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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Confidence-Guided Technique for Tracking Time-Varying Features

Application scientists often employ feature tracking algorithms to capture the temporal evolution of various features in their simulation data. However, as the complexity of the scientific features is increasing with the advanced simulation modeling techniques, quantification of reliability of the feature tracking algorithms is becoming important. One of the desired requirements for any robust feature tracking algorithm is to estimate its confidence during each tracking step so that the results obtained can be interpreted without any ambiguity. To address this, we develop a confidence-guided feature tracking algorithm that allows reliable tracking of user-selected features and presents the tracking dynamics using a graph-based visualization along with the spatial visualization of the tracked feature. Here, the efficacy of the proposed method is demonstrated by applying it to two scientific datasets containing different types of time-varying features.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Current and future directions in network biology

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1994

The 1995 NASA guide to graduate support

The future of the United States is in the classrooms of America and tomorrow's scientific and technological capabilities are derived from today's investments in research. In 1980, NASA initiated the Graduate Student Researchers Program (GSRP) to cultivate additional research ties to the academic community and to support promising students pursuing advanced degrees in science and engineering. Since then, approximately 1300 students have completed the program's requirements. In 1987, the program was expanded to include the Underrepresented Minority and Disabled Focus (UMDF) Component. This program was designed to increase participation of underrepresented groups in graduate study and research and, ultimately, in space science and aerospace technology careers. Approximately 270 minority students have completed the program's requirements while making significant contributions to the nation's aerospace efforts. Continuing to expand fellowship opportunities, NASA announced the Graduate Student Fellowships in Global Change Research in 1990. Designed to support the rapid growth in the study of earth as a system, more than 250 fellowships have been awarded. And, in 1992, NASA announced opportunities in the multiagency High Performance Computing and Communications (HPCC) Program designed to accelerate the development and application of massively parallel processing. Approximately five new fellowships will be awarded yearly. This booklet will guide you in your efforts to participate in programs for graduate student support.

Source record↗
NASA NTRS2023

Trades, Architecture, and Design of the Joint Augmented Reality Visual Informatics (Joint AR) Product

Future expeditions will enable exploration and study of the planetary surfaces of the Moon and Mars by performing extravehicular activity (EVA) operations. Present-day International Space Station (ISS) EVA operations require an intricate and tight choreography of crew, space suits, tools, systems, and flight teams to plan, train, and execute with limited advanced informatics. Additionally, EVA operations, aside from the Apollo Lunar surface missions, have predominately focused on maintenance and construction tasks where success criteria are clearly measurable. However, future exploration missions expect to enable crew to carry out scientific objectives in increasingly Earth-independent ways. In this paper, the Joint Augmented Reality Visual Informatics System (Joint AR) characterizes the design space for developing a modular augmented reality (AR) device for a spacesuit form factor that can support crew decision-making for EVA. This paper highlights the project’s experience with a product-focused management style and use-case centered systems engineering approach to iteratively design, build, and test. The Joint AR product features were defined via trade studies and market analysis of previous EVA display efforts, various AR components such as optics, commercial AR systems, light engines, data interfaces, graphics engine software and analog test beds. We outline the defining architectural design decisions, including safety criticality considerations, suit mounting interfaces, computer architectures, and partnership contracting mechanisms. The outcomes of these studies, architecture decisions, and management requirements result in a recommended design which is the Joint AR product. We discuss the evolution, development of these system components, and what work remains. We hope to share a unified understanding of various design decisions and how they impact the future of crew members’ access to data during Lunar and Martian EVAs. This ongoing effort can enable a community-wide discovery process toward realizing necessary AR features and capabilities for future missions.

