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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 OSTI2026

Evaluating Supply Prioritization Strategies for Risk-Informed Decision Making in an Arbitrary Gas Network

Supply disruptions and infrastructure failures in natural gas networks present critical challenges to energy reliability and risk-informed planning. This study evaluates two supply prioritization strategies, Maximum Delivery Prioritization (MDP) and Demand-Based Prioritization (DBP), within an arbitrary natural gas network under conditions of supply shortage. Model performance under both strategies is assessed in response to node and edge failure using demand satisfaction metrics, system-wide and localized dependency scores, and geographic information system (GIS)-based spatial analysis. Results show that DBP better preserves supply for high-demand nodes, while MDP offers broader coverage. The underlying network topology plays a critical role in shaping prioritization outcomes. Integrated GIS visualization enhances the interpretability of vulnerability assessments, revealing structurally critical components and localized vulnerabilities. The proposed framework supports scalable, data-driven decision-making for infrastructure planners and engineers, enabling improved disruption recovery and efficiency in constrained natural gas networks. These insights contribute to the development of more robust energy systems capable of withstanding stress and disruptions.

Peterson, Steven [ORNL] (ORCID:0000000287672998)↗
DOE OSTI2026

A review of five decades of strategic decisions and key results on the path to ignition on the National Ignition Facility

Successful demonstration of an implosion that meets the physics metrics for ignition on the National Ignition Facility (NIF) in August 2021 and demonstration of G > 1 in December 2022 represented the culmination of five decades of research. This review covers the key strategic decisions and physics results from the 20 J Janus laser to the 2 MJ NIF laser. NIF's specifications were set in order to have a margin in performance to account for uncertainty in the physics challenges leading to ignition, particularly for laser–plasma interaction and hydrodynamic instabilities including long-wavelength modes responsible for implosion symmetry. The ignition experiments utilized all that margin. Achieving the laser and target performance utilized in the Hybrid-E experiments that achieved ignition and G > 1 proved to be a challenge that consumed a decade.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Survey of Space Professionals’ Perception of Satellite Cybersecurity from 2012 to 2022: Decision-Makers’ Thoughts on Satellite Cybersecurity Evolving

Cyberattacks on space assets are often portrayed in vague terms of doubt and mystery. Several claims depict satellites being compromised or attacked, but little corroboration has been published or made publicly available. As the commercial space industry is growing, commercial satellite decision makers will need to analyze the unacknowledged risk of cyberattacks against satellites. This paper identifies and characterizes what a cybersecurity risk to a space asset could look like and why space professionals might not prioritize cybersecurity. Additional information was captured from a decadal survey of space professionals in 2012 and 2022. Comparing the decadal results shows a rise in the perceived risk of satellites to cybersecurity threats from a sample of space professionals. This growing notable shift of perspective is not fully defined or agreed upon.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Boosted decision tree reweighting of simulated neutrino interactions for O ( 1 ) GeV neutrino cross-section measurements

This paper illustrates a generic method for multidimensional reweighting of O ( 1 ) GeV neutrino interaction Monte Carlo samples. The reweighting is based on a boosted decision tree algorithm trained on high-dimensional space in detector final-state observables. This enables one generator’s events to be reweighted so that its reconstructed particle content and kinematics distributions, as well as detector efficiency, match those of a target model. The approach establishes an efficient way to reuse legacy Monte Carlo data, avoiding regeneration. As an example, we test its use in a measurement of transverse kinematic imbalance of the μ - and proton in charged-current quasielastic like ν μ events from the MINERvA experiment.

Lin, Z. [Rochester U.] (ORCID:0009000188903698)↗
DOE OSTI2025

Decision-tree structures utilizing a phase-transition material

The rich internal physics due to competing electronic phases present in phase-transition materials such as VO2 offer the potential for compact building block design for emerging non-von Neumann computing technologies. Here, based on the relaxation dynamics of an insulator-metal phase transition, we demonstrate experimentally a decision-tree classifier embedded within a single volatile resistive switching device. The tree is constructed by the combination of the voltage pulse and relaxation time and can adapt to different tasks. We use machine learning to analyze the relaxation process, enabling a predictive voltage-relaxation time phase diagram for the electrical resistance state. Classification of the etiology of the chronic cough is presented as a proof-of-principle use case. Further, our approach can be generalized to broader classes of solid-state and solid-liquid interfacial systems that demonstrate a variety of phase relaxations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Decision Support System to Compile Environmental Mitigations from Hydropower Licensing Documents

