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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Wind Tunnel Testing of the NASA-DFRC Flutterometer using a Two DOF Wing Section

Flutter of an aeroelastic structure is potentially destructive aeroelastic instability. This phenomenon has motivated research within the aeroelastic community to develop methods that can accurately predict aeroelastic instabilities. The Flutterometer method used herein, and as developed by NASA DFRC, is based upon the mu method which has been coupled with wavelet filtering processes in estimating aeroelastic models from flight data. The approach leads to a methodology to predict the occurrence of flutter boundaries, and may prove to reliably predict flutter boundaries during flight tests. An analytical model is used as the first estimate of the aeroelastic structural dynamics, and uncertainty operators are introduced into the system to model variations between the theoretical system and the physical system. The modelling uncertainties are then updated from experimental data. Although the model used did not work well with this particular experiment, a sensitivity analysis was additionally performed and improvements suggested.

Strganac, Thomas W.↗
DOE OSTI2022

Facilitating Machine Learning Collaborations Between Labs, Universities, And Industry

It is clear from numerous recent community reports, papers, and proposals that machine learning is of tremendous interest for particle accelerator applications. The quickly evolving landscape continues to grow in both the breadth and depth of applications including physics modeling, anomaly detection, controls, diagnostics, and analysis. Consequently, laboratories, universities, and companies across the globe have established dedicated machine learning (ML) and data science efforts aiming to make use of these new state-of-the-art tools. The current funding environment in the U.S. is structured in a way that supports specific application spaces rather than larger collaboration on community software. Here, we discuss the existing collaboration bottlenecks and how a shift in the funding environment, and how we develop collaborative tools, can help fuel the next wave of ML advancements for particle accelerators.

Edelen, J.P.↗
DOE OSTI2025

DOE Repository Metadata Profile (DRMP): A Metadata Framework for Advancing Interoperability and AI Readiness Across Scientific Repositories

The Department of Energy (DOE) funds a diverse and distributed ecosystem of repositories that steward scientific data, publications, and software across its research programs, user facilities, and national laboratories. While significant progress has been made in standardizing dataset-level metadata, the metadata describing repositories themselves (their identity, governance, access interfaces, policies, and technical capabilities) remains inconsistent and fragmented across DOE-funded systems. This variability limits discoverability, interoperability, automated validation, and AI-driven analysis, all of which are increasingly essential for modern scientific workflows. To address this gap, the DOE Data Curation Working Group (DCWG) developed the DOE Repository Metadata Profile (DRMP). The DRMP is a practical, community-driven framework that defines how repositories can describe themselves in a consistent, machine-actionable, and scalable manner. The DRMP is not a new metadata schema. Instead, it is a mapping profile and structured element set capturing the essential characteristics of DOE repositories. It harmonizes repository-level metadata across six widely adopted community schemas: RE3Data; DCAT-US v3; Schema.org; Dublin Core; DataCite 4.6; and PREMIS 3.0. This harmonization eliminates reinvention and enables interoperability within DOE and across the broader scientific ecosystem. A core objective of the DRMP is to reduce burden on repositories by allowing them to reuse their existing metadata through a Rosetta-style crosswalk rather than redesigning local implementations. The profile introduces a three-level conformance model that supports incremental adoption: • Level 1 – Minimum Viable Record (MVR): foundational identification elements required for workflows, project registration, and basic repository presence. • Level 2 – Interoperable: structured metadata enabling alignment with national and international discovery systems. • Level 3 – AI-Ready: enhanced provenance, policy transparency, fixity, semantic context, and capabilities that support automated reasoning, model training governance, and machine-assisted curation. To support implementation, the DRMP includes JSON Schema definitions, OpenAPI patterns, and MCP templates that allow repositories to publish machine-readable metadata directly within existing platforms. These resources are modular and lightweight, enabling adoption without major architectural change. Adopting the DRMP enables repositories to: • Enhance discoverability and interoperability by aligning identifiers, classifications, and descriptive elements across widely used schema standards. • Support federated discovery and cross-registration across DOE systems, Data.gov, and international catalogs. • Enable AI agents and workflow orchestration systems to interpret repository-level metadata within the American Science Cloud (AmSC) through Model Context Protocol (MCP)-based context publication. • Demonstrate alignment with DOE’s open science, stewardship, and FAIR data priorities. This guidance represents a community-driven step forward. Through voluntary adoption and continued feedback, the DRMP advances a cohesive, machine-actionable description of DOE repositories that supports FAIR data practices, preparing the infrastructure for AI-enabled research, and strengthening the discoverability and reuse of DOE’s scientific outputs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Electronic and Spintronic Open-Shell Macromolecules, Quo Vadis ?

