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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Geospatial Data Workflow Orchestration and Architecture

In an era characterized by explosive growth in geospatial data, the selection of appropriate technologies for data storage, processing, and orchestration is critical for organizations aiming to maintain competitive advantages. This white paper provides a comprehensive analysis of how Oak Ridge National Laboratory (ORNL) has effectively employed various cloud technologies, including containerized applications, container orchestrators, and workflow orchestrators, to develop robust geospatial data processing solutions. We explore the fundamental concepts behind these technologies and compare multiple deployment models tailored to diverse use cases. Our findings conclude that while Kubernetes has emerged as the preferred platform for truly scalable and fault-tolerant production workflows, the choice of workflow orchestration tool requires careful consideration of team needs, pipeline complexity, and deployment environments. This paper aims to serve as a strategic guide for organizations leveraging geospatial data, articulating the balance between technology choices and practical implementation to enhance workflow efficacy and scalability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Artificial Intelligence for (AI) Nuclear Security: Expert Perspectives on AI Priorities for the Office of International Nuclear Security

Artificial intelligence (AI) has the potential to transform nuclear security operations, offering opportunities to enhance effectiveness while simultaneously introducing new challenges. As AI technologies rapidly evolve, agencies across the United States Government (USG) are researching, implementing, and evaluating various AI models and systems. Given the broad capabilities and applications of these technologies, it is essential for each agency to identify and articulate those areas where it can make meaningful contributions aligned with its mission and expertise. To address this need for strategic focus, in late Fiscal Year 2025 (FY2025), the Office of International Nuclear Security (INS) established an AI Task Force (AITF) to gather input from subject matter experts (SMEs) regarding the most appropriate role INS could serve in researching, evaluating, or implementing AI for nuclear security. The AITF engaged 15 experts from national laboratories with backgrounds in cyber security, physical security, transport security, insider threat mitigation, nuclear engineering, human-systems engineering, and AI/ML development. This white paper summarizes the insights gathered from these SMEs and presents a potential roadmap for INS engagement with AI technologies. The recommendations outlined here are intended to inform INS leadership as they make strategic decisions about resource allocation and program direction in this rapidly evolving technological domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

DOE BSSD Performance Management Metrics Report Q2

The vision of the National Microbiome Data Collaborative (NMDC) centers on the concept of connecting data, people, and ideas to advance microbiome innovation and discovery. Building data infrastructure, while key to NMDC’s ability to execute on our vision, can only go so far in creating scientific impact. By fostering strong community partnerships and developing a set of robust community outreach and training programs, we are able to turn our products – the Submission Portal, NMDC EDGE, and the Data Portal – into tools that empower the scientific community. Our multi-pronged community building approach spans individual researchers, research teams, consortia and scientific societies, and institutions and federal agencies. To foster a collaborative and inclusive community-centered environment, we have identified three strategic objectives to promote an inclusive and connected community: (1) recognize and support the diverse research needs and perspectives of the microbiome research community; (2) promote best practices across the microbiome community, from researchers to funders, through community-driven practices (FAIR, CARE, and TRUST); and (3) build a microbiome ecosystem that enables scientific discovery and innovation across stakeholders. These strategic objectives allow our team to focus on impact across a diverse range of activities, from launching the American Society for Microbiology (ASM) Microbiome Data Prize to supporting the Ambassador and Champions programs fostering learning and building a collaborative network. We broadly communicate our work through social media (X/Twitter, LinkedIn, and Instagram), The Microbiome Standard (our quarterly newsletter), and Annual Reports. All our work is underpinned by a strong commitment to diversity, equity, and inclusion as articulated in our Action Plan that tracks progress towards key metrics. A core component of our engagement strategy is user research. User research ensures the Submission Portal, NMDC EDGE, Data Portal, and the new Field Notes mobile app are designed with and for the scientific community. Our user research efforts consist of asking researchers exploratory questions to collect information on researcher priorities, methodologies, and perceptions to ensure that we are aware of the current state of microbiome research. Our usability testing provides researchers with prototypes or test environments of the NMDC products, and we capture valuable information on how users interact with the products to make improvements. Given the diverse nature of microbiome work, we acknowledge that we are not aware of all pressing data challenges and thus rely on the research community to help us identify the most important issues to prioritize. To date, we have conducted 24 interviews and one beta-testing call with 10 participants across all NMDC products, which have generated 321 insights and 120 action items. Herein, we describe the ways we engage with the microbiome research community to advance the NMDC mission.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Role of Computational Parameters on Predicting Self-Consistent Residual Stress and Distortion during Wire Arc Additive Manufacturing

