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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

PNNL Soil Microbiome SFA

PNNL's Soil Microbiome Science Focus Area (SFA) is focused on understanding the basic biology underpinning how interactions among various soil microbial community members, across trophic levels, lead to the emergence of community functions. Moisture, in particular, drives microbial interactions and influences everything from cell function to substrate fate within soils. The group predicts this results in repeatable, predictable phenotypes. The sum of these phenotypes comprises the “soil metaphenome”. Understanding how the soil metaphenome shifts in response to moisture will provide a basis for modeling and predicting these shifts in reaction network responses. PNNL’s Soil Microbiome SFA project repository on DataHub allows for exploring and downloading integrated experimental omics data, experimental metadata, pre- and post-processed data files, and other associated materials directly related to experimental project publication data. Visit the PNNL Soil Microbiome Science Focus Area Program homepage for more information.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Calibrating radar wind profiler reflectivity factor using surface disdrometer observations

Abstract. This study uses surface disdrometer reflectivity factor estimates to calibrate the vertical and off-vertical pointing radar beams produced by an ultra high frequency (UHF) band radar wind profiler (RWP) deployed at the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) Central Facility in northern Oklahoma from April 2011 through July 2019. The methodology consists of five steps. First, the recorded Doppler velocity power spectra are adjusted to account for Nyquist velocity aliasing and coherent integration filtering effects. Second, the spectrum moments are calculated. The third step increases the signal-to-noise ratio (SNR) due to inflated noise power estimates during convective rain events that cause SNR to be biased low. The fourth step determines the RWP calibration constant for one radar beam (called the “reference” beam) by comparing uncalibrated RWP reflectivity factors at 500 m above the ground to 1 min resolution surface disdrometer reflectivity factors. The last step uses the calibrated reference beam reflectivity factor to calibrate the other radar beams during precipitation. There are two key findings. The RWP sensitivity decreased by approximately 3 to 4 dB yr−1 as the hardware aged. This drift was slow enough that the reference calibration constant can be estimated over 3-month intervals using episodic rain events. The calibrated moments are available on the DOE ARM data archive, and the Python processing code is available on public repositories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Status Report on Thermal Delivery Modeling in HYBRID

This report details the modeling efforts consistent with the HYBRID repository of the Integrated Energy Systems (IES) program, motivated to develop information data tables characterizing the thermodynamic losses associated with transporting thermal energy over large distances. The described model establishes a methodology to currently characterize the impacts of certain parameters such as distance, piping geometry, and heat transfer fluid among others as well as sets up ability to interconnect the delivery model into Modelica IES simulations in the future. This report describes the modeling efforts completed and describes some of the future studies that will be done using results produced by the modeling efforts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

BESTEST-GSR (Building Energy Simulation Test - Generation Simulation and Reporting) 2023 [SWR 18-23]

Building Energy Simulation Test (BESTEST) is an NREL-developed method to validate the qualitative performance of different whole building simulations engines relative to each other. https://www.nrel.gov/docs/legosti/old/6231.pdf The purpose of this repository is to generate BESTEST test case models, run simulations, and populate data for ASHRAE Standard 140 reporting spreadsheets for EnergyPlus® based whole building simulation tools. It was originally setup for 2014 version of Standard 140. In May of 2022 it was updated to the 2020 version of Standard 140. This update included updates and additions to existing test suites, the bulk of which was in Section 5.2 (Building Thermal Envelope and Fabric Load Tests). We did not add Section 5.5 Airside HVAC Equipment Performance, but we hope to add that later in 2022. At some point we also hope to add Section 5.2.4 ground modeling, which is currently excluded. Supported Tools The default IDF generation is based on the OpenStudio® CLI, but the workflow supports a 'Bring your own IDF' use case. Additionally, for non-EnergyPlus® based tools the post processing scripts can be used if simulation results are provided as a CSV file.The scripts on this repository should work on Mac, Windows, and Linux. Dependencies Install OpenStudio® 3.4.0 make sure command line can recognize the 'openstudio' command This includes EnergyPlus® 22.1 Install Ruby on your system if it isn't already setup. 2.7 is used for development but other versions may work Since OpenStudio has its own embedded Ruby, which is used for running measures, you don't necessarily have to use a version of Ruby supported by OpenStudio. Install RubyXL Ruby gem This is used to modify Microsoft Excel spreadsheets Install Parallel Ruby gem This allows the CLI to run simulations in parallel

Goldwasser, David↗
DOE OSTI2020

NCSP Analytical Methods Subtask 3, AMPX Development and Maintenance, and NCSP Nuclear Data Subtask 6, SAMMY Modernization