Open-shell macromolecules (i.e., polymers containing radical sites either along their backbones or at the pendant sites of repeat units) have attracted significant attention owing to their intriguing chemical and physical (e.g., redox, optoelectronic, and magnetic) properties, and they have been proposed and/or implemented in a wide range of potential applications (e.g., energy storage devices, electronic systems, and spintronic modules). These successes span multiple disciplines that range from advanced macromolecular chemistry through nanoscale structural characterization and on to next-generation solid-state physics and the associated devices. In turn, this has allowed different scientific communities to expand the palette of radical-containing polymers relatively quickly. However, critical gaps remain on many fronts, especially regarding the elucidation of key structure–property–function relationships that govern the underlying electrochemical, optoelectronic, and spin phenomena in these materials systems. Here, we highlight vital developments in the history of open-shell macromolecules to explain the current state of the art in the field. Moreover, we provide a critical review of the successes and bring forward open opportunities that, if solved, could propel this class of materials in a meaningful manner. Finally, we provide an outlook to address where it seems most likely that open-shell macromolecules will go in the coming years. Furthermore, our considered view is that the future of radical-containing polymers is extremely bright and the addition of talented researchers with diverse skills to the field will allow these materials and their end-use devices to have a positive impact on the global science and technology enterprise in a relatively rapid manner.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Computational Methods in Heterogeneous Catalysis

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. Here, we finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Integrating Viral Metagenomics into an Ecological Framework

Viral metagenomics has expanded our knowledge of the ecology of uncultured viruses, within both environmental (e.g., terrestrial and aquatic) and host-associated (e.g., plants and animals, including humans) contexts. Here, we emphasize the implementation of an ecological framework in viral metagenomic studies to address questions in virology rarely considered ecological, which can change our perception of viruses and how they interact with their surroundings. Additionally, an ecological framework explicitly considers diverse variants of viruses in populations that make up communities of interacting viruses, with ecosystem-level effects. It provides a structure for the study of the diversity, distributions, dynamics, and interactions of viruses with one another, hosts, and the ecosystem, including interactions with abiotic factors. An ecological framework in viral metagenomics stands poised to broadly expand our knowledge in basic and applied virology. We highlight specific fundamental research needs to capitalize on its potential and advance the field.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine Learning Framework for Conotoxin Class and Molecular Target Prediction

Conotoxins are small and highly potent neurotoxic peptides derived from the venom of marine cone snails which have captured the interest of the scientific community due to their pharmacological potential. These toxins display significant sequence and structure diversity, which results in a wide range of specificities for several different ion channels and receptors. Despite the recognized importance of these compounds, our ability to determine their binding targets and toxicities remains a significant challenge. Predicting the target receptors of conotoxins, based solely on their amino acid sequence, remains a challenge due to the intricate relationships between structure, function, target specificity, and the significant conformational heterogeneity observed in conotoxins with the same primary sequence. We have previously demonstrated that the inclusion of post-translational modifications, collisional cross sections values, and other structural features, when added to the standard primary sequence features, improves the prediction accuracy of conotoxins against non-toxic and other toxic peptides across varied datasets and several different commonly used machine learning classifiers. Here, we present the effects of these features on conotoxin class and molecular target predictions, in particular, predicting conotoxins that bind to nicotinic acetylcholine receptors (nAChRs). We also demonstrate the use of the Synthetic Minority Oversampling Technique (SMOTE)-Tomek in balancing the datasets while simultaneously making the different classes more distinct by reducing the number of ambiguous samples which nearly overlap between the classes. In predicting the alpha, mu, and omega conotoxin classes, the SMOTE-Tomek PCA PLR model, using the combination of the SS and P feature sets establishes the best performance with an overall accuracy (OA) of 95.95%, with an average accuracy (AA) of 93.04%, and an f1 score of 0.959. Using this model, we obtained sensitivities of 98.98%, 89.66%, and 90.48% when predicting alpha, mu, and omega conotoxin classes, respectively. Similarly, in predicting conotoxins that bind to nAChRs, the SMOTE-Tomek PCA SVM model, which used the collisional cross sections (CCSs) and the P feature sets, demonstrated the highest performance with 91.3% OA, 91.32% AA, and an f1 score of 0.9131. The sensitivity when predicting conotoxins that bind to nAChRs is 91.46% with a 91.18% sensitivity when predicting conotoxins that do not bind to nAChRs.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1975

A state-of-the-art review of transportation systems evaluation techniques relevant to air transportation, volume 1

Mathematical and philosophical approaches are presented for evaluation and implementation of ground and air transportation systems. Basic decision processes are examined that are used for cost analyses and planning (i.e, statistical decision theory, linear and dynamic programming, optimization, game theory). The effects on the environment and the community that a transportation system may have are discussed and modelled. Algorithmic structures are examined and selected bibliographic annotations are included. Transportation dynamic models were developed. Citizen participation in transportation projects (i.e, in Maryland and Massachusetts) is discussed. The relevance of the modelling and evaluation approaches to air transportation (i.e, airport planning) is examined in a case study in St. Louis, Missouri.

