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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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DOE OSTI2023

An Update on the Geothermal Data Repository's Data Standards and Pipelines: Geospatial Data and Distributed Acoustic Sensing Data

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented or is currently implementing data standards and automated data pipelines for the following geothermal data types: 1) drilling data, 2) geospatial datasets, and 3) Distributed Acoustic Sensing (DAS) data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how the GDR team can improve this process.

cloud-optimized↗

DOE OSTI2025

Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics

Microplastic spectral analysis is one of the most time-consuming processes in studying microplastic pollution, often requiring days per sample. Researchers are transitioning to automated batch and hyperspectral image analysis techniques to enhance efficiency. Open Specy, initially aimed at manual single-spectrum analysis, has now integrated automated methods. This updated version, Open Specy 1.0, introduces several new features, including two algorithms for automated processing (smoothing and particle compression), an extensive library containing over 40,000 open-source Raman and FTIR spectra, and two machine learning classifiers (logistic regression and k medoids) developed from this library. Furthermore, it includes a revamped user interface, an R package, and a benchmark data set for testing future advancements in automated techniques. Researchers evaluated various configurations for hyperspectral smoothing, particle identification, compression, and splitting, to achieve combined recovery rates between 50 and 150% particle counts, identities, and sizes with a coefficient of variation (CV) of less than 40% (the accredited standard). Mean absorbance times the standard deviation provided a consistent particle identification. Hyperspectral smoothing led to a 96% combined recovery rate and reduced variability (CV = 38%) compared to the 86% recovery (CV = 83%) of nonsmoothed controls. Additionally, compressing spectra for particles was significantly faster (>3x) and showed similar accuracy but with reduced variability than processing each pixel individually. Key challenges persist in automating spectral analysis, particularly in refining particle splitting algorithms, and improving identification routines to minimize false positives and negatives. In conclusion, new methods in sample preparation for better stabilization and dispersion of particles could overcome some of these issues.

13 HYDRO ENERGY↗

DOE OSTI2023

Factors Influencing Adoption of Pooled Rideshare An Explorative Study on User-Centered Design and Services

Gangadharaiah, Rakesh↗

DOE OSTI2023

A User-Centered Design Exploration of Factors That Influence the Rideshare Experience

The rise of real-time information communication through smartphones and wireless networks enabled the growth of ridesharing services. While personal rideshare services (individuals riding alone or with acquaintances) initially dominated the market, the popularity of pooled ridesharing (individuals sharing rides with people they do not know) has grown globally. However, pooled ridesharing remains less common in the U.S., where personal vehicle usage is still the norm. Vehicle design and rideshare services may need to be tailored to user preferences to increase pooled rideshare adoption. Based on a large, national U.S. survey (N = 5385), the results of exploratory and confirmatory factor analyses suggested that four key factors influence riders’ willingness to consider pooled ridesharing: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. A binomial logistic regression was conducted to determine how the four factors influence one’s willingness to consider pooled ridesharing. The two factors that positively influence riders’ willingness to consider pooled ridesharing are vehicle technology/accessibility (B = 1.10) and convenience (B = 0.94), while lack of passenger safety (B = –0.63) and comfort/ease of use (B = –0.17) are pooled ridesharing deterrents. Understanding user-centered design and service factors are critical to increase the use of pooled ridesharing services in the future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Advances in scientific literature mining for interpreting materials characterization

Abstract Using synchrotron light sources, such as the National Synchrotron Light Source II at Brookhaven National Laboratory, scientists in fields as diverse as physics, biology, and materials science, identify the atomic structure, chemical composition, or other important properties of varied specimens. x-ray spectroscopy from light sources is particularly valuable for materials research with vast information available about reference spectra in the scientific literature. However, as the technique is applicable to many science domains, searching for information about select x-ray spectroscopy spectra is impeded by the sheer number of publications. Moreover, useful information about the context of an experiment or figures presented in papers can be buried among the details, which takes time to assess. This work presents a scientific literature mining system that supports data acquisition, information extraction, and user interaction for referencing x-ray spectra identification and spectral interpretation. The goal is to provide efficient access to useful spectral data to researchers who may spend only a few days at a synchrotron light source. With this system, users browse a classification tree for papers arranged according to x-ray spectroscopic methods, chemical elements, and x-ray absorption spectroscopy edges. Relevant figures are extracted with sentences from the paper that explain them, known as ‘figure explanatory text.’ Notably, this system focuses on semantic aspects (logical analysis) to find figure explanatory text using deep contextualized word embeddings techniques and contains an interface to obtain labeled data from domain experts that is used to evaluate and improve the model.

