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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Leveraging Single-Page Applications for Seamless Scientific Workflows: DevSecOps Considerations

Single-page applications (SPAs) have become indispensable in modern frontend development, with widespread adoption in scientific applications. The process of creating a single-page web application development environment which accurately reflects the production environment isn’t always straightforward. Most SPA build systems assume configuration at build time, while DevSecOps engineers prefer runtime configuration. This paper suggests a framework-agnostic approach to address issues that encompass both development and deployment, but are difficult to tackle without knowledge in both domains.

Drane, Lance↗
DOE OSTI2022

A universal method to compare parts from STEP files

Abstract Model Based Definition (MBD) captures the complete specification of a part in digital form and leverages (at least) the universal “Standard for the Exchange of Product” (STEP) file format. MBD has revolutionized manufacturing due to time and cost savings associated with containing all engineering data within a single digital source. This work presents a novel method to transform digital definitions in any given STEP file into a tensor-like structure that is unique for each part and can be used to regenerate the original STEP file completely. Resulting STEP tensors are amenable to part comparison based on various part specifications in a general and straightforward manner. Here, part similarity is evaluated among sets of parts according to specific geometry, material composition, and design intent. Importantly, specification similarity can be quantified using only the tensors’ structure. As such, this approach is not limited to families of geometric shapes, part types, or fabrication methods; nor does it require any prior knowledge about the parts being compared.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
DOE OSTI2018

Lattice rotations in precipitate free zones in an Al-Mg-Si alloy

Highlights: • Recovery processes in precipitate free zones differ from those in grain interiors. • Most precipitate free zones contain few dislocations, after deformation. • Deformation can induce large misorientations in precipitate free zones. • Some deformed precipitate free zones contain nanoscale grains. - Abstract: Scanning precession electron diffraction and automated crystal orientation mapping in a transmission electron microscope (TEM) have been applied to quantitatively study the crystal orientation of precipitate free zones (PFZs) of four GB regions in an AA6060 alloy in peak aged condition (temper T6) after uniaxial compression to 20% engineering strain. The PFZ width in the alloy was found to be w = 170 ± 40 nm. The results show that some PFZs develop significant misorientations relative to their parent grain, and represent, to the best knowledge of the authors, the first quantitative evidence of this. This misorientation may either be constant inside a particular PFZ, making it appear like a band or a very elongated subgrain, or be partitioned in discrete regions with a diameter comparable to the PFZ width, making the former PFZ into a collection of small grains. The band-like PFZ observed in this work had a misorientation relative to its parent grain of ≈ 7°, while the grain-like PFZ had grains with misorientations between ≈ 12° and ≈ 20° relative to its parent grain. The other PFZs that were observed had only limited misorientations relative to their parent grains, and had either dislocations perpendicular to the GB plane or a dislocation wall at the transition region. A general TEM study of the material at various engineering strains was also conducted and suggests that grain-like PFZs are more frequent for larger strains, indicating that the different PFZ features are likely due to different strain localisation in individual PFZs. This localisation is expected to be influenced by the orientation of the loading axis relative to crystal orientations and GB planes. It is also suggested that the different PFZ features engender different work hardening rates and possibly affect nucleation of intergranular fracture. The results support previous studies on the microstructure evolution of PFZs in age-hardenable aluminium alloys during deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2019

APS Science 2019 (V.1): Research and Engineering Highlights from the Advanced Photon Source at Argonne National Laboratory

The Advanced Photon Source Facility at Argonne National Laboratory The APS, a national synchrotron radiation research facility operated by UChicago Argonne, LLC, and Argonne National Laboratory for the U.S. Department of Energy (DOE) Office of Science-Basic Energy Sciences, Scientific User Facilities Division, provides this nation’s brightest high-energy x-ray beams for science. Research by APS users extends from the center of the Earth to outer space, from new information on batteries, combustion engines and microcircuits, to new and improved pharmaceuticals, and nanotechnologies whose scale is measured in billionths of a meter. The APS helps researchers illuminate answers to the challenges of our high-tech world, from developing new forms of energy to sustaining our nation’s technological and economic competitiveness to pushing back against the ravages of disease. Research at the APS promises to have far-reaching impact on our technology, our economy, our health, and fundamental knowledge of the materials that make up our world.

