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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

VerifyIO: Ensuring Correctness of Consistency Semantics in Parallel I/O

Abstract—High-performance computing (HPC) applications generate and consume substantial amounts of data, typically managed by parallel file systems. These applications access file systems either through the POSIX interface or by using highlevel I/O libraries. While the POSIX consistency model remains dominant in HPC, emerging file systems and popular I/O libraries increasingly adopt alternative consistency models that relax semantics in various ways, creating significant challenges for correctness and portability. This paper addresses these challenges by proposing a trace-driven I/O consistency verification workflow, implemented in our open-source tool, VerifyIO, which collects execution traces, detects data conflicts, and verifies proper synchronization against specified consistency models. Our extensive evaluation of 91 test case executions across three widely used I/O libraries with four I/O consistency models reveals critical consistency issues at both application and implementation levels.

Consistency Semantics↗
DOE OSTI2025

Thermal Hydraulics Analysis of a Divertor Monoblock Using SALAMANDER

Divertors are critical components in magnetic confinement fusion devices. One of the Divertor's crictial roles is absorbing the highest heat flux from the plasma. The Divertor accomplishes this through the use of divertor monoblocks and cooling channels. This work presents a thermal hydraulic analysis of a divertor monoblock using the Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering and Research (SALAMANDER). SALAMANDER is an open source tool developed at Idaho National Laboratory for conducting multiphysics and multscale analysis of Plasma Facing Components (PFCs). For the monoblock, solid heat conduction is modeled in the block with a constant heat flux of 1E7 W/m^2 applied to the top of the block to represent peak heat flux from the plasma. The cooling channel (which is pressurized water at a Reynolds number of ~1,000,000) is modeled using a k-epsilon turbulence model with standard wall functions. The heat transfer between the solid and fluid domain is modeled using the Dittus-Boelter correlation for the convective heat transfer coefficient. This work aims to show the importance of a multiphysics modeling approach when performing simulations on PFCs.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Detecting and Analyzing Power System Disturbances in PMU Data with the Open-Source Archive Walker Tool

As the number of Phasor Measurement Units (PMUs) deployed in power systems increases, so do the archives of synchrophasor measurements stored by utilities. Significant value can be extracted from these archives by using them to support studies such as frequency response analysis, small-signal stability analysis, and power plant model validation. To better enable utilities to perform these studies, the open-source Archive Walker tool was developed. This tool examines PMU data for grid disturbances and periods of interest. Once identified, the data can be further analyzed inside Archive Walker or exported to specialty tools. In this paper, the various disturbance detectors available in Archive Walker are described, and its interactions with other openly-available tools are highlighted.

Follum, James D.↗
DOE OSTI2021

U.S.-China Clean Energy Research Center Building Energy Efficiency (CERC-BEE) Open-Source Retrofit Targeting Tool (CRADA FP00007338 Final Report)

To increase the cost-saving energy and carbon dioxide (CO 2 ) emissions reductions in buildings and portfolios at the scale and speed necessary to limit climate change, researchers at LBNL and Johnson Controls (JCI) developed the Building Efficiency Targeting Tool for Energy Retrofits (BETTER). BETTER is a software tool that consists of three components: (1) the BETTER analytical engine source code (which was developed with intellectual property provided by JCI under CRADA FP00007338); (2) the BETTER web application, developed by LBNL and McQuillen Interactive Pty. Ltd; and (3) the BETTER application programming interface (API), also developed by LBNL and McQuillen Interactive Pty. Ltd. BETTER enables building and portfolio owners, managers, and service providers worldwide to quickly, easily identify cost-saving energy efficiency retrofits in existing buildings and portfolios without expensive site visits or complex modeling. With minimal data input, the tool benchmarks a building’s electric and fossil energy usage against peers; quantifies energy, cost and greenhouse gas (GHG) emission reduction potentials at the building and portfolio levels; and recommends energy efficiency measures to decarbonize and electrify buildings and portfolios, targeting specific energy savings levels. No other tool so comprehensively analyzes buildings and portfolios with such ease. If fully implemented, it is estimated that BETTER could help reduce emissions equivalent to planting 1.3 billion trees globally by 2030. Moreover, an additional 50-75% of embodied GHG emissions could be avoided in each case where BETTER results in a building being retrofitted instead of demolished and replaced, providing substantial additional decarbonization benefits for the buildings sector. BETTER has garnered multiple awards and avid interest from investors. In 2020, it earned a R&D 100 Award for innovation and a LBNL Director’s Award for Technology Transfer. In 2021, BETTER was named an EarthX E-Capital Summit Climate Tech Prize semi-finalist

