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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Stepwise Dynamic Calibration of a Hydromechanical Simulation Using Time-Lapse Vertical Seismic Profile

This study aims to develop a methodology for calibrating subsurface stress changes through time-lapse Vertical Seismic Profiling (VSP) integration. The selected study site is the 13-10A injector well within the ongoing CO2-EOR operation of the Farnsworth Field Unit. The Time-lapse VSP dataset carries the combined effects of fluid substitution and mean effective stress changes, thereby providing a dataset amenable for the calibration of production and injection-induced stress changes. The concept is similar to calibrating a reservoir simulation model in that the process honor real field data to set up an inverse problem. The solution optimizes the independent and impactful geomechanical parameters that replicate the observed time-lapse seismic velocity changes. This stress calibration is enabled by 4D geomechanical modeling and the VSP Integration workflow. This calibration benefits from extensive geological, geophysical and geomechanical characterization through 3D seismic data, geophysical well logs, and core assessed as part of the 1D MEM conducted on the 13-10A subject well. These data are used to develop a site-specific rock physics model. The Biot Gassmann workflow combines rock physics and reservoir simulation outputs to determine the fluid substitution contribution to seismic velocity change. Additionally, modeled seismic velocity attributed to mean effective stress are determined from the geomechanical simulation outputs, and the stress-velocity relationship developed from the ultrasonic seismic velocity measurements on the extracted Morrow B core. A penalty function is then formed between the modeled seismic velocities and the observed time-lapse VSP dataset. Four independent and impactful geomechanical parameters have been determined. These are the bulk modulus and shear modulus for zero porosity and the shear and compressional seismic velocity to mean effective stress derivatives. The dataset of numerous coupled hydromechanical- geomechanical simulation realizations is built by combining variations of the four stated geomechanical parameters. A machine learning-assisted workflow comprised of an artificial neural network and a particle swarm optimizer are used to converge on the optimal geomechanical parameters. The successful execution of this workflow has affirmed the suitability of acoustic time-lapse measurements for 4D-VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling.

02 PETROLEUM↗
NASA NTRSDate not supplied

Advancements to Wind Tunnel Testing at the Marshall Space Flight Center Aerodynamic Research Facility by the Utilization of Additively Manufactured Models

In recent years, the Aerodynamic Research Facility (ARF) at NASA’s Marshall Space Flight Center (MSFC) has heavily utilized additive manufacturing (AM) technology for the manufacturing of models for aerodynamic testing in the 14 x 14-inch Trisonic Wind Tunnel (TWT). This process change resulted in speed and cost improvements to the design and manufacture of model parts, which has provided the capability to perform parametric tests at a rapid pace with high fidelity models that are more finely detailed than previously tested. This paper outlines the model design and fabrication workflow of the ARF and the improvements made, focusing specifically on two vehicles tested. First, identical traditionally machined and AM models of a test missile configuration were tested to isolate effects of differences in manufacturing. Second, a high-fidelity model of NASA’s Space Launch System (SLS) was tested to investigate improvements in data quality possible by utilizing AM on small-scale wind tunnel tests. This paper also discusses detailed model inspections and data comparisons that were performed to prove the reliability of data across the TWT’s entire Mach range.

Ryan C Connelly↗
DOE OSTI2022

Utah FORGE Well 16A(78)-32 Stimulation DFN Fracture Plane Evaluation and Data

This dataset includes files used to fit planar fractures through the preliminary earthquake catalogs of the three stages of the April 2022 well 16A(78)-32 stimulation which is linked bellow. These planar features have been used to update the FORGE reference Discrete Fracture Network (DFN) model. The files are provided to encourage other modelers to use additional workflows to find additional/alternative features. To this end, the dataset includes the cleaned earthquake catalog data translated to the FORGE reference model global reference frame, the well trajectory of 16A(78)-32 in those same coordinates, the fit 15 planar features in csv format, and a pdf file with slides illustrating the process used to fit the features. A recorded presentation of this material is available from the October 2022 FORGE Modeling and Simulation Forum which is also linked below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Enabling Efficient Surveillance, Control, and Automation of Geothermal Operations with Advanced Predictive Analytics

Automation and control of geothermal energy production and operations require reliable and efficient predictive tools. While physics-based simulation offers a comprehensive tool for predicting energy production performance in geothermal systems, predicting the behavior of geothermal reservoirs involves complex multi-physics processes with coupling effects, highly uncertain input parameters and subsurface descriptions. Moreover, building, running, and integrating simulation models into standard model calibration and optimization workflows entail significant technical and computational efforts. An emerging alternative to physics-based simulation is data-driven predictive analytics models that have gained popularity in energy industry. In this report, we develop novel predictive models for integration into real-time fault diagnosis and model predictive control algorithms to improve the efficiency of energy production operations in geothermal reservoirs. The report includes two major research Thrust Areas, that is, the surface power plant and the subsurface reservoir.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