Production of three-dimensional metallic parts through integration of an articulated robot and gas metal arc welding, also known as wire arc additive manufacturing (WAAM), can produce large-scale components with moderate geometrical complexity. This technology is particularly appealing due to its high deposition rates, scalability, and cost-effective feedstock compared to other AM processes. Despite its advantages, WAAM adoption is hindered by challenges in ensuring geometric conformity without extensive distortion, defect-free structures, and consistent mechanical properties. Finite element analysis (FEA) is often used to address the challenge of geometrical conformity. As the size of parts increases, the best practices for mesh size and temporal resolution known in the literature become computationally unviable. This research examined the effects of mesh and time-step resolutions during transient FEA of a large-scale (248 layers) metallic part. The impact of computational parameters on the thermal history, displacement, and residual stress distributions were evaluated. The results showed that predicted distortion was consistent across resolutions, while time-step length significantly affected predicted thermal history, and mesh size influenced residual stress distributions. To investigate this relationship further, directionally biased meshes were considered and analyzed. The results indicated that increasing mesh resolution perpendicular to the welding path yielded stress predictions that aligned closely with higher-resolution models while offering substantial computational savings. In conclusion, the significances of this research are related to verification and validation of WAAM models for widespread industrial adoption and pragmatic guidelines for optimizing computation parameters for balancing computational efficiency and predictive accuracy of residual stress and distortion.

Solsbee, Brandon [Univ. of Tennessee, Knoxville, T↗
DOE OSTI2022

Using Macro- and Microscale Preservation in Vertebrate Fossils as Predictors for Molecular Preservation in Fluvial Environments

Exceptionally preserved fossils retain soft tissues and often the biomolecules that were present in an animal during its life. The majority of terrestrial vertebrate fossils are not traditionally considered exceptionally preserved, with fossils falling on a spectrum ranging from very well-preserved to poorly preserved when considering completeness, morphology and the presence of microstructures. Within this variability of anatomical preservation, high-quality macro-scale preservation (e.g., articulated skeletons) may not be reflected in molecular-scale preservation (i.e., biomolecules). Excavation of the Hayden Quarry (HQ; Chinle Formation, Ghost Ranch, NM, USA) has resulted in the recovery of thousands of fossilized vertebrate specimens. This has contributed greatly to our knowledge of early dinosaur evolution and paleoenvironmental conditions during the Late Triassic Period (~212 Ma). The number of specimens, completeness of skeletons and fidelity of osteohistological microstructures preserved in the bone all demonstrate the remarkable quality of the fossils preserved at this locality. Because the Hayden Quarry is an excellent example of good preservation in a fluvial environment, we have tested different fossil types (i.e., bone, tooth, coprolite) to examine the molecular preservation and overall taphonomy of the HQ to determine how different scales of preservation vary within a single locality. We used multiple high-resolution mass spectrometry techniques (TOF-SIMS, GC-MS, FT-ICR MS) to compare the fossils to unaltered bone from extant vertebrates, experimentally matured bone, and younger dinosaurian skeletal material from other fluvial environments. FT-ICR MS provides detailed molecular information about complex mixtures, and TOF-SIMS has high elemental spatial sensitivity. Using these techniques, we did not find convincing evidence of a molecular signal that can be confidently interpreted as endogenous, indicating that very good macro- and microscale preservation are not necessarily good predictors of molecular preservation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Benchmarking a 9Cr-2WVTa Reduced Activation Ferritic Martensitic Steel Fabricated via Additive Manufacturing

