsequent feature extraction yielded detailed inputs for machine learning, including combinations of elemental content, isotopic ratios, and parameters of spectral peaks and troughs. Feature significance was evaluated across diverse machine learning methodologies, such as k-nearest neighbors, logistic regression, Random Forest, support vector machines, and Gaussian Naïve Bayes, along with a combined voting classifier. We utilized Receiver Operating Characteristic Area Under the Curve (ROC AUC) across 2,000 50% test-train splits as a robust metric of model performance. Results revealed a promising ROC AUC of 0.853 for the combined voting classifier. Removing elemental abundance data notably reduced model accuracy (13% decrease in AUC), highlighting its critical role in biosignature detection. Several other individual data features exhibited significance within their respective data types, offering additional granularity. This research fortifies the relevance of machine learning to astrobiology, potentially enhancing life detection missions by allowing algorithmic prioritization of high-interest samples for further investigation. Future work will refine data standardization, expand the dataset to include more terrestrial systems, and incorporate convolutional neural networks for spectral feature extraction. The potential for public data sharing is also under exploration, reinforcing our commitment to collective scientific advancement.

Statistical↗
DOE OSTI2024

Speeding up and reducing memory usage for scientific machine learning via mixed precision

Scientific machine learning (SciML) has emerged as a versatile approach to address complex computational science and engineering problems. Within this field, physics-informed neural networks (PINNs) and deep operator networks (DeepONets) stand out as the leading techniques for solving partial differential equations by incorporating both physical equations and experimental data. However, training PINNs and DeepONets require significant computational resources, including long computational times and large amounts of memory. In search of computational efficiency, training neural networks using half precision (float16) rather than the conventional single (float32) or double (float64) precision has gained substantial interest, given the inherent benefits of reduced computational time and memory consumed. However, we find that float16 cannot be applied to SciML methods, because of gradient divergence at the start of training, weight updates going to zero, and the inability to converge to a local minima. To overcome these limitations, we explore mixed precision, which is an approach that combines the float16 and float32 numerical formats to reduce memory usage and increase computational speed. Our experiments showcase that mixed precision training not only substantially decreases training times and memory demands but also maintains model accuracy. Here, we also reinforce our empirical observations with a theoretical analysis. The research has broad implications for SciML in various computational applications.

97 MATHEMATICS AND COMPUTING↗