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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Novel Framework for Performance Evaluation and Design Optimization of PCM Embedded Heat Exchangers for the Built Environment

This research sheds light on the performance evaluation and design optimization of PCM-HXs for the built environment, addressing several barriers to practical issues to PCM-HX commercialization such as modeling aspects (i.e., modeling expertise and computational / time investment, etc.), manufacturing aspects (i.e., at-scale manufacturing, cost assessments, etc.) and experimental performance assessment (i.e., reliable experimental data, assessment of multiple PCM-working fluid combinations, etc.). We present a novel, comprehensive, and experimentally-validated design optimization framework for PCM-HXs capable of simulating any PCM-HX geometry with reasonable accuracy and significant computational time savings when compared to traditional CFD-based design practices. The framework was validated for a wide range of PCM-HX configurations, including a design optimization for a domestic hot water heater application where TES partially replaces electrical heating input. The resulting PCM-HXs were found to deliver 34-68% of the total daily hot water supply with only 5-10% package volume increase from the water heater, thus within U.S. DOE targets for TES systems. To identify the most promising HXs for PCM applications, first-order geometry and cost analyses were conducted based on off-the-shelf HX products. As part of this work, 9 PCM-HX prototypes were manufactured using additive and conventional manufacturing methods. Detailed economy-of-scale assessments were conducted for the most promising PCM-HXs and were found to have a good outlook for the next 5-10 years. The PCM-HX design optimization framework was validated through comprehensive in-house experimental testing using newly-developed PCM-to-fluid test facilities. In total,10 total in-house component-level experiments were conducted using these prototypes, including 9 with water and 1 with refrigerant (R410A) as the working fluid. It was found that the framework can successfully predict experimental thermal-hydraulic performance within ±10-20% the first time without manual design changes, eliminating the need for time-consuming and expensive prototyping efforts as part of the design process. As part of this work, a publicly-available PCM web tool was released which includes a PCM property database (531 PCMs) and PCM-HX modeling tool to assist the design community on common PCM-HX use-cases, e.g., single/multiple flow path(s) fluid-to-PCM and air-to-fluid-to-PCM configurations (https://ceeeweb.umd.edu/pcmapp/). This work will accelerate the design and time to market for next generation PCM-HXs.

25 ENERGY STORAGE↗
DOE OSTI2024

Where are the Data? Automating a Workflow for Carbon Storage Data Gap Analyses

This presentation demonstrates a spatial analysis workflow to assess data availability for the many components of geologic carbon storage technical viability. The workflow relies upon a knowledge-data framework that links the different components of GCS technical viability to the data types needed for evaluation. Using this contextual information, a combination of data science methods (e.g., natural language processing) and spatial analyses are applied to identify areas where sufficient data exists for a given component. The results are aggregated into maps illustrating data density and spatial gaps across all technical viability factors and data categories, as well as the individual component and category level for a more nuanced understanding. Presented at the FECM NETL Carbon Management Program Review Meeting 2024.

Creason, Christopher↗
DOE OSTI2024

Where are the Data? Automating a Workflow for Carbon Storage Data Gap Analyses

This presentation demonstrates a spatial analysis workflow to assess data availability for the many components of geologic carbon storage technical viability. The workflow relies upon a knowledge-data framework that links the different components of GCS technical viability to the data types needed for evaluation. Using this contextual information, a combination of data science methods (e.g., natural language processing) and spatial analyses are applied to identify areas where sufficient data exists for a given component. The results are aggregated into maps illustrating data density and spatial gaps across all technical viability factors and data categories, as well as the individual component and category level for a more nuanced understanding. Presented at the Geological Society of America Connects 2024 Annual Meeting in Anaheim, California, 22-25 September 2024.