Reduced activation ferritic (RAF) martensitic steels are promising candidates for the first wall of fusion reactors. However, current manufacturing capabilities call for these components to be made by welding wrought plates. This limits design freedom and necessitates the use of post-weld heat treatments (PWHT) in accordance with the boiler and pressure vessel code. Additive manufacturing (AM) can offer a unique solution to solve this challenge by leveraging the layer-wise deposition strategy to come up with temper bead deposition techniques to eliminate post-processing heat treatments (PPHT). However, it is necessary to benchmark the properties of RAF steels fabricated by AM with their wrought counterparts to identify the process-structure-property correlation, which is the goal of this study. The study demonstrates that while tensile properties at room temperature and high temperatures are satisfactory, the as fabricated and samples after PPHT have significant heterogeneity in tensile elongation. This has been attributed to the presence of discontinuities in the build. The as-fabricated samples have an average tensile strength of 1190 + 12 MPa and an average elongation of 15 + 5% at room temperature and 658 ± 20 MPa ultimate tensile strength (UTS) and 14 ± 7% at 600 °C. After the post-weld heat treatment, mechanical properties decrease to around 600–650 MPa and an elongation between 20–25% at room temperature to 300 MPa UTS and 25–28% elongation at 600 °C. The characterization of microstructures at various length scales demonstrates that the as-fabricated structure has a significant fraction of delta ferrite in a lath martensitic matrix. No precipitates could be identified in the as-fabricated structure. PPHT led to a decrease in the area fraction of delta ferrite and precipitation of M 23 C 6 and MX. Detailed characterization clearly demonstrates that the lack of precipitates in the as-fabricated structure could be due to the slow tempering response of the alloy. Finally, the needs to develop new alloys to achieve the objectives stated above are articulated.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study

Neural scaling laws play a pivotal role in the performance of deep neural networks and have been observed in a wide range of tasks. However, a complete theoretical framework for understanding these scaling laws remains underdeveloped. In this paper, we explore the neural scaling laws for deep operator networks, which involve learning mappings between function spaces, with a focus on the Chen and Chen style architecture. These approaches, which include the popular Deep Operator Network (DeepONet), approximate the output functions using a linear combination of learnable basis functions and coefficients that depend on the input functions. We establish a theoretical framework to quantify the neural scaling laws by analyzing its approximation and generalization errors. We articulate the relationship between the approximation and generalization errors of deep operator networks and key factors such as network model size and training data size. Moreover, we address cases where input functions exhibit low-dimensional structures, allowing us to derive tighter error bounds. These results also hold for deep ReLU networks and other similar structures. Our results offer a partial explanation of the neural scaling laws in operator learning and provide a theoretical foundation for their applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning

Quantifying uncertainties for machine learning (ML) models is a foundational challenge in modern data analysis. This challenge is compounded by at least two key aspects of the field: (a) inconsistent terminology surrounding uncertainty and estimation across disciplines, and (b) the varying technical requirements for establishing trustworthy uncertainties in diverse problem contexts. In this position paper, we aim to clarify the depth of these challenges by identifying these inconsistencies and articulating how different contexts impose distinct epistemic demands. We examine the current landscape of estimation targets (e.g., prediction, inference, simulation-based inference), uncertainty constructs (e.g., frequentist, Bayesian, fiducial), and the approaches used to map between them. Drawing on the literature, we highlight and explain examples of problematic mappings. To help address these issues, we advocate for standards that promote alignment between the \textit{intent} and \textit{implementation} of uncertainty quantification (UQ) approaches. We discuss several axes of trustworthiness that are necessary (if not sufficient) for reliable UQ in ML models, and show how these axes can inform the design and evaluation of uncertainty-aware ML systems. Our practical recommendations focus on scientific ML, offering illustrative cases and use scenarios, particularly in the context of simulation-based inference (SBI).

Trivedi, Shubhendu [MIT] (ORCID:0000000312374301)↗
DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗
DOE OSTI2020

Software Sustainability $\&$ High Energy Physics

New facilities of the 2020s, such as the High Luminosity Large Hadron Collider (HL-LHC), will be relevant through at least the 2030s. This means that their software efforts and those that are used to analyze their data need to consider sustainability to enable their adaptability to new challenges, longevity, and efficiency, over at least this period. This will help ensure that this software will be easier to develop and maintain, that it remains available in the future on new platforms, that it meets new needs, and that it is as reusable as possible. This report discusses a virtual half-day workshop on "Software Sustainability and High Energy Physics" that aimed 1) to bring together experts from HEP as well as those from outside to share their experiences and practices, and 2) to articulate a vision that helps the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) to create a work plan to implement elements of software sustainability. Software sustainability practices could lead to new collaborations, including elements of HEP software being directly used outside the field, and, as has happened more frequently in recent years, to HEP developers contributing to software developed outside the field rather than reinventing it. A focus on and skills related to sustainable software will give HEP software developers an important skill that is essential to careers in the realm of software, inside or outside HEP. The report closes with recommendations to improve software sustainability in HEP, aimed at the HEP community via IRIS-HEP and the HEP Software Foundation (HSF).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Real-Time Modal Analysis of Electric Power Grids - The Need for Dynamic State Estimation: Preprint