Augmented Reality↗
DOE OSTI2019

A roadmap for electronic grade 2D materials

Since their modern debut in 2004, 2-dimensional (2D) materials continue to exhibit scientific and industrial promise, providing a broad materials platform for scientific investigation, and development of nano- and atomic-scale devices. A significant focus of the last decade's research in this field has been 2D semiconductors, whose electronic properties can be tuned through manipulation of dimensionality, substrate engineering, strain, and doping. Molybdenum disulfide (MoS 2 ) and tungsten diselenide (WSe 2 ) have dominated recent interest for potential integration in electronic technologies, due to their intrinsic and tunable properties, atomic-scale thicknesses, and relative ease of stacking to create new and custom structures. Yet, to go 'beyond the bench', advances in large-scale, 2D layer synthesis and engineering must lead to 'exfoliation-quality' 2D layers at the wafer scale. This roadmap aims to address this grand challenge by identifying key technology drivers where 2D layers can have an impact, and to discuss synthesis and layer engineering for the realization of electronic-grade, 2D materials. We center on three fundamental areas of research that must be heavily pursued in both experiment and computation to achieve high-quality materials for electronic and optoelectronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Genesis Data Card Schema, Template and Supporting Tools

Genesis Data Cards provide a standardized template and schema for documenting scientific datasets in support of discovery, access, interoperability, reusability, governed use, and AI usability. This release of the Genesis Data Card repository includes a versioned Markdown template, a LinkML schema with generated Pydantic and JSON artifacts, schema documentation, and example completed data cards. Validation tooling is provided to ensure that completed data cards conform to the schema prior to submission. Accompanying documentation for the structured metadata is provided as a Field Reference Guide. The schema and accompanying template provided in this repository address the call for actionable context that enables humans and AI systems to find, access, interpret, cite, and reuse data, and, when appropriate, integrate it into AI and machine learning workflows. The data card is intended to serve as a common metadata artifact intended to support standardized, cross-program dataset documentation across Department of Energy (DOE)-aligned efforts, including but not limited to Genesis Mission-related implementations, the Office of Science, National Nuclear Security Administration (NNSA), and Advanced Simulation and Computing (ASC) data governance and stewardship initiatives.

data card↗
NASA NTRSDate not supplied

Design of a Radioisotope Heat Exchanger Rocket

The state of planetary exploration vehicle technology has become increasingly more advanced over its long history. Better technologies enable more detailed scientific research to be conducted during the vehicle’s life, while advanced manufacturing strategies allow the vehicles to operate for greater lengths of time. However, one aspect of these vehicles has remained constant: the means of exploration. Previous vehicles have all relied on wheels to traverse the surface of the explored planet, limiting their range. The Mars Opportunity Rover, the longest operating rover, only traveled 45.15 km over its life. The radioisotope heat exchanger rocket is a new vehicle concept, designed to use heated gas as a means of propellant. The vehicle collects gas from the surface of a body, stores it in a heated tank, and passes it through a heat exchanger. This process significantly increases the temperature of the collected gas, allowing it to exit through a nozzle with high velocity and efficiency. This process will enable the vehicle to hop across the surface of the body and repeatedly refuel, with the potential of travelling thousands of kilometers during its lifetime. A computer model was constructed to simulate the heat exchanger and nozzle of this proposed system. This simulation allowed the heat exchanger and propellant tank properties to be optimized for the current mission parameters: the exploration of Triton. The nitrogen flows through a 25 kg lithium heat exchanger, consisting of 540, 1.5-mm diameter, 40-cm long Titanium Zirconium Molybdenum pipes, to three operating nozzles. Once landed, the vehicle will conduct science and refueling operations for six days before hopping again. With the optimized design determined from the simulations conducted in this paper, 235.26 kg of nitrogen is collected and stored in a 300 K, 3000 psia tank. With these parameters, a potential hop distance of 39 km with an average specific impulse of 127.5 s is attained. A previous iteration of this vehicle did not include a heat exchanger, instead heating 100 kg of nitrogen to 300 K, only allowing the vehicle to hop 5 km and with a low specific impulse of 60 s. Thus, the addition of the heat exchanger component of the vehicle is shown to potentially double the specific impulse and quadruple the hop distance compared to the previous design.

Thomas E. O'Brien↗
DOE OSTI2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.