Creason, Christopher↗
DOE OSTI2024

Pumped Storage Hydropower Potential and Opportunities

Pumped storage hydropower (PSH) is a flexible energy storage technology with the potential to improve grid reliability, resiliency, and stability in the electric grid of the future. NREL has developed a range of data and tools to help understand opportunities for new PSH deployment, including nationwide resource assessment data, a bottom-up component-level cost model, and a lifecycle greenhouse gas emissions calculator. These datasets can then be used to inform grid planning models, analysis, and decision making to understand the role PSH can play in the power sector.

cost↗
DOE OSTI2025

Transfer learning of neural surrogates on multifidelity groundwater simulations

Multifidelity data used in the paper published in Advances in Water Resources 206 (2025) 105140, https://doi.org/10.1016/j.advwatres.2025.105140 The code used to process the data is openly available on GitHub at https://github.com/Model-Reduction-and-UQ-Group/Transfer_Learning_K_reconstruction Computationally inexpensive surrogates of process-based models, such as deep neural networks, enable ensemble-based computations used in risk assessment, data assimilation, etc. However, generation of large datasets required to train a neural network can be as expensive as the ensemble simulations themselves. We ameliorate this challenge by using data from multifidelity (MF) groundwater simulations and transfer learning (TL) to reduce data generation costs while maintaining model accuracy. As a computational example, we train a deep convolutional neural network (CNN) to reconstruct permeability fields from saturation maps derived from a multiphase flow model. Starting with very low- and low-fidelity data generated on increasingly coarse meshes, we pretrain the CNN, followed by output-layer training and fine-tuning using only a limited number of high-fidelity samples. We demonstrate the surrogate’s robustness when interpreting low-quality inputs—such as interpolated maps or data affected by noise—which has strong implications for the applicability in practical hydrogeological scenarios. This multilevel MF-TL strategy achieves a favorable trade-off between computational efficiency and predictive accuracy, significantly outperforming high-fidelity-only approaches under the same computational budget.

Chiofalo, Alessia [University of Bologna] (ORCID:0↗
DOE OSTI2026

Modeling Framework for Data Center

This chapter highlights the critical need for advanced modeling of data centers due to their rapidly increasing energy consumption and impact on grid reliability. Driven by the demand for AI applications, data centers are projected to consume a significant portion of US energy by 2028, putting stress on an already challenged power grid. The chapter emphasizes the importance of "fast" time-scale models to understand the dynamic interactions between data centers and the grid, especially given the rapid power fluctuations of AI workloads. It outlines a modeling framework that includes both offline and real-time EMT domain simulations, detailing the necessary representations for various components like utility interfaces, transformers, IT loads, UPS, cooling loads, Battery Energy Storage Systems (BESS), generators, protection systems, and higher-level control systems. While standard simulation tools like PSCAD offer basic models, custom development is often required to accurately capture the unique and fast-changing behaviors of modern data centers. The chapter also discusses key metrics and test cases for validating these models, focusing on transient load responses, protection relay coordination, and demand flexibility. Finally, it addresses the challenges of modeling large-scale data centers, such as computational complexity and the trade-off between model fidelity and practicality, suggesting hybrid modeling approaches as a solution. The overarching goal is to create a robust framework that helps assess data center impacts on grid stability, identify vulnerabilities, and inform the development of standards for reliable integration of these large loads into the bulk power system.

25 ENERGY STORAGE↗
DOE OSTI2025

Equation of State for the Thermodynamic Properties of Trans-1,2-dichloroethene [R-1130(E)]

We present an empirical equation of state in terms of the Helmholtz energy for trans-1,2-dichloroethene [R-1130(E)]. The range of validity is from the triple-point temperature, 223.31 K to 525 K with pressures up to 30 MPa. It may be used to calculate all thermodynamic properties in the fluid phase, including liquid, gas, and supercritical regions. Comparisons are given with existing literature data and estimated uncertainties are provided. In addition, checks were made for correct extrapolation behavior so that the equation behaves in a physically realistic manner when used outside of its range of validity, enabling its use in mixture models. The estimated uncertainties (at a k = 2 or 95 % level of confidence) are based on comparisons with critically assessed data and are 0.25 % for vapor pressure for temperatures in the range 300 K < T < 454 K, rising to 1.5 % as the temperature decreases from 300 K to 265 K. For density in the liquid phase the estimated uncertainty is 0.14 % for temperatures 270 K < T < 410 K and for pressures up to 30 MPa. For the vapor phase the estimated uncertainty in density is 3 %. The uncertainty for liquid-phase heat capacity is 1 % at atmospheric pressure over the temperature range 268 K < T < 309 K, and the uncertainty for the speed of sound in the liquid phase is 0.25 % for temperatures 230 K < T < 420 K and for pressures up to 30 MPa. The uncertainties are larger outside of these specified ranges and in the critical region.