LASSO for CALPHAD Model Selection Enables Data-Efficient Thermodynamic Modeling: An Application in Thermochemical Hydrogen Production Materials

Phenomenological CALPHAD (CALculation of PHAse Diagrams) models, widely used for multicomponent materials, often contain a considerable number of parameters and require fitting using data from a relatively small number of experimental measurements or theoretical calculations. Sometimes these parameters are introduced for the purpose of improving model fits but without clear physical justification, which leads to overparametrized models with poor generalization performance. Automated approaches for optimal model selection based on the available data therefore become critical. Here, in this work, a least absolute shrinkage and selection operator (LASSO)-based approach is developed for model selection by leveraging the linearity of the CALPHAD model with respect to its parameters to convert the model selection and fitting to a LASSO minimization problem. We demonstrate its utility for thermodynamic modeling of thermochemical hydrogen (TCH) production materials using lanthanum strontium manganite (LSM) as an example. Various TCH-relevant properties, including oxygen stoichiometry as a function of oxygen partial pressure, enthalpy of reduction, and entropy of reduction, are successfully predicted with reasonable accuracy using a minimal set of model parameters. Importantly, the model selection and fitting involve minimal human decision; it can therefore be applied to high-throughput DFT defect calculations and yield efficient workflows for TCH material modeling and optimization.

CALPHAD↗
DOE OSTI2025

ADEPT: A Pedagogical Framework for Integrating Agentic AI with Deterministic Scientific Workflows

The integration of Large Language Models (LLMs) into scientific research promises to accelerate discovery, yet a significant gap remains between the dynamic reasoning of Artificial Intelligence (AI) agents and the static, deterministic nature of canonical scientific workflows. This paper introduces ADEPT (Agentic Discovery and Exploration Platform for Tools), a reference architecture and pedagogical framework explicitly designed to bridge this gap. ADEPT's primary mission is to provide a transparent, "glass-box" environment where researchers and engineers can learn to effectively wrap established scientific software (e.g., BLAST, Nextflow pipelines) and compose it into reliable, agent-driven workflows. We describe its modular, multi-server architecture, which leverages the Model Context Protocol (MCP) for tool serving, LangGraph for robust agentic orchestration, and a secure nsjail-based sandbox for safe code execution. By prioritizing architectural clarity, safety, and modularity, ADEPT serves as an extensible blueprint for building trustworthy AI-augmented systems and fosters the collaborative development necessary to responsibly employ agentic AI for science. We provide practical examples of how to adapt and extend this framework, highlighting its utility in workforce development and AI-readiness capabilities across research and development projects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

36 MATERIALS SCIENCE↗
NASA NTRS2023

Kamodo: Simplifying Model Data Access and Utilization

To address the lack of user-friendly software needed to simplify the utilization of model data across Heliophysics, the Community Coordinated Modeling Center (CCMC) at NASA’s Goddard Space Flight Center has developed a model-agnostic method via Kamodo for users to easily access and utilize model data in their workflows. By abstracting away the broad range of file formats and the intricacies of interpolation on specialized grids, this approach significantly lowers the barrier to model data access and utilization for the community while adding exciting new capabilities to their tool boxes. This paper describes the direct interfaces to the model data, called model readers, and a basic introduction on how to use them. Additionally, we detail the planned approach for including custom interpolation codes, and include current progress on specialized visualization developments. The CCMC is maintaining Kamodo as an official NASA open-sourced software to enable and encourage community collaboration.

Heliophysics↗
DOE OSTI2026

Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials

This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain‐specific constraints. We introduce a two‐dimensional readiness model that combines canonical preprocessing patterns with a five‐level operational readiness scale, both tailored to high‐performance computing (HPC) environments. This construct helps outline key challenges in transforming large‐scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross‐domain support for scalable and reproducible AI for science. Finally, we evaluate this maturity matrix in the context of case studies including ClimaX (climate), AFLOW (materials), OpenFold (proteomics), and DIII‐D fusion disruption‐prediction workflows, from which we distill lessons learned and provide recommendations to guide practitioners in developing robust AI‐readiness pipelines. Finally, we discuss remaining cross‐cutting challenges that persist across scientific domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Panorama 360 (Final Report)