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

An Open-source Llm Enhanced-tool Specialized In Helping Moose Related Problems And Tasks

MOOSEenger is an open-source, terminal-first chat application for the MOOSE ecosystem that couples specialized parsing of MOOSE documentation and “.i” input files with retrieval-augmented generation to deliver grounded answers about multiphysics modeling and workflows. It includes dedicated readers for MOOSE-style HTML and a pyhit-based parser that uses the MOOSE syntax tree to preserve block structure and attach retrieval metadata. A data-ingestion pipeline performs semantic chunking into atomic facts and stores them hierarchically in a local Chroma vector database that maintains parent–child relationships across documents; the system can ingest directories, individual files, and single-page web content, and it provides CRUD operations (insert, update, delete) to manage the corpus. At query time, relevant chunks are embedded, retrieved, and fused into the model context, with interactive features such as token streaming, persistent chat history, and dynamic RAG (retrieval triggered by user input or intermediate model output). Deployment is flexible: MOOSEenger runs with local Ollama models or remote Hugging Face/OpenAI backends—typically coordinating generation, lightweight tagging/summarization, and embeddings across three models—and it also supports a server mode and integration with the VS Code Continue interface.

Li, Mengnan [Idaho National Laboratory (INL), Idah↗
DOE OSTI2024

Catching Rays: How Bifacial_Radiance Sheds Light on the Future of Solar PV

The challenge of energy transition is immediate and immense, with current projections targeting 75 TW of photovoltaics (PV) capacity globally by 2050. Alongside the rapid deployment is the "solar-coaster" ride the PV industry experiences with evolving technologies and novel installation methods. In 2016, NREL developed bifacial_radiance, a python open-source modeling tool for bifacial PV. This tool is a wrapper of the raytracing engine Radiance, which you all know better than us at this workshop. Bifacial_radiance integrates the many characteristics of common PV systems to model irradiance on both the front and rear sides of bifacial PV technology - a technology that now represents 75% of utility-scale deployment in the US. Bifacial_radiance has been pivotal for understanding bifacial system performance, shading, and edge effects, and now agrivoltaics research. It has also helped develop simplified models used in PV due diligence tools for optimizing new deployments or evaluating the performance of existing projects. Now, it's the go-to comparison tool for many university, and industry-developed systems modeling tools, and a pivotal tool for further research in photovoltaics. This talk will cover the needs bifacial_radiance addresses as an open-source tool, its development path, and the opportunity for any raytracer to shine light on the solar industry through research and practical application of modeling in regular site installations and novel setups like agrivoltaics and vertical panels at high latitudes (and even the South Pole!).

agrivoltaics↗
DOE OSTI2025

Blending Pipeline Analysis Tool for Hydrogen (BlendPATH) Documentation and User Manual