1,2-Dichloroethene↗
DOE OSTI2026

Simulating competition in the US bioeconomy to produce hard‐to‐electrify transportation fuels using limited biomass resources

This study presents a novel bioeconomy optimization framework, BiOpt, designed to address critical questions regarding the strategic use of limited US biomass resources for biofuel production. By integrating detailed techno-economic analyses, life cycle assessments, and resource assessment data, BiOpt optimizes resource distributions across competing technologies to maximize economic performance and/or minimize greenhouse gas emissions. Using feedstock scenarios from the 2023 Billion Ton Study, the analysis explores optimal biomass allocations across sustainable aviation fuel, diesel, and marine biofuel conversion pathways given varying production targets and policy incentives. Results demonstrate distinct feedstock preferences and pathway utilizations when prioritizing economic returns vs. emissions reductions. For instance, fats, oils, and greases were highly favored in cost-optimized scenarios, while low-carbon feedstocks such as wet waste dominated greenhouse gas-minimized strategies. The findings underscore the pivotal role of policy incentives and technological advances in shaping biofuel supply chains and provide actionable insights for scaling sustainable biofuel production to decarbonize hard-to-electrify sectors. This framework offers a robust tool for policymakers and stakeholders to evaluate biofuel strategies that balance energy output, economic viability, and environmental impact.

09 BIOMASS FUELS↗
DOE OSTI2025

Illinois Storage Corridor CarbonSAFE Phase III: Stakeholder Engagement and Outreach Plan

The Stakeholder Engagement and Outreach Plan provides a comprehensive framework for engaging stakeholders of the Illinois Storage Corridor (ISC) project. The ISC project is a CarbonSAFE Phase III project designed to facilitate commercial deployment of carbon capture, utilization, and storage (CCUS) in Illinois. The project aims to establish a multi-industry carbon storage corridor through development of storage sites near the One Earth Energy (OEE) ethanol production facility in north-central Illinois and the Prairie State Generating Company (PSGC) coal-fired power plant in south-central Illinois, with combined annual CO 2 capture ultimately exceeding 8.6 million tons per year. Stakeholder engagement is recognized as a critical component for successful CCUS deployment, alongside technical and economic considerations. As an emerging technology, CCUS may not be well understood by the general population, and lack of public awareness can lead to opposition that poses significant barriers to project development. This plan addresses this challenge through systematic stakeholder identification, analysis, planning, and implementation of engagement actions. The plan is structured around four main sections: Communication, Stakeholder Analysis, Stakeholder Engagement, and Environmental Justice. Activities will be conducted under Tasks 1 and 4 of the project's Statement of Project Objectives, with two key subtasks: (1) developing a stakeholder analysis and engagement plan through face-to-face meetings, facilitated discussions, and surveys; and (2) implementing stakeholder engagement and public outreach activities including meetings, open houses, and permit hearings. The Illinois State Geological Survey (ISGS) will manage engagement activities following DOE-NETL best practices, focusing on providing objective, fact-based information about CCUS and the ISC project. A comprehensive Communication Plan establishes protocols for media contacts, site visits, and crisis communications. The stakeholder analysis follows a structured workflow process divided into Pre-feasibility and Feasibility phases, incorporating contextual understanding, assessment, data collection, and analysis. Key stakeholder groups include government bodies, educational organizations, conservation and environmental groups, agricultural communities, and religious organizations. The plan addresses common stakeholder questions regarding project risks, benefits, safety, property values, liability, and environmental impacts. Recommendations emphasize developing clear messaging, creating informational materials, and preparing to address both project-specific and broader environmental concerns to ensure transparent communication and build stakeholder support throughout project implementation.

