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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Maskman

SAND2025-04369O Maskman is a user-friendly tool designed to create hex masks, which are essential for optimizing application performance in high-performance computing environments. By converting a list of integers into binary and then hex masks, Maskman simplifies the process of setting application affinity. This ensures that software runs efficiently on specific nodes within a computing cluster. Ideal for researchers and developers, Maskman streamlines the preparation of inputs for HPC schedulers, enhancing resource management and improving overall system performance. 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.

Pase, Douglas [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2025

MiniMOD

SAND2025-03854O MiniMod is a user-friendly software tool designed to assess the performance of high-performance computing (HPC) systems. Researchers can use the program to test communication methods and computational tasks to understand how different setups can affect application efficiency. This software is particularly useful for optimizing network performance in scientific research, simulations, and data analysis. MiniMod‘s flexible design allows users to make informed decisions about their computing environments, which can enhance productivity and results in real-world applications. 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.

Dosanjh, Matthew [Sandia National Lab. (SNL-CA), L↗
DOE OSTI2025

Uniform Field Electrode Generator

SAND2025-03788O Uniform Field Electrode Generator is a user-friendly tool designed to create uniform electric field electrodes, essential for high voltage applications. By generating electrodes with precise dimensions, it ensures a consistent electric field across their surfaces. This software is particularly beneficial for researchers and engineers working on projects like closing switches and studying power flow in advanced systems, such as the Z machine. Its unique approach simplifies the design process, making it accessible for users without technical expertise, while enhancing the efficiency and effectiveness of their electrical applications. 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.

Mason, Tyler [Sandia National Lab. (SNL-CA), Liver↗
DOE OSTI2025

Multifidelity_Timeseries

SAND2025-03305O Multifidelity Timeseries is a user-friendly tool designed to create advanced models for analyzing time-series data. It offers three modeling options, allowing users to choose the best fit for their specific needs. The software efficiently processes multiple data sources without the need for complex sampling methods. It helps uncover patterns and insights using data. The result is it is easier to make informed decisions for projects. 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.

Katona, Ryan [Sandia National Lab. (SNL-CA), Liver↗
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↗
DOE OSTI2025

Controlled Environment Agriculture SiteScout (CEA SiteScout) v1.0

This is a geospatial online decision support tool designed to help identify locations where controlled environment agriculture (CEA) can be cited to take advantage of energy and water resources. It also quantifies the benefits of selected energy and water efficiency measures.

Stokes-Draut, Jennifer [Lawrence Berkeley National↗
DOE OSTI2025

Atmospheric Profile Builder

APBuilder is a software tool designed to generate atmospheric profiles for use with AC2Dr by downloading weather model data and transforming it into 1D or 2D binary profiles. These binary profiles serve as required inputs for AC2Dr, an LLNL-developed, open source, two-dimensional numerical solver for the acoustic wave equation based on the finite difference method.

Kim, Keehoon [Lawrence Livermore National Laborato↗
DOE OSTI2024

RetroTide v1.0

RetroTide is a retrobiosynthesis software tool, designed to guide the design of biological pathways to make valuable small molecules. It takes as input a desired small molecule chemical structure, and outputs a design for a polyketide synthase enzyme (PKS) that has the closest possible chemical structure, as accessible by PKS chemistry. It is developed as a Python software library, and internally performs a search of the PKS design space, while simulating the chemicals produced by each design.

Backman, Tyler↗
DOE OSTI2026

OCHRE

OCHRE™ uses a variety of input data sources to run time-series simulations. Building models can be taken from the ResStock™ database or generated using the Building Energy Optimization Tool (BEopt™) or other OpenStudio-HPXML workflows. EV charging profiles can be taken from datasets used in NLR's 2030 National Charging Network project. Weather data can be taken from the National Solar Radiation Database or EnergyPlus® weather files. There are no public datasets with OCHRE outputs at this time. However, a recent project dataset on water heater and EV demand flexibility can be requested. OCHRE is a Python-based energy modeling tool designed to model flexible loads in residential buildings. OCHRE includes detailed models and controls for flexible devices including HVAC equipment, water heaters, EVs, solar PV, and batteries. It is designed to run in co-simulation with custom controllers, aggregators, and grid models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Xopt and Badger: a machine learning ecosystem for real-time accelerator control and optimization