The process of deciphering, extracting, and compiling information from texts dense with domain-specific terminology and technical jargon is a challenging endeavor. It demands considerable expertise and deep knowledge in the respective field, resulting in a labor-intensive process when executed by humans. Furthermore, the task of identifying multiple class labels in extensive texts presents a challenge due to intra- and inter-reader variability, making the process time-consuming and costly.We’re introducing a user-friendly graphical interface, fortified with a BERT model-powered decision support system. This advanced system aims to augment efficiency, curtail data collection time, and sustain high precision in data acquisition. It is instrumental in deciphering and synthesizing intricate texts teeming with a spectrum of expressions, even within similar mitigation categories. Such tasks traditionally demand substantial human effort and specialized knowledge in the domain.Our system is specifically engineered for the task of extracting environmental mitigation information to promote sustainable hydropower development from licenses issued by the Federal Energy Regulatory Commission (FERC). These license documents are comprehensive, each containing over 15,000 words and requiring the identification of 135 different class labels. We anticipate that our system will boost reading speed, improve the consistency of classification outputs among readers, and contribute to the development of a robust scientific database of environmental mitigations associated with the 2,000+ non-federal hydropower facilities licensed by FERC in the United States.

Yoon, Hong-Jun [ORNL] (ORCID:0000000254505878)↗
DOE OSTI2024

Simulation-Guided Decision-Making for Enhancing Energy Resilience in a Remote Alaskan Community

The increasing frequency and severity of extreme weather events underscore the need to bolster the resilience of energy infrastructure in remote coastal communities exposed to climate hazards. In recent years, considerable effort has been made to harden the grid infrastructure of remote communities through investment in energy storage, advanced metering, renewable generation and energy-efficient loads. However, simulation-based studies are still needed to identify appropriate locations for investment and determine the adequacy of existing infrastructure in supporting new resources. Preparing the required models may often be challenging due to insufficient metering, disparate data sources and workforce limitations. This paper describes modeling efforts undertaken to represent, within a unified co-simulation platform: (a) the electric distribution network and (b) the thermal behavior of a community medical center, in the remote community of Cordova, Alaska. With the help of case-studies, it is shown how the developed simulation platform can help the local utility in making decisions regarding capital investment aimed at enhancing community resilience.

Microgrid, energy storage, resilience↗
DOE OSTI2026

Decision Making Under Uncertainty Human Subjects Data - Fire Evacuation Task

This dataset contains de-identified data from human subjects experiments, along with the images and code that were used to run the experiments (as a crowdsourced online study). In this study, participants were shown the probability of a house being in the burn zone of a wildfire. They were asked if they would stay in the house or evacuate in that scenario. The probability information was presented in different ways, including text and maps. The studies tested the impact of different visual cues on the participants' patterns of decisions.

Matzen, Laura E. [Sandia National Laboratories (SN↗
DOE OSTI2026

Dynamic Transmission Line Switching Amid Wildfire-Prone Weather Under Decision-Dependent Uncertainty

During dry and windy seasons, environmental conditions significantly increase the risk of wildfires, exposing power grids to disruptions caused by transmission line failures. Wildfire propagation exacerbates grid vulnerability, potentially leading to prolonged power outages. To address this challenge, we propose a multistage optimization model that dynamically adjusts transmission grid topology in response to wildfire propagation, aiming to develop an optimal response policy. By accounting for decision-dependent uncertainty, where line survival probabilities depend on usage, we employ distributionally robust optimization to model uncertainty in line survival distributions. We adapt the stochastic nested decomposition algorithm and derive a deterministic upper bound for its finite convergence. To enhance computational efficiency, we exploit the Lagrangian dual problem structure for a faster generation of Lagrangian cuts. Using realistic data from the California transmission grid, we demonstrate the superior performance of dynamic response policies against two-stage alternatives through a comprehensive case study. In addition, after solving the multistage formulation, we construct easy-to-implement policies that significantly reduce computational burden while maintaining good performance in real-time deployment. History: Accepted by Russell Bent, Area Editor for Network Optimization: Algorithms and Applications. Funding: This work was supported by the U.S. Department of Energy, Office of Electricity [Grant DE-AC02-05CH11231]. The work of R. Jiang was supported in part by the U.S. National Science Foundation, Division of Electrical, Communications and Cyber Systems [Grant ECCS-1845980] and the U.S. Air Force Office of Scientific Research [Grant FA9550-23-1-0323]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2025.1210 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2025.1210 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Estrada-Garcia, Juan-Alberto↗
DOE OSTI2026