The Blending Pipeline Analysis Tool for Hydrogen (BlendPATH) is a flexible, open-source Python tool designed to provide users with case-by-case analysis capabilities to identify the necessary modifications to repurpose existing natural gas transmission pipeline networks to transport hydrogen as a blend of a user-specified volume fraction of hydrogen or as a pure stream and to estimate the associated capital and operating expenditures resulting from those modifications. This tool is intended to be applied during the initial screening stage of a prospective project when pipeline developers compile transmission pipeline technical documentation and history but prior to performing detailed pipeline inspections. Performing analysis with BlendPATH during this initial screening stage can provide the user with an understanding of promising opportunities and probable economic outcomes of repurposing their pipeline network for hydrogen before proceeding with detailed pipeline materials testing and pipeline inspections. BlendPATH consists of multiple modules to simulate, assess, and modify existing natural gas transmission pipeline network designs to be compatible with hydrogen as a blend or pure stream. The tool employs an open-source gas network hydraulic model to simulate existing, user-specified transmission pipeline networks, and it applies ASME B31.12 to assess the pipe segments within these networks for compatibility with hydrogen and to modify the networks to achieve compatibility where the existing infrastructure is inadequate. Users can specify ASME B31.12 design options for pipeline assessment and can select from multiple methods for pipeline modification. This report details the functionalities of BlendPATH Version 2.0.2 and demonstrates an example of applying BlendPATH to a case study. Potential and intended users of this framework include natural gas pipeline developers and operators and researchers at both public and private institutions. BlendPATH is publicly available at https://github.com/NREL/BlendPATH.

08 HYDROGEN↗
DOE OSTI2024

Hector-FACTS Technical Report & User Guide: Integrating Hector into the Framework for Assessing Changes To Sea-level

The Framework for Assessing Changes To Sea-level (FACTS) platform was developed by (Kopp et al., 2023) as an open-source modular tool capable of generating probabilistic projections for global mean sea-level change and future relative and extreme sea-level change at New York City (Kopp et al., 2023). FACTS is the first open-source modular tool designed to explore deep uncertainty in SLR while also synthesizing across different modeling groups and integrating the various components of SLR used in key climate reports.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

GeoThermalCloud: A Machine Learning Tool for Discovery, Exploration, and Development of Hidden Geothermal Resources

In this 25 minute presentation, we showcase our open source “GeoThermalCloud” tool for identifying hidden geothermal resources using a publicly available dataset for southwestern New Mexico. The presenters include Bulbul Ahmmed and Luke Frash. All of the visuals use source material from LA-UR approved publications and this work falls under the Earth Sciences DUSA. The code shown in this video is already released with LANL approval in open source format on GitHub and DockerHub. The audio in this video includes only material on the topics of geothermal energy and machine learning applied to geothermal energy. The primary machine learning method used is LANL’s Non-negative Matrix Factorization “NMFk” method. Modeling work also mentions LANL’s Geothermal Design Tool “GeoDT” which is another approved open source code that has been released by LANL. This work was performed for DOE Geothermal Technologies Office (DE-EE-3.1.8.1). The host for the released video is intended to be YouTube or a suitable perpetual data repository such as GDR.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Automating Traffic Microsimulation from SYNCHRO UTDF to SUMO

Modern transportation research relies on seamlessly integrating traffic signal data with robust network representation and simulation tools. This study presents utdf2gmns, an open-source Python tool that automates conversion of the Universal Traffic Data Format, including network representation, signalized intersections, and turning volumes into the General Modeling Network Specification (GMNS) Standard. The resulting GMNS-compliant network can be converted for microsimulation in SUMO. By automatically extracting intersection control parameters and aligning them with GMNS conventions, utdf2gmns minimizes manual preprocessing and data loss. utdf2gmns also integrates with the Sigma-X engine to extract and visualize key traffic control metrics, such as phasing diagrams, turning volumes, volume-tocapacity ratios, and control delays. This streamlined workflow enables efficient scenario testing, accurate model building, and consistent data management. Validated through case studies, utdf2gmns reliably models complex urban corridors, promoting reproducibility and standardization. Documentation is available on GitHub and PyPI, supporting easy integration and community engagement.