The modernization of SAMMY continued with the removal of all container array access for the main program. The container array was used due to the computer limitations at the time of the initial writing of SAMMY, but it prevented the use of some modern code testing options. Additionally, the authors continued to make the code more modular by shifting the storage of the energy grid and the covariance information to C++ storage, thus allowing for better unit testing and the elimination of scratch files. For AMPX, the authors continued to implement the new Evaluated Nuclear Data File format to allow AMPX to read new evaluations as they become available.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Web of Registries Search (WoRS) v1.0.0

The Web of Registries Search (WoRs) is a web based software application that enables users to search for publicly available biological parts using keywords or sequence fragments. For the initial version (1.0.0) of the application, WoRS targets 10 sources of biological part data: the GenBank NIH genetic sequence database (https://www.ncbi.nlm.nih.gov/genbank/), the iGem parts registry (parts.igem.org), the Addgene plasmid repository (https://www.addgene.org/), and 7 Inventory of Composable Elements (ACS Synbio, JGI, JBEI, JBEI Public, ABF, SynBerc, ABF Public) registry instances. WoRs has built-in automated web scrapers which extract data from sources that do not have a public or well-defined application programming interface (API). They extract as much public data as they can find and create a searchable index to speed up searches. Included in the indexed information is the source of the information.

Plahar, Hector↗
DOE OSTI2026

Data from: Lowland Tropical Forests Remain a Methane Sink Under Warming and Long-Term Hurricane Disturbance Recovery

The repository folder contains spreadsheets and script for soil greenhouse gas (GHG) fluxes, soil moisture, soil temperature, air temperature, and precipitation measurements collected from the Tropical Responses to Altered Climate Experiment (TRACE) at the Sabana Research Field Station, El Yunque National Forest (USDA Forest Service; 18°19′28.74″ N, 65°43′50.09″ W) — an open-air field warming experiment located in a lowland tropical forest in Puerto Rico within the Luquillo Experimental Forest (LEF) — six to seven years after Hurricanes Irma and Maria (2017). All spreadsheets for soil and air microclimate data, as well as soil greenhouse gas data, are included as csv files. Air temperature data are also included as Excel spreadsheets (.xlsx). The script is built in R Studio, which is the only software required to run data analysis. This dataset is associated with the manuscript “Larocca Conte G ; Zuvela L ; Cruz-Pérez R ; Barreto-Vélez T ; Becerra-Santillan N ; Campbell S ; Chu H ; Dam T ; Grullón-Penkova I ; Kleit M ; Ortiz-Iglesias D ; Rubio-Lebrón L ; Cavaleri M ; Reed S ; Sihi D ; Wood T ; O'Connell C., 2026. Lowland Tropical Forests Remain a Methane Sink Under Warming and Long-Term Hurricane Disturbance Recovery. Agricultural and Forest Meteorology. In review". The dataset was used to test the effect of warming on soil CH4 dynamics following long-term legacy effects of hurricane disturbance. The dataset includes: - An overall README file in word and pdf format describing methodology and spreadsheets’ structure. - Continuous measurements of soil temperature and moisture from January 2023 to July 2024 measured with Campbell CS655 probes (“TRACE_soil_temperature_and_moisture_2023_cleaned(in).csv” and “TRACE_soil_temperature_and_moisture_2024_cleaned. csv”). - Air temperature data measured with a HOBO MX23O1A data logger (“Hobo air temperature 2023 Sep 2024” and “Hobo air temperature 2023 Sep 2024” – “CSV FILES folders”). - Precipitation data from a nearby weather tower downloaded from González et al. (2025; “sabana_2020-2025.csv”). - Soil CH4 and CO2 effluxes measured intermittently in two summer campaigns (June – August 2023 and June – July 2024) with a LI-COR 8200-01S Portable Smart Chamber coupled with a LI-COR LI-7810 CH4/ CO2/H2O Trace Gas Analyzer (“23_24COMBO2.0.csv”). - R markdown script for data analysis (“Trace new_PLOTS.Rmd”).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data and Code for Atomic Scale Etching of Diamond: Insights from Molecular Dynamics Simulations

This work investigates the effects of argon ions, hydrogen atoms, and hydrogen ions on the diamond (100) surface using classical molecular dynamics simulations. The purpose of this investigation was to asses plasma processing techniques for applications in quantum device manufacturing. The simulations suggest that combining argon ion smoothing with selective, near threshold energy H removal of amorphous C could be an effective strategy for diamond surface engineering, leading to more reliable and sensitive diamond color center devices. Results were found to differ significantly with interatomic potential, and an analysis of these differences was also carried out. Included in this repository are LAMMPS source files, input scripts, and plotting scripts required to reproduce the data. Also included are the output data required to make all the plots included in the associated publication.