Dataset for Top Model Decision Tree: Selecting Segmentation Models for Reliable Quantitative Analysis in Low- and Ultralow-Dose CryoEM

Motivation Multiple deep learning model architectures can be used to segment bacterial membranes in cryoEM images. However, an AI-based tool advancement is often presented with only a single segmentation model for broad use, and this single model may show inconsistent results across datasets from different users. Here, we present the Top Model Decision Tree, a model screening framework to screen for the best model to generate bacterial inner and outer membrane masks based on user priorities. We use pre-trained segmentation models from YOLOv11, YOLO26, U-Net, Detectron2 and SAM3 fine-tuned on bacterial inner and outer membranes imaged with cryoEM. Run the Framework This notebook must be opened in Google Colab. Mount Google Drive and run with a GPU-based runtime. Open the notebook and follow steps to git clone in folders and files within this repository. There will be a repeating top_model_decision_tree.ipynb (notebook clone) that will not be used. Save your .png binary mask files and .csv table outputs within your Google Drive or download before closing the notebook. The models and all analysis/training scripts are available at [GitHub: https://github.com/Lynnicia/CryoEM_membranes_top_model_decision_tree and https://github.com/Sireesiru/Semantic-Segmentation-of-bacterial-cell-envelope-using-U-Nets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Travel Decision Survey - San Francisco - 2012

On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to accurately assess mode share for trips within San Francisco (including trips to or from San Francisco, but excluding through trips) among city residents and residents of other Bay Area counties; provide additional trip details, including trip purpose information for each trip in the mode-share question series; and use the mode-share percentages collected on this survey in conjunction with total trip estimates provided by SFMTA to project the number of trips by mode on an average day in San Francisco.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Travel Decision Survey - San Francisco - 2013

On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Travel Decision Survey - San Francisco - 2014

On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Travel Decision Survey - San Francisco - 2015

On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Travel Decision Survey - San Francisco - 2017

On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Exploring Data Set Bias and Decision Support with Predictive Uncertainty Through Bayesian Approximations and Convolutional Neural Networks

Individual seismic catalogs can contain multiscale observations from fault level to global scales and associated waveforms from discrete events reflect crustal structure across many different scales and locations. Seismic network aperture, geographic location, and observation distance may not provide informative guidance or intuition on how different catalogs will behave across models trained under different conditions. We rely on uncertainty to provide guardrails for when to trust model decisions, but understanding when our uncertainty is trustworthy is an open challenge. Here, in this work, we explore Bayesian approximation methods for assigning predictive uncertainty in seismic event classification problems. We find that computationally expensive Bayesian approximations do not outperform simple ensemble methods. We also find that when exploiting multiple seismic event catalogs, joint training with data from all the catalogs combined with Bayesian approximations and supervised training for classification can obscure bias and result in less robust uncertainty while also not providing substantial performance benefits compared to training individual models for each catalog.

58 GEOSCIENCES↗
DOE OSTI2026

Optimizing Repowering and Lifecycle Decisions with PV ICE and SAM

Should you repower or extend the life of your PV system? Are high-efficiency modules, durable modules, or recyclable modules the best option for your site and goals? Evaluating the trade-offs in design and lifecycle strategies can be complex. The PV in Circular Economy (PV ICE) tool is an open-source model designed to help developers, modelers, and decision-makers assess material flows, energy return on investment (EROI), and financial viability of PV systems. Now integrated with the System Advisor Model (SAM), PV ICE enables site-specific comparisons of lifecycle strategies - such as repowering benefits, module selection for reliability and recyclability, among others. This interactive tutorial will provide hands-on experience with PV ICE using Google Collab, exploring scenario-based analyses on these topics.

36 MATERIALS SCIENCE↗