Park, Gilchan (ORCID:0000000201536646)↗

DOE OSTI2026

Implementing a unified solver for nonlinearly constrained optimization

SQP and interior-point methods (also referred to as Lagrange-Newton methods) typically share key algorithmic components, such as strategies for computing descent directions and mechanisms that promote global convergence. Building on this insight, we introduce a unifying framework with eight building blocks that abstracts the workflows of Lagrange-Newton methods. We then present Uno, a modular C++ solver that implements our unifying framework and allows the automatic combination of a wide range of strategies with no programming effort from the user. Uno is meant to (1) organize mathematical optimization strategies into a coherent hierarchy; (2) offer a wide range of efficient and robust methods that can be compared for a given instance; (3) enable researchers to experiment with novel optimization strategies; and (4) reduce the cost of development and maintenance of multiple optimization solvers. Uno’s software design allows user to compose new customized solvers for emerging optimization areas such as robust optimization or optimization problems with complementarity constraints, while building on reliable nonlinear optimization techniques. We demonstrate that Uno is highly competitive against state-of-the-art solvers filterSQP, IPOPT, SNOPT, MINOS, LANCELOT, LOQO, and CONOPT on a subset of 429 small problems from the CUTE collection. Uno is available as open-source software under the MIT license at https://github.com/cvanaret/Uno and via its C, Julia, Python, Fortran, and AMPL interfaces.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Conference Support for 2024 MagNetUS Meeting

The 4th annual MagNetUS meeting was held from April 14-18, 2024 in Los Angeles, California at the UCLA Lake Arrowhead Conference Center. The goal of the meeting was to continue the successful template of a mix of invited talks, contributed talks, posters, and discussion sessions established by the first three MagNetUS meetings, in order to 1) communicate physics advances relevant for basic magnetized plasma research which can be performed at small and mid-size experimental facilities; 2) allow collaborator facilities to connect and interact with existing users and potential new users; and 3) grow the community of potential users of collaborator facilities, as well as scientists who focus on theory/modeling and spacecraft observations. The meeting was attended by 77 participants (71 in person) from 23 unique institutions and included 14 invited talks, 19 contributed talks, 22 posters, and six discussion sessions. Topics covered by the meeting included fusion research, space and astrophysical systems (including laboratory analogs), magnetic reconnection, plasma thrusters, turbulence, waves, dusty plasmas, laser plasmas, plasma vortices, plasma sources, and software ecosystems. Discussion sessions included discussions on science communication, with NSF and DOE program managers, on the MagNetUS application process for new users, on experimental facility capabilities, and as a tie-in to the Solar Wind Machine meeting.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

How open data and interdisciplinary collaboration improve our understanding of space weather: A risk and resiliency perspective

Space weather refers to conditions around a star, like our Sun, and its interplanetary space that may affect space- and ground-based assets as well as human life. Space weather can manifest as many different phenomena, often simultaneously, and can create complex and sometimes dangerous conditions. The study of space weather is inherently trans-disciplinary, including subfields of solar, magnetospheric, ionospheric, and atmospheric research communities, but benefiting from collaborations with policymakers, industry, astrophysics, software engineering, and many more. Effective communication is required between scientists, the end-user community, and government organizations to ensure that we are prepared for any adverse space weather effects. With the rapid growth of the field in recent years, the upcoming Solar Cycle 25 maximum, and the evolution of research-ready technologies, we believe that space weather deserves a reexamination in terms of a “risk and resiliency” framework. By utilizing open data science, cross-disciplinary collaborations, information systems, and citizen science, we can forge stronger partnerships between science and industry and improve our readiness as a society to mitigate space weather impacts. The objective of this manuscript is to raise awareness of these concepts as we approach a solar maximum that coincides with an increasingly technology-dependent society, and introduce a unique way of approaching space weather through the lens of a risk and resiliency framework that can be used to further assess areas of improvement in the field.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Guiding the choice of informatics software and tools for lipidomics research applications

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

A Dynamic Pricing Method to Manage the Impact of EV Charging on the Grid Using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

EV charging, dynamic pricing, grid-informed chargi↗

DOE OSTI2025

A dynamic pricing method to manage the impact of EV charging on the grid using RL

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

TRACER-MAP Field Campaign Report

During July-August 2022, the U.S. Department of Energy (DOE) Atmospheric System Research (ASR)-funded Tracking Aerosol Convection Interactions Experiment (TRACER)-MAP campaign completed an Atmospheric Radiation Measurement (ARM) user facility field study to measure and map aerosol, volatile organic compounds (VOC), trace gas, and select meteorological observations across Houston, Texas. That ASR project and ARM field campaign, TRACER-MAP, was designed to complement measurements made at the first ARM Mobile Facility (AMF1) by the Aerosol Observing System (AOS) during the TRACER campaign. In this manner, TRACER-MAP effectively extended aerosol measurements from the TRACER AMF deployment, increasing the spatial coverage across the Houston metropolitan area, characterizing a greater diversity of source mixtures (e.g., industrial, traffic, residential, and biogenic) and capturing air masses with differential aging of urban source emissions (e.g., downtown versus downwind). That rich data set now offers many possibilities for in-depth analysis that align with the ASR mission to “research aerosol processes that affect Earth’s radiative balance and hydrological cycle.”