Brenner↗
DOE OSTI2026

Data for KETCHUP: Parameterizing of Large-Scale Kinetic Models Using Multiple Datasets with Different Reference States

Repository for Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP can use K-FIT [2] input files. Example K-FIT input files are located in the K-FIT repository at https://github.com/maranasgroup/K-FIT.

Metabolomics↗
DOE OSTI2023

Azure Data Tools (FKA: OEDI (Open Energy Data Initiative) Data Access Tools) [SWR-23-92]

The Open Energy Data Initiative (OEDI) provides a number of tools to enable the use of the open data published through this initiative. The source is largely written in Python, including Jupyter notebooks. The Open Energy Data Initiative (OEDI) is a partnership between the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), Amazon, Microsoft, and Google to provide universal access to big data in the cloud. At the heart of OEDI is a centralized repository of high-value energy research datasets aggregated from DOE Program Offices, National Laboratories and other collaborators.

Jonathan, Weers↗
DOE OSTI2025

building-physics/refrigeration-system

The existing refrigeration system for OpenStudio was originally designed for a prototype supermarket model. To support the development of new modular space types, it is crucial to refactor the system. This process includes refining the design by utilizing the manufacturer's existing data, along with incorporating the latest data from manufacturers, to enhance the system’s adaptability and functionality. This repository currently contains the refrigeration system database and Python-based automation for generating OpenStudio-compatible refrigeration JSON files.

Bae, Yeonjin [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2026

DDCP framework

DDCP protocol software 1.0 This repository contains the C++ implementation of version 1.x of the Distributed Data Communications Protocol (DDCP). DDCP provides request/reply, feature discovery, data transfer, control, interrupt, and transaction support for communicating with accelerator instrumentation over UDP. The standard server port is 65000. The framework is a source dependency for services that communicate directly with DDCP hardware. It is not a deployable service by itself.

Joshi, Shreya [Fermi National Accelerator Laborato↗
DOE OSTI2026

Data and scripts associated with “Non-random processes impacting organic matter chemistry are maximized in mid-order streams”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the publication “Non-random processes impacting organic matter chemistry are maximized in mid-order streams” submitted to Limnology and Oceanography (L&O) by Danczak et al. (in review). This package contains data and scripts used to investigate dissolved organic matter (DOM) molecular chemistry and diversification processes across 47 surface-water sampling sites in the Yakima River Basin, Washington, USA, during an August 2021 sampling campaign. The package contains analyses of ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), geochemical measurements, geospatial attributes, molecular diversity, and meta-metabolome ecological null models needed to reproduce the main manuscript results. The underlying field data were pulled from exising data packages at https://doi.org/10.15485/1892052 (Fulton et al., 2022) and https://doi.org/10.15485/1898914 (Grieger et al., 2022). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. We thank the following organizations for providing access to field locations for sample collection: the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, the Confederated Tribes and Bands of the Yakama Nation, and the Cowiche Canyon Conservatory. Research was conducted under Washington State Parks and Recreation Commission Scientific Research Permit #210901. We are grateful to the Yakama Nation Tribal Council and Yakama Nation Fisheries for their collaboration in facilitating sample collection and ensuring data usage aligns with their values and worldview. This data package contains an R-Markdown file for analyses and five folders: (1) Data, (2) Geospatial Data, (3) Supplemental_Files, (5) Figures_pdf, (4) and src. The Data folder contains tabular inputs and derived files used in the manuscript analysis. The Geospatial Data folder contains climate and water-balance, hydrologic, land-cover, population/regional water-use, stream, topographic, and stream-order attribute CSV files. The src folder contains scripts used to process data, run analyses, and generate figures. The Figures_pdf folder contains manuscript figure outputs. The Supplemental_Files folder contains supplemental analysis products. All files are .csv, .pdf, .html, .png, .R, .Rmd, .svg, or .tre. This data package is associated with the rcfsa-RC2-SPS_Null_Modeling repository found at https://github.com/river-corridors-sfa/rcfsa-RC2-SPS_Null_Modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework and fictional input data, and a genetic optimization is included which determines optimal flash plant parameters. The inputs and outputs associated with the forecast and genetic optimization are included. The input and output files consist of data, configuration files, and plots. A link to the Physics-Guided Neural Networks (phygnn) GitHub repository is also included, which augments a traditional neural network loss function with a generic loss term that can be used to guide the neural network to learn physical or theoretical constraints. phygnn is used by the GOOML framework to help integrate its machine learning models into the relevant physics and engineering applications. Note that the data included in this submission are intended to provide a demonstration of GOOML's capabilities. Additional files that have not been released to the public are needed for users to run these models and reproduce these results. Units can be found in the readme data resource.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Cancer genomics predicts disease relapse and therapeutic response to neoadjuvant chemotherapy of hormone sensitive breast cancers