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

Exocortex Network for AI-Augmented Human-Led Scientific Expedition

AI advances in science can be viewed along two main directions with a fluid boundary: enhancing efficiency through automation and smart tools to accelerate tasks that humans can already perform; and enabling exploration into uncharted territories and potentially toward AGI. These advances manifest in the AI cognitive core through the development and explainability of foundation models; in the physical embodiment of instruments and facilities; and in the integrated agency of AI workflows exemplified by the science exocortex. To address the role of humans in this evolving landscape, in this Perspective, we suggest a third direction: the development of personalized agents that form human-centered networks, supporting both efficiency and exploration while ensuring that AI remains aligned with human vision.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Exploring Classification of Topological Priors With Machine Learning for Feature Extraction

In many scientific endeavors, increasingly abstract representations of data allow for new interpretive methodologies and conceptualization of phenomena. For example, moving from raw imaged pixels to segmented and reconstructed objects allows researchers new insights and means to direct their studies toward relevant areas. Thus, the development of new and improved methods for segmentation remains an active area of research. With advances in machine learning and neural networks, scientists have been focused on employing deep neural networks such as U-Net to obtain pixel-level segmentations, namely, defining associations between pixels and corresponding/referent objects and gathering those objects afterward. Topological analysis, such as the use of the Morse-Smale complex to encode regions of uniform gradient flow behavior, offers an alternative approach: first, create geometric priors, and then apply machine learning to classify. This approach is empirically motivated since phenomena of interest often appear as subsets of topological priors in many applications. Using topological elements not only reduces the learning space but also introduces the ability to use learnable geometries and connectivity to aid the classification of the segmentation target. Here, in this article, we describe an approach to creating learnable topological elements, explore the application of ML techniques to classification tasks in a number of areas, and demonstrate this approach as a viable alternative to pixel-level classification, with similar accuracy, improved execution time, and requiring marginal training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Latent space mapping: Revolutionizing predictive models for divertor plasma detachment control

The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster but less accurate empirical methods. This work introduces DivControlNN, a novel machine-learning-based surrogate model designed to address these limitations by enabling quasi-real-time predictions (i.e., ~ 0.2 ms) of boundary and divertor plasma behavior. Trained on over 70,000 2D UEDGE simulations from KSTAR tokamak equilibria, DivControlNN employs latent space mapping to efficiently represent complex divertor plasma states, achieving a computational speed-up of over 10 8 compared to traditional simulations while maintaining a relative error below 20% for key plasma property predictions. During the 2024 KSTAR experimental campaign, a prototype detachment control system powered by DivControlNN successfully demonstrated detachment control on its first attempt, even for a new tungsten divertor configuration and without any fine-tuning. These results highlight the transformative potential of DivControlNN in overcoming diagnostic challenges in future fusion reactors by providing fast, robust, and reliable predictions for advanced integrated control systems.

Artificial neural networks↗
DOE OSTI2023

Probe microscopy is all you need *

We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods. Indeed, the tremendous progress achieved by machine learning (ML) and artificial intelligence over the last decade has been largely achieved via the utilization of static data sets, from the paradigmatic MNIST to the bespoke corpora of text and image data used to train large models such as GPT3, DALL·E and others. However, it is now recognized that continuous, minute improvements to state-of-the-art do not necessarily translate to advances in real-world applications. We argue that a promising pathway for the development of ML methods is via the route of domain-specific deployable algorithms in areas such as electron and scanning probe microscopy and chemical imaging. This will benefit both fundamental physical studies and serve as a test bed for more complex autonomous systems such as robotics and manufacturing. Favorable environment characteristics of scanning and electron microscopy include low risk, extensive availability of domain-specific priors and rewards, relatively small effects of exogenous variables, and often the presence of both upstream first principles as well as downstream learnable physical models for both statics and dynamics. Recent developments in programmable interfaces, edge computing, and access to application programming interfaces (APIs) facilitating microscope control, all render the deployment of ML codes on operational microscopes straightforward. We discuss these considerations and hope that these arguments will lead to create novel set of development targets for the ML community by accelerating both real world ML applications and scientific progress.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Early Inference of Nuclear Technology-Directed Research Activities of Authors from Scientific Publications