This is the final technical report for the DOE-funded Panorama 360 project. Panorama 360 provided a resource for the collection, analysis, and sharing of performance data about end-to-end scientific workflows executing on DOE facilities. The work focused on workflows that include experimental data generation at DOE facilities. The main activities of Panorama 360 include the development of: 1. A distributed repository that stores different types of workflow execution data (e.g., point and time series performance traces at fine- and coarse-grained levels); 2. A set of open-source data capture, curation, and publishing tools fully integrated with a state-of-the-art workflow management system that automates data ingestion to the repository and enables users to discover, query, and process data from the repository; 3. A set of analysis algorithms and machine learning based tools to perform analysis and characterization of the gathered data, which can be used to detect anomalous performance or system faults; and 4. Best practices and recommendations for workflow evaluation, analysis, execution, and architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

CO2 Plume Imaging with Accelerated Deep Learning-based Data Assimilation Considering Multiple Realizations: Application to the Illinois Basin-Decatur Carbon Sequestration Project

We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site. Unlike the previous work, this study incorporates the impact of spatial heterogeneity using multiple realizations. In the proposed workflow, a neural network model utilizes available monitoring data such as downhole pressure measurements as input and predicts the propagating pressure ‘front’ using the diffusive time of flight (DTOF) map which is considered as representative reservoir image of the flow field. The DTOF is the arrival time of pressure front propagation, which can be computed by the Fast Marching Method rapidly without flow simulations. Reservoir model calibration can be implemented by selecting the training data samples that describe the predicted DTOF map based on observed data. The power and efficacy of our workflow is demonstrated by application to the Illinois Basin-Decatur Project.

CO2 plume imaging↗
DOE OSTI2025

Pipeline for Integrated Projects in Energy Systems (PIPES): A Tool for Integrated System Planning [Slides]

The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. This presentation introduces PIPES a multi-model tool for integrated system planning; it describes the underlying architecture, deep dives into common user workflows, and outlines the upcoming development roadmap beyond its current alpha state.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Evaluating FRI3D for Cost Savings in Fire Hazard Analysis at DOE Sites

A fire hazard analysis, required for many U.S. Department of Energy (DOE) facilities, is a complex, cumbersome, and costly process. Fire hazard analyses may be viewed as a checkbox, but ideally and in spirit with the DOE-STD-1066, the fire hazard analysis (FHA) should be a part of the workflow and used to help in modifications, maintenance, and improving operational safety. With current FHA development processes, it is both time and cost prohibitive for true integration. A tool called Fire Risk Investigation in 3D or FRI3D was developed under the DOE Light Water Reactor Sustainability program to simplify and automate many aspects of a fire probabilistic risk analysis for existing nuclear power plants. The FRI3D tool automates fire scenarios by combining approved fire simulation codes, U.S. Nuclear Regulatory Commission fire calculations methods, 3D modeling and visualization, and probabilistic risk analysis models into a single workflow supported with a user interface. FRI3D was initially designed for used in combination with a PRA, this case study, evaluated using FRI3D for a plant modification, determined the benefits that detailed fire modeling can have for U.S. Department of Energy facilities with or without a PRA model. It also looked at what tasks from DOE requirements could be reduced using the tool and what is needed to integrate fire hazard analysis into site workflow.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine learning enabling high-throughput and remote operations at large-scale user facilities

Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher resolution, thus producing vast amounts of data for analysis. These innovations are particularly pronounced at user facilities and synchrotron light sources. Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. However, there remain conceptual barriers to entry for the facility general user community, whom often lack expertise in ML, and technical barriers for deploying ML models. Herein, we demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II). We describe these examples instructively, with a focus on integrating the models into existing experimental workflows, such that the reader can easily include their own ML techniques into experiments at NSLS-II or facilities with a common infrastructure. The framework presented here shows how with little effort, diverse ML models operate in conjunction with feedback loops via integration into the existing Bluesky Suite for experimental orchestration and data management.

36 MATERIALS SCIENCE↗
NASA NTRS2012

The Invasive Species Forecasting System (ISFS): An iRODS-Based, Cloud-Enabled Decision Support System for Invasive Species Habitat Suitability Modeling

The Invasive Species Forecasting System (ISFS) is an online decision support system that allows users to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of interest, such as a national park, monument, forest, or refuge. Target customers for ISFS are natural resource managers and decision makers who have a need for scientifically valid, model- based predictions of the habitat suitability of plant species of management concern. In a joint project involving NASA and the Maryland Department of Natural Resources, ISFS has been used to model the potential distribution of Wavyleaf Basketgrass in Maryland's Chesapeake Bay Watershed. Maximum entropy techniques are used to generate predictive maps using predictor datasets derived from remotely sensed data and climate simulation outputs. The workflow to run a model is implemented in an iRODS microservice using a custom ISFS file driver that clips and re-projects data to geographic regions of interest, then shells out to perform MaxEnt processing on the input data. When the model completes, all output files and maps from the model run are registered in iRODS and made accessible to the user. The ISFS user interface is a web browser that uses the iRODS PHP client to interact with the ISFS/iRODS- server. ISFS is designed to reside in a VMware virtual machine running SLES 11 and iRODS 3.0. The ISFS virtual machine is hosted in a VMware vSphere private cloud infrastructure to deliver the online service.