We articulate the reason why dynamic state estimation is needed to push the boundaries of the modal analysis of electric power grids in real-time operation. Then, we demonstrate how to unravel linear and nonlinear modes by using the extended dynamic mode decomposition along with estimates of the synchronous generators' rotor angles and rotor speed deviations from nominal speed. The estimated modes are associated with electromechanical oscillations that take place continuously in electric power grids because of imbalances between power generation and demand. The numerical simulations are performed on a synthetic, albeit realistic, 2,000-bus network that was designed to resemble the electric power grid of Texas.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Developing Technology Performance Level Assessments for Early-Stage Wave Energy Converter Technologies: Preprint

The advantage of using Technology Performance Level (TPL) in conjunction with Technology Readiness Level (TRL) assessments in guiding technology development trajectories to successful outcomes in less time, at less overall cost, and with less encountered risk has been well articulated in the literature. In partnership with industry and international collaborators, a TPL assessment methodology for grid-connected applications has been developed through the application of the systems engineering approach. Metrics under seven different categories have been developed, weighted based on their relative relevance, and combined to yield a composite score. The methodology has been implemented in a spreadsheet tool plus a web application specifically aimed at assessing early stage (TRL 1-3) concepts. The target use cases are (a) technology developers improving their design, to find fatal flaws early, to get feedback on current design, to identify areas of improvement that will yield the highest return on investment, (b) reviewers assessing technologies in competitions or for making funding decisions, (c) investor or project developer doing due diligence, (d) policy makers landscaping the technology domain for formulating R&D strategy. The methodology and the tools are undergoing continuous improvement based on the experience and lessons learnt from applying it to internal and external marine energy technology development projects. The methodology is also being adapted for assessing WECs servicing markets outside the continental grid - broadly categorized as Powering the Blue Economy (PBE) applications. Such applications have vastly different functional requirements entailing a modification of the methodology to account for their higher risk tolerance, reduced price sensitivities, lower power needs, different permitting protocols, etc. This paper presents the latest status of the TPL assessment methodology and tools, describes its adaptation to select PBE markets, and explores its extension to other domains where it could provide a comprehensive and holistic measure of a nascent or disruptive technology's technoeconomic performance potential.

metrics↗
DOE OSTI2021

Self-balancing, long reach robotic arm

Vital accelerator components are difficult to inspect and troubleshoot when they fail. Theuse of a self-balancing, long articulating robot simplifies the identification and troubleshootingof broken components in the narrow places of the beamline by pinpointing the location of theseerrors due to its ability to maneuver through these limited spaces. The design of the long reachrobot is the second iteration of a previous model accomplished by interns Amanda Hoeksemaand Brenda Sanchez. This new robot was redesigned with a greater focus on safety and structural integrity by removing 3D-printed parts in load-bearing positions and switching to off-the-shelf components with manufacturer-specified capabilities. These changes ensure that the new robotic arm and counterbalance designs conform closer to safety regulations by not depending on the vague durability of 3D-printed parts. This design change necessitated new calculations for the arm and counterweight to ensure the robot will function as intended.

Hoeksema, Amanda↗
DOE OSTI2021

Comparing the OpenSolar Remote Shading Analysis Tool to On-Site Shading Measurements

OpenSolar is a free of charge application built for solar installers that creates a fully articulated 3D model of the site and performs remote shading analysis, in order to produce highly accurate designs and interactive, customizable proposals. Under the National Renewable Energy Laboratory (NREL) Commercialization Assistance Program (NCAP), NREL independently verified the accuracy of OpenSolar's remote analysis of solar access percentage values (SAVs) on an annual basis for locations in Los Angeles, CA, and Denver, CO. In aggregate, the results of the analysis show that Open Solar-calculated annual SAV measurements were statistically equivalent within ±2.63 SAVs for Los Angeles locations and ±2.69 SAVs for Denver locations compared to those of on-site measurements made with SunEye devices.