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Design and implementation of dynamic I/O control scheme for large scale distributed file systems

In this paper, we have analyzed the input/output (I/O) activities of Cori, which is a high-performance computing system at the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory. Our analysis results indicate that most users do not adjust storage configurations but rather use the default settings. In addition, owing to the interference from many applications running simultaneously, the performance varies based on the system status. To configure file systems autonomously in complex environments, we developed DCA-IO, a dynamic distributed file system configuration adjustment algorithm that utilizes the system log information to adjust storage configurations automatically. Our scheme aims to improve the application performance and avoid interference from other applications without user intervention. Moreover, DCA-IO uses the existing system logs and does not require code modifications, an additional library, or user intervention. To demonstrate the effectiveness of DCA-IO, we performed experiments using I/O kernels of real applications in both an isolated small-sized Lustre environment and Cori. Our experimental results shows that our scheme can improve the performance of HPC applications by up to 263% with the default Lustre configuration.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Collaborative Study for Certification of Trace Elements in Uranium Ore Concentrate CRMs UCLO-1, UCHI-1, and UPER-1

Trace impurity patterns are important nuclear forensic signatures in uranium ore concentrates (UOCs) and Certified Reference Materials (CRMs) are used to validate the analysis methods employed by end users. Herein, we discuss the certification campaign for three new UOC CRMs from the National Research Council Canada: UCLO-1 (https://doi.org/10.4224/crm.2020.uclo-1), UCHI-1 (https://doi.org/10.4224/crm.2020.uchi-1), and UPER-1 (https://doi.org/10.4224/crm.2020.uper-1). This study involved 15 laboratories from 10 countries, using sector-field and (triple) quadrupole inductively-coupled plasma mass spectrometry to analyze 64 trace element impurities. We discuss the importance of the acids used for sample digestion, difficulties analyzing in a high uranium matrix, and data combination and uncertainty evaluation for this large dataset.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2020

GradeIT (Road Grade Inference Tool)

The National Renewable Energy Laboratory (NREL) has developed the Road Grade Inference Tool, "GradeIT". This tool is a modular Python package the leverages the United States Geological Survey’s (USGS) 1/3 arc-second Digital Elevation Model (DEM), an open and free public dataset. Roadway gradient (slope) has a strong influence on vehicle energy consumption behavior and longitudinal dynamics. Therefore, any tools, models, or analyses that consider vehicle energy consumption or driving profiles is incomplete without accurate elevation and grade information. Despite the acute need for this data, it is rarely collected alongside GPS (latitude and longitude) data, and tools to post-process GPS data to append elevation and grade are difficult to come by. GradeIT satisfies this significant need in the research community. Utilization of the USGS DEM makes the underlying data available to any potential user and ensures continuous data anywhere in the continental United States. The USGS DEM is a great resource; however, in order to be useful for automotive and traffic engineering applications, significant interpolation and filtering is required to achieve an accurate representation of gradient on the roadway. GradeIT employs a two-dimensional interpolation to approximate elevation at a point on the road from the nearest nodes in the DEM. Then, GradeIT applies multiple filtering techniques to smooth the elevation profile to a realistic road topology. Finally, road grade is derived from the elevation profile and returned as the primary output.

Holden, Jacob↗

DOE OSTI2021

Role-Based Access Control for Distributed Energy Resources v.1.0

The Role-Based Access Control (RBAC) for Distributed Energy Resources (DER) software is an automated and transparent stack for RBAC administrators to interact with RBAC policy. The package includes demonstrations of centralized and decentralized RBAC implementation. Both include simulated DER entities that are stored automatically in a centralized Lightweight Directory Access Protocol (LDAP) server. It also includes a REST-API to serve the HTTP requests of the RBAC administrators. A complete front-end is included to serve the users' needs. This software may be used by industry (DER vendors/utilities) as well as the research community to determine if a centralized or decentralized solution fits better the needs of an RBAC model in a DER environment. Additionally, parts of this software may be useful for developing implementations for specific needs. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-8292 O

Johnson, Jay↗

DOE OSTI2025

DiMER

SAND2025-04145O DiMER is a Python based tool that helps researchers understand the functions of genes by searching through multiple biological databases. It takes user-provided data and scans various databases to find the best matches for gene functions, generating a clear summary of results. DiMER identifies the most relevant functional annotations and improves upon previous annotations by replacing instances of "unknown protein function" with more accurate descriptions. DiMER requires minimal setup. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Mageeney, Catherine [Sandia National Lab. (SNL-CA)↗

DOE OSTI2025

Calculating topological properties of artificial graphene in B-field

SAND2025-03258O Calculating topological properties of artificial graphene in B-field is a user-friendly tool designed to analyze artificial graphene systems influenced by magnetic fields. It helps researchers understand the unique properties of these materials by calculating the local Chern marker, a key indicator of their behavior. With just one file, this software runs easily on any device with Matlab, making it accessible for scientists and engineers. It provides valuable insights into the electronic characteristics of artificial graphene, which supports advancements in material science and technology, paving the way for innovative applications in electronics and beyond. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Spataru, Dan [Sandia National Lab. (SNL-CA), Liver↗