14 SOLAR ENERGY↗
DOE OSTI2020

The challenges of theory-software translation

Background: Software is now ubiquitous within research. In addition to the general challenges common to all software development projects, research software must also represent, manipulate, and provide data for complex theoretical constructs. Ensuring this process of theory-software translation is robust is essential to maintaining the integrity of the science resulting from it, and yet there has been little formal recognition or exploration of the challenges associated with it. Methods: We thematically analyse the outputs of the discussion sessions at the Theory-Software Translation Workshop 2019, where academic researchers and research software engineers from a variety of domains, and with particular expertise in high performance computing, explored the process of translating between scientific theory and software. Results: We identify a wide range of challenges to implementing scientific theory in research software and using the resulting data and models for the advancement of knowledge. We categorise these within the emergent themes of design, infrastructure, and culture, and map them to associated research questions. Conclusions: Systematically investigating how software is constructed and its outputs used within science has the potential to improve the robustness of research software and accelerate progress in its development. We propose that this issue be examined within a new research area of theory-software translation, which would aim to significantly advance both knowledge and scientific practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗
DOE OSTI2019

FUSRAP Data Management and Information Transitions - 19459

The Formerly Utilized Sites Remedial Action Program (FUSRAP) was created in the mid-1970's to clean up radiological contamination resulting from early development of nuclear weapons. The US Department of Energy (US DOE) was responsible for FUSRAP until October 1997, when Congress transferred administration and execution of FUSRAP site cleanup to the US Army Corps of Engineers (USACE). Currently, the US DOE Office of Legacy Management (LM) determines the eligibility of a site for referral to USACE. USACE determines whether the site is included in FUSRAP for cleanup, conducts remedial actions, and transfers sites back to LM for long-term surveillance and maintenance (LTS and M). LM and USACE established the Data Management Working Group (DMWG) to (1) establish a common understanding of US DOE and USACE district-specific data, records practices, and requirements as they apply to FUSRAP programmatic documents for site transition and transfer; (2) establish methods to ensure accurate data transfer; and (3) develop a timeline for data and records transfer. These methods will ensure continuity of stewardship while transitioning from cleanup to LTS and M and beyond as more FUSRAP sites transfer from USACE to LM. The DMWG has met monthly and the meetings are attended by a wide range of LM and USACE project managers and subject matter experts. Discussion topics have addressed site records management, including data needs and uses; transfer and posting of Administrative Record and Permanent Record files for public accessibility; and management of legal and classified files. There have been a number of group successes, including the following: - Establishment of a common understanding of data and records practices and requirements as the sites transition and transfer from USACE to LM; - Development of effective lines of communication between USACE and LM concerning data management for transition; - Establishment of a cohesive approach to data management between the two agencies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Adoption of image-driven machine learning for microstructure characterization and materials design: A Perspective

Microstructure characterization enables the development of structure-processing-property relationships critical to several research areas within the broad field of materials science, from alloy design to the assessment of corrosion resistance, and failure analysis. Conventional approaches to material characterization have relied on either qualitative inference by the human ex-pert or software applications that can extract high-level features from images, such as boundary segmentation, average grain diameter, etc. Such approaches rely heavily on subject matter expert user intervention and knowledge of what phases or more generally, what microstructural features, are of interest. The recent surge in the adoption of machine learning techniques to address problems in materials engineering has brought with it an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we review the applications of IDML to the field of materials characterization. A canonical hierarchy of stages is defined, which when put sequentially together completes an IDML study: problem definition, dataset building, model selection and training, model evaluation, and integration with existing instrumentation or simulation workflow. The studies reviewed in this work are analyzed from the perspective of each of these stages. Such a review permits agranular assessment of the field, for example the impact of IDML on materials characterization at the nanoscale, the size of a typical dataset required to train a semantic segmentation model on electron microscopy images, ubiquitousness of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability in the field of IDML for materials characterization, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.