Nuclear research articles can provide information about early nuclear proliferation indicators such as influential research entities and technology capability levels of a country, but detection of nuclear activities typically occurs after they have started. We investigate the extent to which nuclear research articles can be used to infer whether a research entity will acquire or develop a nuclear technology before it happens. Early detection of nuclear proliferation or technology development indicators from data is challenging due to partial observability, sparse and unlabeled information, and confounding signals from multiple concurrent activities. This paper presents the early detection problem as a sequential decision-making, goal inference problem, where the objective is to characterize and predict an individual’s, organization’s, or a country’s intent (unobserved goal-directed behavior) towards developing a nuclear capability from partially observed sequences of their research publications, using inverse reinforcement learning and Bayesian goal inference methods. A computational framework is presented, and its application demonstrated using 29,196 Scopus records for a case study related to a civil nuclear capability. The case study results serve as a proof-of-concept demonstration for inference of technology-directed research activity of authors who publish in the nuclear domain. The inference method, combined with advanced computing, may be used to assess and monitor activities pertaining to early developmental stages of a nuclear technology or capability, which in turn can help to identify and prioritize activities with nuclear proliferation potential for further investigation.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

A hierarchical evaluation framework for assessing climate simulations relevant to the energy-water-land nexus (Final Report)

The overarching goal was to construct a hierarchy of new and well-tested metrics and analysis tools that support both fundamental and use-inspired research. Motivating this goal was a convergence of needs from climate scientists and stakeholders alike for a systematic, robust framework of model evaluation and diagnosis to provide scientific insights, inform model development, support best practices for the use of climate model outputs, and facilitate communication of climate information in the evolving landscapes of multi-model, multi-resolution, and large ensemble simulations that generate terabytes of data for any single climate run. Steps toward attaining these goals benefited from expertise and established capability of the project team, which included leadership of the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Downscaling Experiment (CORDEX), development of hierarchical model evaluation approaches, and successful research in the analysis and diagnosis of climate model skill, as well as the understanding and modeling of regional climate processes in North America. As part of the overarching goal, the project worked to disseminate a suite of methodologies, algorithms, and software components that the wider community can employ to advance climate science and applications. With rigorous demonstration, the evaluation framework and the mix of standard and high risk / high reward approaches helped form the basis for future development of a computationally enabled user-friendly system for community use.

17 WIND ENERGY↗
DOE OSTI2018

Advancing graph-based algorithms for predicting flow and transport in fractured rock

Discrete fracture network (DFN) models are a powerful alternative to continuum models for subsurface flow and transport simulations because they explicitly include fracture geometry and network topology, thereby allowing for better characterization of the latter's influence on flow and transport through fractured media. Recent advances in high performance computing have opened the door for flow and transport simulations in large explicit three-dimensional DFN, but this increase in model fidelity and system size comes at a huge computational cost because of the large number of mesh elements required to represent thousands of fractures (with sizes that can range several orders of magnitude, from mm to km). In most subsurface applications, fracture characteristics are only known statistically and numerous realizations are needed to bound uncertainty in flow and transport in the system, thereby exacerbating the computational burden. blackGraphs provide a simple and elegant way to characterize, query, and interrogate fracture network connectivity. We propose a DFN model reduction framework where various graph-representations are used in conjunction with high-fidelity DFN simulations to increase computational efficiency while retaining accuracy of key quantities of interest. The appropriate choice of a graph-representation, namely, which attributes of the DFN are to be represented as nodes and which ones as edges connecting those nodes, depends on the relevant scientific questions. We demonstrate that the proposed DFN model reduction framework provides an efficient means for DFN modeling through both system reduction of the DFN using graph-based properties and combining DFN and graph-based flow and transport simulations.