Gill, Roger↗
DOE OSTI2026

3D reconstruction and neural rendering for adversarial machine learning

While evasion attacks on computer vision systems have been widely studied, creating attacks that remain effective under significant changes in viewpoint continues to be challenging. Traditional approaches often rely on affine transformations of images, but these approaches degrade at larger perspective shifts and often produce unrealistic or ineffective perturbations. Recent methods use differentiable renderers to improve viewpoint robustness, but they typically depend on manually constructed 3D models. We introduce a semi-automated pipeline that generates physically printable and perspective-invariant adversarial patches using only a small set of 2D images. Our method integrates 3D reconstruction, neural rendering, adversarial patch optimization, and an object detection victim model into a unified workflow. We use 2D Gaussian Splatting for high fidelity mesh reconstruction and FlexPara for surface parameterization that produces texture maps suitable for patch editing. Together, these components form a fully differentiable pipeline in PyTorch3D that links texture modification to model outputs, enabling efficient optimization of patches that remain effective across many viewpoints. The complete process, from image capture to patch printing and physical evaluation, can be completed within a few hours. We demonstrate the effectiveness of the resulting patches through attacks on the YOLOv8 object detection model and discuss remaining challenges and opportunities for improving robustness and scalability.

Singhvi, Vivaan [ORNL] (ORCID:0009000586288221)↗
DOE OSTI2023

Hourly Electricity Demand Projections for Eight Combined Climate and Socioeconomic Scenarios

This dataset contains 40 years (1980-2019) of simulated historical hourly electricity demand (i.e., loads) and 80 years (2020-2099) of projected hourly loads for 54 Balancing Authorities (BAs) and 48 states plus the District of Columbia. Details about the scenarios and variables included in this dataset are in the readme.pdf file. The two primary models that created the dataset are a version of the Global Change Analysis Model with detailed sectoral resolution over the United States (GCAM-USA) and the Total ELectricity Loads (TELL) model. Links to the model source code and workflow for deriving the dataset are provided in an accompanying meta-repository: https://github.com/IMMM-SFA/burleyson-etal_2023_applied_energy. Projections are for four future climate scenarios that represent combinations of Representative Concentration Pathways (RCPs) 4.5 and 8.5 combined with two levels of climate model sensitivities: rcp45cooler, rcp45hotter, rcp85cooler, and rcp85hotter. The four climate scenarios are crossed with Shared Socioeconomic Pathways (SSPs) 3 and 5 to yield eight different future load projections: rcp45cooler_ssp3, rcp45cooler_ssp5, rcp45hotter_ssp3, rcp45hotter_ssp5, rcp85cooler_ssp3, rcp85cooler_ssp5, rcp85hotter_ssp3, and rcp85hotter_ssp5. The climate scenarios are from the IM3 Thermodynamic Global Warming (TGW) dataset which is linked below in the related metadata. The related metadata also contains links to a repository containing the raw GCAM-USA output files.

Climate Change↗
DOE OSTI2024

Hourly Electricity Demand Projections for Eight Combined Climate and Socioeconomic Scenarios

This dataset contains 40 years (1980-2019) of simulated historical hourly electricity demand (i.e., loads) and 80 years (2020-2099) of projected hourly loads for 54 Balancing Authorities (BAs) and 48 states plus the District of Columbia. Details about the scenarios and variables included in this dataset are in the readme.pdf file. The two primary models that created the dataset are a version of the Global Change Analysis Model with detailed sectoral resolution over the United States (GCAM-USA) and the Total ELectricity Loads (TELL) model. Links to the model source code and workflow for deriving the dataset are provided in an accompanying meta-repository: https://github.com/IMMM-SFA/burleyson-etal_2024_applied_energy. Projections are for four future climate scenarios that represent combinations of Representative Concentration Pathways (RCPs) 4.5 and 8.5 combined with two levels of climate model sensitivities: rcp45cooler, rcp45hotter, rcp85cooler, and rcp85hotter. The four climate scenarios are crossed with Shared Socioeconomic Pathways (SSPs) 3 and 5 to yield eight different future load projections: rcp45cooler_ssp3, rcp45cooler_ssp5, rcp45hotter_ssp3, rcp45hotter_ssp5, rcp85cooler_ssp3, rcp85cooler_ssp5, rcp85hotter_ssp3, and rcp85hotter_ssp5. The climate scenarios are from the IM3 Thermodynamic Global Warming (TGW) dataset which is linked below in the related metadata. The related metadata also contains links to a repository containing the raw GCAM-USA output files.

Climate Change↗