Luo, Roy [ORNL] (ORCID:0009000312909983)↗
DOE OSTI2022

Development of an open-source segmented blade design tool

As wind turbines continue to grow ever larger to reduce the cost of energy, their blades follow suit, with the largest commercial offshore blades extending past 100 m. Massive blades such as these raise key transportation and manufacturing challenges, especially for land-based turbines. Segmented blades are one solution and are garnering increased industry and research interest. In this work, a detailed mechanical joint model is integrated into the Wind-Plant Integrated System Design and Engineering Model (WISDEM ® ), which will facilitate future segmented blade research and optimization. WISDEM is used to design a wind turbine with 100-m segmented blades. This wind turbine design is compared to other machines with 100-m monolithic blades designed for rail-transportability. The designs are compared in terms of blade mass and cost, turbine capital cost, annual energy production, and levelized cost of energy, with monolithic designs being the lightest and most economical. However, this result may vary by wind plant location. A variety of segmentation joint types exist, and they will inevitably vary in parameters such as cost, spanwise location, and physical characteristics. This work examines the sensitivity of wind turbine design drivers and annual energy production to a variety of the aforementioned parameters, using the open-source wind turbine design codes OpenFAST and WISDEM, finding that joint mass, stiffness, and location can have significant effects on design drivers.

17 WIND ENERGY↗
DOE OSTI2026

Understanding Isomeric Effects on Properties of Aviation Fuels via a Group Contribution Method: Preprint

The molecular composition of aviation fuels, including conventional and sustainable aviation fuels (SAFs), significantly influences their performance, safety, and environmental impact. This study examines the effect of isomeric variation for compounds with the same carbon number and chemical family on key fuel properties, focusing on compounds commonly found in conventional jet fuels and SAFs. A group contribution method (GCM) is employed to predict thermophysical and combustion properties, providing an efficient analytical approach to evaluate the contributions of individual compounds to overall fuel mixture behavior. As part of this work, we introduce FuelLib, an open-source Python tool built around the GCM, to calculate individual compound and fuel mixture properties using various mixing rules. Our work evaluates whether the GCM can capture isomeric effects, that are often overlooked in traditional fuel property estimation. This is particularly important for SAFs, which often are composed of a more limited set of compound classes than conventional fuels, making isomeric differences more critical. Two-dimensional gas chromatography (GCxGC) data, which can be obtained from small fuel samples, provides weight percentages of compounds grouped by chemical family and carbon number rather than detailed information about individual compounds. As a result, assumptions must be made when decomposing GCxGC data into functional groups for GCM applications. Using GCxGC data, we show that the FuelLib tool can be used to predict the fuel properties of conventional jet fuels, with validation against experimental data. The provided tool enables researchers to predict fuel properties of candidate fuels and supports the design of new SAFs at both the component and mixture levels. This capability provides a foundation for studying fuel and combustion properties during SAF development, reducing reliance on costly experimental methods and advancing progress toward certification of new SAFs.The molecular composition of aviation fuels, including conventional and sustainable aviation fuels (SAFs), significantly influences their performance, safety, and environmental impact. This study examines the effect of isomeric variation for compounds with the same carbon number and chemical family on key fuel properties, focusing on compounds commonly found in conventional jet fuels and SAFs. A group contribution method (GCM) is employed to predict thermophysical and combustion properties, providing an efficient analytical approach to evaluate the contributions of individual compounds to overall fuel mixture behavior. As part of this work, we introduce FuelLib, an open-source Python tool built around the GCM, to calculate individual compound and fuel mixture properties using various mixing rules. Our work evaluates whether the GCM can capture isomeric effects, that are often overlooked in traditional fuel property estimation. This is particularly important for SAFs, which often are composed of a more limited set of compound classes than conventional fuels, making isomeric differences more critical. Two-dimensional gas chromatography (GCxGC) data, which can be obtained from small fuel samples, provides weight percentages of compounds grouped by chemical family and carbon number rather than detailed information about individual compounds. As a result, assumptions must be made when decomposing GCxGC data into functional groups for GCM applications. Using GCxGC data, we show that the FuelLib tool can be used to predict the fuel properties of conventional jet fuels, with validation against experimental data. The provided tool enables researchers to predict fuel properties of candidate fuels and supports the design of new SAFs at both the component and mixture levels. This capability provides a foundation for studying fuel and combustion properties during SAF development, reducing reliance on costly experimental methods and advancing progress toward certification of new SAFs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