58 GEOSCIENCES↗
DOE OSTI2025

New Particle Formation and Growth in the Houston Atmosphere During TRACER (Final Report)

From 2020-2025, researchers from UC Irvine, UC Riverside, and Colorado State University collaborated on a Department of Energy-funded project to understand how airborne particles form and grow in urban atmospheres, conducting an intensive field campaign in Houston, Texas during summer 2022. Using advanced instruments to measure gas-phase chemicals, particle composition, and a specialized chamber to study particle growth, the team discovered that sulfur-containing compounds from industrial and power plant emissions are the dominant driver of new particle formation in Houston, with particles typically forming locally in the city and growing as air moves away in the urban plume. The research revealed an important methodological insight: measurements from fixed ground stations can be misleading when interpreting how particles actually evolve as air masses move, which has significant implications for how scientists worldwide interpret atmospheric observations. These findings improve understanding of urban air quality and help reduce uncertainties in climate models, since these particles play critical roles in cloud formation and Earth's radiation balance, while also providing detailed information about ultrafine particle composition relevant to public health. The project trained three doctoral students, developed enhanced computer models for urban particle formation, and made all data publicly available through the DOE Atmospheric Radiation Measurement data archive for use by the broader scientific community.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

OpenNEX: An open collaboration platform for the earth science community

Satellite data from the past several decades provide the most consistent record of land-surface processes that form the basis for scientific assessments of the impacts of climate variations and changes on the environment and human social-economical activities. During this time, scientific research on the characterization and assessment of environmental changes had tended to focus on large-scale land-surface changes with significant social-economic impacts. Increasingly, attention is shifting toward changes that occur more locally and that most directly relate to the everyday life of the majority of the population. In addition, there has been needs to develop management and policy decision support systems that are based on local environmental information. Almost at the same time, the advancement in sensor technology has allowed us to collect an unprecedented volume of environmental data. These data must be curated and analyzed to extract useful information for research and decision support purposes. Established in 2013 and funded by NASA, the Open NASA Earth eXchange (OpenNEX; https://opennex.org/ , Jia et al., 2019 ) project partnered with Amazon Web Services (AWS) to make available a large amount of Earth observing data, modeling results, and analysis tools on the AWS. OpenNEX provides researchers, developers, educators, and ordinary users with easy access to an integrated Earth science computational and data platform, enabling citizen scientists and application developers to realize the full value of NASA data assets and software tools. To encourage the public's engagement in this project, NASA ran virtual workshops and prize competitions. The virtual workshops provided online lectures and tutorials about how prominent scientists used the data in their research and the tutorials gave examples how to use the tools to interrogate the data in the Amazon cloud. Finally, the prize competitions allowed much wider participation in the OpenNEX project and enable testing the non-traditional projects and out-of-box ideas. OpenNEX has continued to evolve and mature. Here, we highlight new features and functionalities available to the community.

Jian Zhang↗
NASA NTRS2003

The Laboratory for Terrestrial Physics

The Laboratory for Terrestrial Physics is dedicated to the advancement of knowledge in Earth and planetary science, by conducting innovative research using space technology. The Laboratory's mission and activities support the work and new initiatives at NASA's Goddard Space Flight Center (GSFC). The Laboratory's success contributes to the Earth Science Directorate as a national resource for studies of Earth from Space. The Laboratory is part of the Earth Science Directorate based at the GSFC in Greenbelt, MD. The Directorate itself is comprised of the Global Change Data Center (GCDC), the Space Data and Computing Division (SDCD), and four science Laboratories, including Laboratory for Terrestrial Physics, Laboratory for Atmospheres, and Laboratory for Hydrospheric Processes all in Greenbelt, MD. The fourth research organization, Goddard Institute for Space Studies (GISS), is in New York, NY. Relevant to NASA's Strategic Plan, the Laboratory ensures that all work undertaken and completed is within the vision of GSFC. The philosophy of the Laboratory is to balance the completion of near term goals, while building on the Laboratory's achievements as a foundation for the scientific challenges in the future.

Source record↗