Baskaran, Arun↗
DOE OSTI2021

The value of human data annotation for machine learning based anomaly detection in environmental systems

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection, the research community focused on environmental systems still lacks a comparative analysis that is simultaneously comprehensive, objective, and systematic. This knowledge gap is addressed for the first time in this study, where 15 different supervised and unsupervised anomaly detection models are evaluated on 5 different environmental datasets from engineered and natural aquatic systems. To this end, anomaly detection performance, labelling efforts, as well as the impact of model and algorithm tuning are taken into account. As a result, our analysis reveals the relative strengths and weaknesses of the different approaches in an objective manner without bias for any particular paradigm in machine learning. Most importantly, our results show that expert-based data annotation is extremely valuable for anomaly detection based on machine learning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Radiological Toolbox User’s Guide

The Radiological Toolbox software developed by Oak Ridge National Laboratory (ORNL) for U. S. Nuclear Regulatory Commission (NRC) is designed to provide electronic access to the vast and varied data that underlies the field of radiation protection. These data represent physical, chemical, anatomical, physiological, and mathematical parameters detailed in various handbooks which a health physicist might consult while in his office. The initial motivation for the software was to serve the needs of the health physicist away from his office and without access to his handbooks; e.g., NRC inspectors. The earlier releases of the software were widely used and accepted around the world by not only practicing health physicist but also those within educational programs. This release updates the software to accommodate changes in Windows operating systems and, in some aspects, radiation protection. This release has been tested on Windows 7 and 8 and on 32- and 64-bit machines. The nuclear decay data has been updated and thermal neutron capture cross sections and cancer risk coefficients have been included. This document and the software’s user’s guide provide further details and documentation of the information captured within the Radiological Toolbox.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Coordination and Convening of the 2016 Arctic Science Summit Week

The Arctic Science Summit Week, Arctic Observing Summit, Arctic Council Senior Arctic Officials, Model Arctic Council, and International Arctic Assembly were convened on the campus of the University of Alaska Fairbanks with great productivity and satisfaction of the participants. We were pleased to welcome over 1000 participants from 30 different nations and over 130 different institutions. The organization and execution of these meetings was extensive and complex involving more than 250 coordinators, volunteers and contributors from across Alaska. The participants were enthusiastic in their praise of the content and accomplishments of the meeting, but they were equally happy about the genuine welcome offered to our guests by the people of Alaska. Hosting a complex event such as this summit required an army of supporting services and we were blessed to have volunteers from Fairbanks, North Pole, Anchorage and other communities throughout Alaska helping us meet these needs. This truly was an event hosted by the people of Alaska. The significance of these events cannot be overstated. The US and global communities are finally coming to the realization of the important role that the Arctic plays in international politics, economics, and science. The Arctic has experienced tremendous changes in recent years, offering new opportunities that may be addressed through international collaborations, and serious challenges that must be addressed through active investment, adaptation and national and international coordination. Over 10% of the meeting participants were indigenous peoples, from indigenous organizations or hailed from small remote communities. This is still lower than we had hoped, but it is greater participation than similar meetings have experienced in the past. It is through such engagement that we can attack problems related to the changing environment, stagnant economies, and social ills.

42 ENGINEERING↗
DOE OSTI2023

Science &Technology Review (October/November 2022)

Livermore researchers seek to understand the disease mechanism of amyotrophic lateral sclerosis (ALS) and, with that knowledge, identify therapies to cure this always-fatal disease. As the article beginning on p. 4 describes, the ALS research effort combines Laboratory core competencies in bioscience, engineering, computational modeling, and data science.

36 MATERIALS SCIENCE↗
DOE OSTI2021

BETO 2021 Peer Review - FCIC Task 6: High Temperature Conversion

The impacts of feedstock variability on pyrolysis processes are significant but poorly defined. Current engineering designs are based on empirical guidelines, useful only over a narrow range of feedstock properties. The objectives of this project are to (1) Develop science-based knowledge of how feedstock attributes and operational parameters impact pyrolysis process reliability and product quality; and (2) Build an experimental and computational toolset that predicts these outcomes, enabling processes to optimize reliability and product quality. Biomass is a complex feedstock. Controlling for and testing the effects of individual attributes is very challenging. This project couples multiscale experimentation and modeling to accurately capture the fundamental physics and chemistry of biomass flow and conversion behavior in feeding and pyrolysis reactor operations. Our focus is on pine residue attributes – anatomical fraction (bark, needles, wood), particle morphology (size/shape distribution, density, porosity), and chemical composition (extractives, biopolymers, alkali metals) – that impact product quality for downstream catalytic upgrading. Because detailed pyrolysis product characterization is limited, cutting-edge analytical techniques are being developed to reveal impactful product attributes. The tools and knowledge developed here will enable integrated pyrolysis-based processes that are more robust, flexible, and market-responsive with respect to feedstock variability.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Electrodialysis and nitrate reduction (EDNR) to enable distributed ammonia manufacturing from wastewaters