Machine learning (ML)-based black-box optimization algorithms have demonstrated significant improvements in accelerator optimization speed, often by orders of magnitude. However, deploying these algorithms in real-time facility control remains challenging due to the specialized expertise and infrastructure required. To bridge this gap, we introduce the Xopt ecosystem, a versatile suite of tools designed to make advanced ML-based optimization accessible to the broader accelerator community. This ecosystem includes Xopt, a modular Python framework that facilitates the integration of ML-based optimization algorithms with arbitrary control problems, and Badger, a graphical user interface built on top of Xopt, which enables seamless deployment of ML algorithms in real-time control systems. The Xopt ecosystem has been successfully applied towards solving challenging real-time control problems at leading international accelerator facilities, including SLAC, LBNL, Argonne, Fermilab, BNL, DESY, and ESRF, demonstrating its effectiveness in real-world optimization tasks. In this presentation, we provide an overview of Xopt’s capabilities and illustrate its impact through case studies from SLAC accelerator facilities including LCLS, LCLS-II, and FACET-II.

Roussel, Ryan [SLAC]↗
DOE OSTI2024

Carbon Storage Site Mapping Inquiry Tool (MapIT)

The Carbon Storage Site Mapping Inquiry Tool (MapIT) is an online web mapping tool designed to help users discover available public-sourced data to facilitate data exploration in support of Underground Injection Control (UIC) Program Class VI Well Site permitting for the geologic sequestration of carbon dioxide.

Pantaleone, Scott↗
DOE OSTI2024

Dynamic CCS-EJ-SJ Database and Web Application - What's New

At the 2024 FECM/NETL Carbon Management Research Project Review Meeting, within the Carbon Transport and Storage Breakout Session 3, the presentation "Dynamic CCS-EJ-SJ Database and Web Application - What's New" highlights the critical tool designed to integrate environmental and social justice considerations into Carbon Capture and Storage (CCS) projects. Key features include an interactive dashboard for data access and visualization, which supports stakeholders in making informed decisions regarding CCS implementation, and updated data layers. The latest version enhances data integration and usability, providing a comprehensive resource for assessing the social and environmental impacts of CCS projects. There are 7 categories in the CCS EJSJ v2 database (released 03/31/2024): environmental justice, energy justice, economic justice, social justice, ecosystem assets, clean energy, and infrastructure. Most of the layers within each category have been updated in this version. As compared to the old database, there are 3 new categories in the v2 database: ecosystem assets, clean energy, and infrastructure.

Sharma, Maneesh↗
DOE OSTI2024

Preliminary Results on Process Modeling Tools for Determining Variability in Additively Manufactured Stainless Steel 316 Parts

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the distinct characteristics of additive manufacturing (AM) materials, stemming from their unique processing history, microstructure, and properties, pose significant challenges for the qualification and certification of nuclear components. These challenges primarily arise from component-scale variations in microstructure and properties influenced by local process conditions and geometry, which affect thermal history, melt pool dynamics, and microstructure evolution. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents preliminary results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Initial results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing the challenges inherent in process modeling of AM components and outlines a plan for future development needs.

36 MATERIALS SCIENCE↗
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 OSTI2024

Object-Oriented Controllable High-Resolution Residential Energy (OCHRE) (TM) Model

This presentation describes the OCHRE model as part of NREL's Powered By webinar series. OCHRE is a building energy modeling tool designed to model flexible loads in residential buildings. OCHRE includes detailed models and controls for flexible devices including HVAC equipment, water heaters, electric vehicles, solar PV, and batteries. It is designed to run in co-simulation with custom controllers, aggregators, and grid models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

Presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗
DOE OSTI2025

Real Time Phasor Analytics (RTPA) and RTPA-SCR System Strength Online Tool

This presentation showcases the Real-Time Phasor Analytics (RTPA) framework for monitoring inertia and assessing system strength in power grids. RTPA is an open-source tool designed to standardize access to data from Power Management Units (PMUs) and Phasor Data Concentrators (PDCs). It facilitates real-time connectivity to multiple PDCs in accordance with the IEEE C37.118-2 standard and supports asynchronous data stream integration. Additionally, RTPA can simulate a PDC server streaming C37.118-2 data and provides Python bindings for seamless interaction with the framework, eliminating the need for direct Rust programming.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Bayesian Attack Model (BAM) User Story

This document presents a user story for the Bayesian Attack Model (BAM) tool designed to aggregate and analyze cyber-attack observables for operational technology (OT) systems. BAM aims to empower cybersecurity analysts by providing a streamlined interface for collecting observable data from various sources, enabling real-time analysis of potential adversary activity. By enhancing the response capabilities of security teams, BAM facilitates risk-informed decision-making and improves organizational security posture. This user story outlines the key functionalities, user interactions, and requirements necessary to successfully integrate BAM with other security information and event management (SIEM) technology and cybersecurity operations centers (CSOCs).

97 MATHEMATICS AND COMPUTING↗