Haefner, L. E.↗
NASA NTRS1976

Program on Earth Observation Data Management Systems (EODMS), appendixes

The needs of state, regional, and local agencies involved in natural resources management in Illinois, Iowa, Minnesota, Missouri, and Wisconsin are investigated to determine the design of satellite remotely sensed derivable information products. It is concluded that an operational Earth Observation Data Management System (EODMS) will be most beneficial if it provides a full range of services - from raw data acquisition to interpretation and dissemination of final information products. Included is a cost and performance analysis of alternative processing centers, and an assessment of the impacts of policy, regulation, and government structure on implementing large scale use of remote sensing technology in this community of users.

Eastwood, L. F., Jr.↗
NASA NTRS1977

Application of remote sensing to state and regional programs

The problem includes data acquisition and transformation to products acceptable to the users. Optimized institutionalization of data management, product transfer, and education of the user community are also of major concern. With respect to the lattice, various structures were suggested and the fields of application are presented.

Miller, W. F.↗
NASA NTRS1983

Comparative analysis of data base management systems

A study to determine if the Remote File Inquiry (RFI) system would handle the future requirements of the user community is discussed. RFI is a locally written and locally maintained on-line query/update package. The current and future on-line requirements of the user community were studied. Additional consideration was given to the types of data structuring the users required. The survey indicated the features of greatest benefit were: sort, subtotals, totals, record selection, storage of queries, global updating and the ability to page break. The major deficiencies were: one level of hierarchy, excessive response time, software unreliability, difficult to add, delete and modify records, complicated error messages and the lack of ability to perform interfield comparisons. Missing features users required were: formatted screens, interfield comparions, interfield arithmetic, multiple file access, security and data integrity. The survey team recommended Kennedy Space Center move forward to state-of-the-art software, a Data Base Management System which is thoroughly tested and easy to implement and use.

Smith, R.↗
NASA NTRS1988

Detecting air pollution stress in southern California vegetation using Landsat Thematic Mapper band data

Landsat Thematic Mapper (TM) and aircraft-borne Thematic Mapper simulator (TMS) data were collected over two areas of natural vegetation in southern California exposed to gradients of pollutant dose, particularly in photochemical oxidants: the coastal sage scrub of the Santa Monica Mountains in the Los Angeles basin, and the yellow pine forests in the southern Sierra Nevada. In both situations, natural variations in canopy closure, with subsequent exposure of understory elements (e.g.,rock or soil, chaparral, grasses, and herbs), were sufficient to cause changes in spectral variation that could obscure differences due to visible foliar injury symptoms observed in the field. TM or TMS data are therefore more likely to be successful in distinguishing pollution injury from background variation when homogeneous communities with closed canopies are subjected to more severe pollution-induced structural and/or compositional change. The present study helps to define the threshold level of vegetative injury detectable by TM data.

Westman, Walter E.↗
NASA NTRS1990

Management of the Space Physics Analysis Network (SPAN)

Here, the purpose is to define the operational management structure and to delineate the responsibilities of key Space Physics Analysis Network (SPAN) individuals. The management structure must take into account the large NASA and ESA science research community by giving them a major voice in the operation of the system. Appropriate NASA and ESA interfaces must be provided so that there will be adequate communications facilities available when needed. Responsibilities are delineated for the Advisory Committee, the Steering Committee, the Project Scientist, the Project Manager, the SPAN Security Manager, the Internetwork Manager, the Network Operations Manager, the Remote Site Manager, and others.