Several studies provide insight into the landscape of breast cancer genomics with the genomic characterization of tumors offering exceptional opportunities in defining therapies tailored to the patient’s specific need. However, translating genomic data into personalized treatment regimens has been hampered partly due to uncertainties in deviating from guideline based clinical protocols. Here we report a genomic approach to predict favorable outcome to treatment responses thus enabling personalized medicine in the selection of specific treatment regimens. The genomic data were divided into a training set of N = 835 cases and a validation set consisting of 1315 hormone sensitive, 634 triple negative breast cancer (TNBC) and 1365 breast cancer patients with information on neoadjuvant chemotherapy responses. Patients were selected by the following criteria: estrogen receptor (ER) status, lymph node invasion, recurrence free survival. The k-means classification algorithm delineated clusters with low- and high- expression of genes related to recurrence of disease; a multivariate Cox’s proportional hazard model defined recurrence risk for disease. Classifier genes were validated by Immunohistochemistry (IHC) using tissue microarray sections containing both normal and cancerous tissues and by evaluating findings deposited in the human protein atlas repository. Based on the leave-on-out cross validation procedure of 4 independent data sets we identified 51-genes associated with disease relapse and selected 10, i.e. TOP2A, AURKA, CKS2, CCNB2, CDK1 SLC19A1, E2F8, E2F1, PRC1, KIF11 for in depth validation. Expression of the mechanistically linked disease regulated genes significantly correlated with recurrence free survival among ER-positive and triple negative breast cancer patients and was independent of age, tumor size, histological grade and node status. Importantly, the classifier genes predicted pathological complete responses to neoadjuvant chemotherapy (P < 0.001) with high expression of these genes being associated with an improved therapeutic response toward two different anthracycline-taxane regimens; thus, highlighting the prospective for precision medicine. Our study demonstrates the potential of classifier genes to predict risk for disease relapse and treatment response to chemotherapies. The classifier genes enable rational selection of patients who benefit best from a given chemotherapy thus providing the best possible care. The findings encourage independent clinical validation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Common Electric Power Transmission System Model JSON Schema Specification

The Common Electric Power Transmission System Model (CTM) is an intuitive, extensible, language-agnostic, and error-resistant specification of electric power network components parameter names and units, and relation between components, intended for use by the research community developing new computational methods for power systems operations and simulation. Power system datasets following the CTM specification can be read as dictionaries and manipulated in that form in most programming languages (e.g., Python, Julia, C++). This standard data structure in CTM makes it easy to work in multiple power systems domains (e.g., economic operation, reliability assessment, electricity markets, stability assessment, etc.) without requiring conversions between use-case-specific file formats with information loss in the process. This repository specifies CTM as a JSON Schema, provides documentation, derivate (code-generated) implementations of CTM, and example data and usage of the schema for important use cases.

Aravena Solis, Ignacio↗
DOE OSTI2025

Scientific Open-Source Software Is Less Likely to Become Abandoned Than One Might Think! Lessons from Curating a Catalog of Maintained Scientific Software

Scientific software is essential to scientific innovation and in many ways it is distinct from other types of software. Abandoned (or unmaintained), buggy, and hard to use software, a perception often associated with scientific software can hinder scientific progress, yet, in contrast to other types of software, its longevity is poorly understood. Existing data curation efforts are fragmented by science domain and/or are small in scale and lack key attributes. We use large language models to classify public software repositories in World of Code into distinct scientific domains and layers of the software stack, curating a large and diverse collection of over 18,000 scientific software projects. Using this data, we estimate survival models to understand how the domain, infrastructural layer, and other attributes of scientific software affect its longevity. We further obtain a matched sample of non-scientific software repositories and investigate the differences. We find that infrastructural layers, downstream dependencies, mentions of publications, and participants from government are associated with a longer lifespan, while newer projects with participants from academia had shorter lifespan. Against common expectations, scientific projects have a longer lifetime than matched non-scientific open-source software projects. We expect our curated attribute-rich collection to support future research on scientific software and provide insights that may help extend longevity of both scientific and other projects.

Malviya Thakur, Addi [ORNL] (ORCID:000000022681999↗
DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

42 ENGINEERING↗