25 ENERGY STORAGE↗
DOE OSTI2025

LandScan Mosaic Rapid Population Update: Jamaica After Hurricane Melissa (V1)

During a natural disaster such as Hurricane Melissa, understanding where people are located is critical for situational awareness, operational planning and humanitarian support and consequence assessment. Traditional population datasets focus on mapping populations based on residential, or "business-as-usual" scenarios. However, natural disasters can create disruptions in daily routines of population in addition to the magnitude of the population displacement, depending on the type, duration, context, and location of the event. The Geospatial Science and Human Security Division at Oak Ridge National Laboratory (ORNL) produced this latest LandScan Mosaic Rapid Population Update for Jamaica following Hurricane Melissa, a category 5 hurricane that made landfall on Jamaica on October 28 2025. This Rapid Population Update captures the immediate population displacement following the hurricane using a combination of open-source building damage assessment data from Microsoft, flood exposure data from the Global Flood Monitoring service, reported population displacement information, and humanitarian shelter locations from the Jamaican Office of Disaster Preparedness and Emergency Management and the underlying LandScan Mosaic Jamaica as a base population.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Probing BSM Oscillatory Signals with the DUNE Detectors and the LBNF Neutrino Beam: NSI and Sterile Neutrino Sensitivities

The Deep Underground Neutrino Experiment (DUNE) is a flagship long-baseline accelerator neutrino experiment under construction in the U.S. With a 1,300 km distance between its Near Detector (ND) and Far Detector (FD), the world’s most intense LBNF (Long-Baseline Neutrino Facility) neutrino beam, and high-resolution LArTPC (Liquid Argon Time Projection Chamber) detectors, DUNE will measure the neutrino oscillation parameters with unprecedented precision. In addition to the determination of the neutrino mass ordering and the potential discovery of Charge-Parity violation in the leptonic sector, DUNE's capabilities present a unique opportunity for probing Beyond the Standard Model (BSM) physics signals with neutrinos. Examples of BSM physics manifestations include the presence of Non-Standard Interactions (NSI) of neutrinos with matter, or the existence of sterile neutrinos that mix with 3-Flavor neutrinos. In this study, we present a joint ND+FD fit to simulated data assessing DUNE's sensitivity to probing neutrino NSI, as well as scenarios including sterile neutrino mixing.

Prais, Luiz [Cincinnati U.] (ORCID:000000018224947↗
DOE OSTI2024

Radioisotope Identification with List-Mode Gamma Ray Data: A rigorous assessment on the value of temporal information applied to radioisotope identification.

This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely on full gamma-ray spectrum analysis, often require long dwell times and struggle with “confuser” sources, or spectra with similarly spaced spectral peaks. We hypothesize that by leveraging the probabilistic nature of nuclear decay and the time-encoded information from decay sequences and interactions with surrounding materials, we can improve classification accuracy over static spectral analysis. This research rigorously examines the temporal content of list-mode data through exploratory data analysis via correlation discovery and information theory. We further propose a basic classification model that can utilize spectral or temporal data (or both) to determine if the incorporation of temporal information can improve radioisotope identification. The findings suggest that the temporal information present in list-mode gamma-ray data has merit and should be further investigated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

SITCOMTN-161: PSF assessment in the field of Abell 360 and shapeHSM shear profile using LSSTComCam data

The Rubin LSSTComCam on-sky campaign performed at the end of 2024 provided observations of the Abell 360 galaxy cluster; these data allow a preliminary study of cluster weak lensing analysis using Rubin Data Preview 1 (DP1) data. Among all the steps required for such analyses, accurate modeling of the PSF is essential. This work uses several diagnostics, mostly based on the residuals between the second moments of stars and the PSF model, to characterize the accuracy of the PSF modeling in the A360 field. We find the level of the residuals to be sufficiently low not to hinder the measurement of the tangential shear profile around A360. With a simple source selection process, we demonstrate that outputs of the LSST Science Pipelines can be used to detect the tangential shear profile in Abell 360 at the 3.6σ level, and our analysis indicates that contamination from PSF modeling systematics is negligible.