The Coastal Carbon Library and Atlas: Open source soil data and tools supporting blue carbon research and policy

Abstract Quantifying carbon fluxes into and out of coastal soils is critical to meeting greenhouse gas reduction and coastal resiliency goals. Numerous ‘blue carbon’ studies have generated, or benefitted from, synthetic datasets. However, the community those efforts inspired does not have a centralized, standardized database of disaggregated data used to estimate carbon stocks and fluxes. In this paper, we describe a data structure designed to standardize data reporting, maximize reuse, and maintain a chain of credit from synthesis to original source. We introduce version 1.0.0. of the Coastal Carbon Library, a global database of 6723 soil profiles representing blue carbon‐storing systems including marshes, mangroves, tidal freshwater forests, and seagrasses. We also present the Coastal Carbon Atlas, an R‐shiny application that can be used to visualize, query, and download portions of the Coastal Carbon Library. The majority (4815) of entries in the database can be used for carbon stock assessments without the need for interpolating missing soil variables, 533 are available for estimating carbon burial rate, and 326 are useful for fitting dynamic soil formation models. Organic matter density significantly varied by habitat with tidal freshwater forests having the highest density, and seagrasses having the lowest. Future work could involve expansion of the synthesis to include more deep stock assessments, increasing the representation of data outside of the U.S., and increasing the amount of data available for mangroves and seagrasses, especially carbon burial rate data. We present proposed best practices for blue carbon data including an emphasis on disaggregation, data publication, dataset documentation, and use of standardized vocabulary and templates whenever appropriate. To conclude, the Coastal Carbon Library and Atlas serve as a general example of a grassroots F.A.I.R. (Findable, Accessible, Interoperable, and Reusable) data effort demonstrating how data producers can coordinate to develop tools relevant to policy and decision‐making.

Holmquist, James R.↗
DOE OSTI2025

Open Source Software Prevalence Ingest Tool

The OSSP Ingest Tool accepts user-input organizational information, ingests IT/OT asset lists in Excel format, and ingests the associated CycloneDX SBOM's. It then performs analytics demonstrating the ability to answer the follow research questions: o RQ1. Ability to identify all OSS services running on, and all OSS components present within, an OT device o RQ1a: Ability to differentiate multiple versions of the same OSS component within each OT device. o RQ1b: Ability to differentiate running from not-running OSS components. o RQ1c: Ability to differentiate based on the originator of the component, because a supplier may have modified it after retrieval from the upstream software source. o RQ2. Ability to correlate the identity of a single OSS component across multiple OT devices, mitigating common name variations such as differences in capitalization, '-' vs '_', and so on. o RQ3. Ability to perform subset analysis of OSS components across multiple OT devices o RQ3a: Ability to perform subset analysis across OSS libraries, generating density & distribution graphs to identify commonly-used libraries and outliers. o RQ3b: Ability to perform subset analysis of a single OSS library, generating density & distribution by CI sector, by device type, by device make/model, and/or by firmware version. o RQ3c: Ability to perform subset analysis by grouping OSS libraries according to programming language, then overlay with RQ4b. o RQ3d: Ability to perform subset analysis by OSS upstream source, providing insight into degree of modifications performed by suppliers. o RQ4. Ability to identify dependencies (transitive and direct) of each differentiated OSS library within each OT device, and enable RQ1,2,3 iteratively for dependencies. o RQ1. Ability to identify all OSS services running on, and all OSS components present within, an OT device o RQ1a: Ability to differentiate multiple versions of the same OSS component within each OT device. o RQ1b: Ability Page

Kapadia, Shayna [Lawrence Livermore National Labor↗