Green, James L.↗
NASA NTRS1994

Split mandrel versus split sleeve coldworking: Dual methods for extending the fatigue life of metal structures

It is common practice to use split sleeve coldworking of fastener holes as a means of extending the fatigue life of metal structures. In search of lower manufacturing costs, the aerospace industry is examining the split mandrel (sleeveless) coldworking process as an alternative method of coldworking fastener holes in metal structures. The split mandrel process (SpM) significantly extends the fatigue life of metal structures through the introduction of a residual compressive stress in a manner that is very similar to the split sleeve system (SpSl). Since the split mandrel process is significantly less expensive than the split sleeve process and more adaptable to robotic automation, it will have a notable influence upon other new manufacture of metal structures which require coldworking a significant number of holes, provided the aerospace community recognizes that the resulting residual stress distributions and fatigue life improvement are the same for both processes. Considerable testing has validated the correctness of that conclusion. The findings presented in this paper represent the results of an extensive research and development program, comprising data collected from over 400 specimens fabricated from 2024-T3 and 7075-T651 aluminum alloys in varied configurations, which quantify the benefits (fatigue enhancement and cost savings) of automating a sleeveless coldworking system.

Rodman, Geoffrey A.↗
NASA NTRS2006

Improved Atmospheric Boundary Layer Observations of Tropical Cyclones with the Imaging Wind and Rain Airborne Profiler

The Imaging Wind and Rain Arborne Profilers (IWRAP) is a dual-frequency, conically-scanning Doppler radar that measures high-resolution, dual-polarized, multi-beam C- and Ku-band reflectivity and Doppler velocity profiles of the atmospheric boundary layer (ABL) within the inner core of hurricanes.From the datasets acquired during the 2002 through 20O5 hurricane seasons as part of the ONR Coupled Boundary Layer Air-Sea Transfer (CBLAST) program and the NOAA/NESDIS Ocean Winds and Rain experiments, very high resolution radar observations of hurricanes have been acquired and made available to the CBLAST community. Of particular interest am the ABL wind fields and 3-D structures found within the inner core of hurricanes. As a result of these analysis, a limitation in the ability to retrieve the ABL wind field at very low altitudes was identified. This paper shows how this limitation has been removed and presents initial results demonstrating its new capabilities to derive the ABL wind field within the inner are of hurricanes to much lower altitudes than the ones the original system was capable of.

wind field↗
NASA NTRS2012

Fast Simulators for Satellite Cloud Optical Centroid Pressure Retrievals, 1. Evaluation of OMI Cloud Retrievals

The cloud Optical Centroid Pressure (OCP), also known as the effective cloud pressure, is a satellite-derived parameter that is commonly used in trace-gas retrievals to account for the effects of clouds on near-infrared through ultraviolet radiance measurements. Fast simulators are desirable to further expand the use of cloud OCP retrievals into the operational and climate communities for applications such as data assimilation and evaluation of cloud vertical structure in general circulation models. In this paper, we develop and validate fast simulators that provide estimates of the cloud OCP given a vertical profile of optical extinction. We use a pressure-weighting scheme where the weights depend upon optical parameters of clouds and/or aerosol. A cloud weighting function is easily extracted using this formulation. We then use fast simulators to compare two different satellite cloud OCP retrievals from the Ozone Monitoring Instrument (OMI) with estimates based on collocated cloud extinction profiles from a combination of CloudS at radar and MODIS visible radiance data. These comparisons are made over a wide range of conditions to provide a comprehensive validation of the OMI cloud OCP retrievals. We find generally good agreement between OMI cloud OCPs and those predicted by CloudSat. However, the OMI cloud OCPs from the two independent algorithms agree better with each other than either does with the estimates from CloudSat/MODIS. Differences between OMI cloud OCPs and those based on CloudSat/MODIS may result from undetected snow/ice at the surface, cloud 3-D effects, low altitude clouds missed by CloudSat, and the fact that CloudSat only observes a relatively small fraction of an OMI field-of-view.

Joiner, J.↗
NASA NTRSDate not supplied

Impacts and Conclusions of NASA DEVELOP’s Environmental Justice Landscape Analyses and Feasibility Studies

The NASA DEVELOP National Program builds dual capacity in their partner organizations and participants (students and early or transitioning career professionals) to apply Earth science and remote sensing for decision making about environmental and policy issues. The first iteration of projects at DEVELOP’s Environmental Justice (EJ) node conducted landscape analyses to identify geospatial understandings and needs of organizations working at the intersection of EJ and disasters and air quality. Their findings about collaboration, community engagement, networking, and holistic approaches to justice informed the planning and structures of the EJ node’s successive feasibility projects, which leverage NASA Earth observations and sociodemographic and economic data to provide geospatial analyses tailored to partner concerns. This poster provides an overview of DEVELOP’s approach to conducting EJ feasibility projects, use of Earth observations, end products, project outcomes, challenges, and impacts.

Julianne Bo-Heen Liu↗
DOE OSTI2024

Distributed Multi-GPU Community Detection on Exascale Computing Platforms

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (distributed parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

graph algorithms, high performance comptuing↗