Underutilized wastewaters containing dilute levels of reactive nitrogen (Nr) can help rebalance the nitrogen cycle. This study describes electrodialysis and nitrate reduction (EDNR), a reactive electrochemical separation architecture that combines catalysis and separations to remediate nitrate and ammonium-polluted wastewaters while recovering ammonia. By engineering operating parameters (e.g., background electrolyte, applied potential, electrolyte flow rate), we achieved high recovery and conversion of Nr in both simulated and real wastewaters. The EDNR process demonstrated long-term robustness and up-concentration that recovered >100 mM ammonium fertilizer solution from agricultural runoff that contained 8.2 mM Nr. EDNR is the first reported process to our knowledge that remediates dilute real wastewater and recovers ammonia from multiple Nr pollutants, with an energy consumption (245 MJ per kg NH 3 –N in simulated wastewater, 920 MJ per kg NH 3 –N in agricultural runoff) on par with the state-of-the-art. Demonstrated first at proof-of-concept and engineered to technology readiness level (TRL) 4–5, EDNR shows great promise for distributed wastewater treatment and sustainable ammonia manufacturing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Sensor Authentication in Collaborating Sensor Networks

In this thesis, we address a new security problem in the realm of collaborating sensor networks. By collaborating sensor networks, we refer to the networks of sensor networks collaborating on a mission, with each sensor network is independently owned and operated by separate entities. Such networks are practical where a number of independent entities can deploy their own sensor networks in multi-national, commercial, and environmental scenarios, and some of these networks will integrate complementary functionalities for a mission. In the scenario, we address an authentication problem wherein the goal is for the Operator O i of Sensor Network S i to correctly determine the number of active sensors in Network Si. Such a problem is challenging in collaborating sensor networks where other sensor networks, despite showing an intent to collaborate, may not be completely trustworthy and could compromise the authentication process. We propose two authentication protocols to address this problem. Our protocols rely on Physically Unclonable Functions, which are a hardware based authentication primitive exploiting inherent randomness in circuit fabrication. Our protocols are light-weight, energy efficient, and highly secure against a number of attacks. To the best of our knowledge, ours is the first to addresses a practical security problem in collaborating sensor networks.

42 ENGINEERING↗
DOE OSTI2021

Optimal Header Design for Diffusion Bonded Heat Exchangers

Microchannel heat exchangers have seen increasing adoption in many high-pressure applications in recent decades but are subject to particulate fouling from the relatively small channel size compared to traditional designs. Typical cleaning methods require process shutdown, heat exchanger removal, cleaning, then reassembly. The objective of this project was to refine and transfer technology to enable header design improvements for Cleaning-in-Place (CIP), allowing for reduced/negligible process interruption for the cleaning process. The technology transfer was from Sandia National Laboratories (Sandia) to Vacuum Process Engineering, Inc. (VPE). This primary purpose of CIP was developed while considering channel flow uniformity and heat exchanger cost. The project phases were to 1) capture and define potential improvement options, 2) evaluate options with both simulation and experiments, and 3) transfer design knowledge to the industry partner. These efforts resulted in improved header designs from the first known focused effort in this area. The improved designs will help the entire microchannel heat exchanger field that has applications in supercritical CO 2 power cycles, hydrogen (fuel cell) vehicle fueling, liquified natural gas processing, and more.

42 ENGINEERING↗
DOE OSTI2018

Coupled laboratory and field investigations resolve microbial interactions that underpin persistence in hydraulically fractured shales

Hydraulic fracturing is one of the industrial processes behind the surging natural gas output in the United States. This technology inadvertently creates an engineered microbial ecosystem thousands of meters below Earth’s surface. Here, we used laboratory reactors to perform manipulations of persisting shale microbial communities that are currently not feasible in field scenarios. Metaproteomic and metabolite findings from the laboratory were then corroborated using regression based modeling performed on metagenomic and metabolite data from more than 40 produced fluids from five hydraulically fractured shale wells. Collectively, our findings show that Halanaerobium, Geotoga, and Methanohalophilius strain abundances predict a significant fraction of nitrogen and carbon metabolites in the field. Our laboratory findings also exposed cryptic predatory, cooperative, and competitive interactions that impact microorganisms across fractured shales. Scaling these results from the laboratory to the field identified mechanisms underpinning biogeochemical reactions, yielding knowledge that can be harnessed to potentially increase energy yields and inform management practices in hydraulically fractured shales.

hydraulic fracturing, metaproteomics, methanogenes↗