Dell'Antonio, Ian [Brown University]↗
DOE OSTI2025

Open data sets for assessing photovoltaic system reliability

Photovoltaic (PV) systems have become a cornerstone of renewable energy strategies, particularly due to the significant reduction in solar power costs over the past decade. However, the long-term reliability of PV installations presents a persistent challenge, requiring the development of advanced monitoring and predictive maintenance strategies. A wide range of data types is used to evaluate the health of PV systems, including environmental conditions, electrical performance, and inspection imagery. These data enable methodologies such as machine learning (ML) models for lifetime prediction and computer vision techniques for defect detection. However, the acquisition of high-quality and comprehensive data is difficult, particularly in terms of long-term consistency and data variety. Publicly available data sets serve as valuable resources for addressing these challenges, but they often suffer from fragmentation and are difficult to access. This paper presents a comprehensive review of existing open-source data sets related to PV degradation, analyzing their features, functionalities, and potential applications. We categorize these data sets based on the specific aspects of PV system information they cover, such as environmental conditions, operational monitoring, image inspection and module materials, and propose relevant tools and ML models for processing them. In addition, we propose practices for future data collection and usage, while also discussing potential directions in data-driven research. Our aim is to enhance data utilization and publication among researchers and industry professionals, promoting a deeper understanding of the role of data in enhancing the performance and durability of PV systems.

14 SOLAR ENERGY↗
DOE OSTI2024

A Million Person Study Innovation: Evaluating Cognitive Impairment and other Morbidity Outcomes from Chronic Radiation Exposure Through Linkages with the Centers for Medicaid and Medicare Services Assessment and Claims Data

Here, the study of One Million U.S. Radiation Workers and Veterans, the Million Person Study (MPS), examines the health consequences, both cancer and non-cancer, of exposure to ionizing radiation received gradually over time. Recently the MPS has focused on mortality patterns from neurological and behavioral conditions, e.g., Parkinson's disease, Alzheimer's disease, dementia, and motor neuron disease such as amyotrophic lateral sclerosis. A fuller picture of radiation-related late effects comes from studying both mortality and the occurrence (incidence) of conditions not leading to death. Accordingly, the MPS is identifying neurocognitive diagnoses from fee-for-service insurance claims from the Centers for Medicare and Medicaid Services (CMS), among Medicare beneficiaries beginning in 1999 (the earliest date claims data are available). Linkages to date have identified ∼540,000 workers with available health information. Such linkages provide individual information on important co-factor and confounding variables such as smoking, alcohol consumption, blood pressure, obesity, diabetes and many other health and demographic characteristics. The total person-level set of time-dependent variables, outcomes, organ-specific dose measures, co-factors, and demographics will be massive and much too large to be evaluated with standard software. Thus, development of specialized open-source software designed for large datasets (Colossus) is nearly complete. The wealth of information available from CMS claims data, coupled with individual dose reconstructions, will thus greatly enhance the quality and precision of health evaluations for this new field of low-dose radiation and neurocognitive effects.

Dauer, Lawrence T.↗
DOE OSTI2025

Airport Ground Support Equipment Infrastructure & Logistics Electrification Assessment Tool: 2025 Data Development, Modeling and Analysis for DFW

The aviation industry is increasingly turning to modernize freight facilities by integrating electric Ground Support Equipment (eGSE) to enhance operational efficiency of freight facility moving vehicles and equipment. Airports worldwide are adopting eGSE to streamline cargo movement, reduce fuel and maintenance costs, and improve logistics coordination.1 North America, with its advanced aviation infrastructure, leads this transition, leveraging Internet of things (IoT)-enabled automation and zero emission technologies to boost reliability and reduce human errors.2 Electrification of freight facility moving vehicles and equipment boosts turnaround times, improves equipment reliability, and optimizes logistics coordination, giving operators a competitive advantage. With rising fuel price volatility and the pressure to meet stringent performance benchmarks, airports are focusing on cost-effective, scalable solutions for long-term financial and operational gains. To further accelerate electrification, airports are integrating Zero Emission Vehicles (ZEVs) into rental car fleets and deploying electric baggage carts, requiring strategic investments in charging infrastructure. 3 The shift, however, presents challenges, such as limited technical expertise, high capital costs, and complex procurement processes. By forging strategic partnerships, leveraging advanced technologies, and optimizing infrastructure investments, airports can create a resilient, future-ready ecosystem that enhances the movement of people and goods through electrification-driven efficiency. Supported by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO), this electrification effort provides a scalable, cost-effective solution to improve airport freight operations. Through targeted investments and innovation, airports enhance efficiency, reduce costs, and meet performance benchmarks while advancing toward a resilient, electrified future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data

In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account.

59 BASIC BIOLOGICAL SCIENCES↗