MATHEMATICS AND COMPUTING,SOLAR ENERGY↗
DOE OSTI2021

A Community Roadmap for Scientific Workflows Research and Development

The landscape of workflow systems for scientific applications is notoriously convoluted with hundreds of seemingly equivalent workflow systems, many isolated research claims, and a steep learning curve. To address some of these challenges and lay the groundwork for transforming workflows research and development, the WorkflowsRI and ExaWorks projects partnered to bring the international workflows community together. This paper reports on discussions and findings from two virtual “Workflows Community Summits” (January and April, 2021). The overarching goals of these workshops were to develop a view of the state of the art, identify crucial research challenges in the workflows community, articulate a vision for potential community efforts, and discuss technical approaches for realizing this vision. To this end, participants identified six broad themes: FAIR computational workflows; AI workflows; exascale challenges; APIs, interoperability, reuse, and standards; training and education; and building a workflows community. We summarize discussions and recommendations for each of these themes.

Ferreira Da Silva, Rafael↗
DOE OSTI2021

Developing Technology Performance Level Assessments for Early-Stage Wave Energy Converter Technologies

The advantage of using Technology Performance Level (TPL) in conjunction with Technology Readiness Level (TRL) assessments in guiding technology development trajectories to successful outcomes in less time, at less overall cost, and with less encountered risk has been well articulated in the literature. In partnership with industry and international collaborators, a TPL assessment methodology for grid-connected applications has been developed through the application of the systems engineering approach. Metrics under seven different categories have been developed, weighted based on their relative relevance, and combined to yield a composite score. The methodology has been implemented in a spreadsheet tool plus a web application specifically aimed at assessing early stage (TRL 1-3) concepts. The target use cases are (a) technology developers improving their design, to find fatal flaws early, to get feedback on current design, to identify areas of improvement that will yield the highest return on investment, (b) reviewers assessing technologies in competitions or for making funding decisions, (c) investor or project developer doing due diligence, (d) policy makers landscaping the technology domain for formulating R&D strategy. The methodology and the tools are undergoing continuous improvement based on the experience and lessons learnt from applying it to internal and external marine energy technology development projects. The methodology is also being adapted for assessing WECs servicing markets outside the continental grid - broadly categorized as Powering the Blue Economy (PBE) applications. Such applications have vastly different functional requirements entailing a modification of the methodology to account for their higher risk tolerance, reduced price sensitivities, lower power needs, different permitting protocols, etc. This paper presents the latest status of the TPL assessment methodology and tools, describes its adaptation to select PBE markets, and explores its extension to other domains where it could provide a comprehensive and holistic measure of a nascent or disruptive technology's technoeconomic performance potential.

metrics↗
DOE OSTI2021

A Taxonomic Classification Approach for Global Spatio-temporal Data

The World Bank, World Health Organization, and other major vendors collectively provide thousands of global time series datasets that focus on issues of the environment, public health, economics, violence, education, and national security. Sorting these data into meaningful information requires the use of data mining techniques to cluster trends into an orderly and manageable number of cases. The World SpatioTemporal Analytics and Mapping (WSTAMP) project database (wstamp.ornl.gov) was developed to spatiotemporally harmonize global vendor data (23,300+ attributes, 200+ locations, 50+ years). Within the WSTAMP analytical environment, Dynamic Time Warping (DTW) has been a highly effective data-driven approach for clustering and mapping these time series into national spatiotemporal behavior maps. Two significant properties have surfaced from this work. First, several recognizable cluster patterns have emerged and persist across a range of locations, attributes, and time frames (e.g., increasing, decreasing, rebounding, peak, oscillating). Secondly, practitioners engaging WSTAMP have noted the explanatory and anticipatory value of these patterns and articulated particular interest in detecting them within the spatiotemporal cube. This need was addressed by shifting DTW-based clustering from an open ended, data-driven implementation to a taxonomic pattern matching approach. This paper presents the method including implementation strategies for visualization and human computer interaction and applies the approach to a sample data set and concludes with next steps.

Stewart, Robert↗
DOE OSTI2022

WaveSPARC: TPL Assessment - Guiding Technology Development Trajectories to Successful Outcomes in Less Time, at Less Overall Cost, and With Less Encountered Risk

The advantage of using Technology Performance Level (TPL) in conjunction with Technology Readiness Level (TRL) assessments in guiding technology development trajectories to successful outcomes in less time, at less overall cost, and with less encountered risk has been well articulated in the literature. This poster will showcase the TPL assessment methodology and tools developed at NREL.

marine renewable energy↗