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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 415 records · Page 23

Solar and Battery Storage Permitting and Siting Requirements - Solar Prize Round 7 (CRADA Final Report)

The purpose of this project is to identify local zoning ordinances, state permitting requirements, federal regulatory bottlenecks throughout the development process, and the potential to use Large Language Models (LLM) and aggregate permitting and interconnection data for commercial and industrial (C&I), community, and utility-scale solar photovoltaic (PV) and battery storage projects.

14 SOLAR ENERGY↗

Techno-Economic Assessment of Data Center Load Demand Powered by Small Modular Reactors and Distributed Energy Resources

The rapid increase in data center energy demand, driven by AI and large-scale data processing, poses significant challenges to global energy infrastructure. Data centers require substantial and reliable energy for continuous operations and high-performance computing. Current electrical grids face issues such as transmission bottlenecks and aging infrastructure, making it difficult to meet these demands. Integrating inverter-based-resources (IBRs) like solar and wind presents both opportunities and challenges due to their intermittent nature. Small Modular Reactors (SMRs) offer a promising solution with their enhanced safety, modularity, reliability, and scalability, providing consistent base load power ideal for data center operations. This study presents a comprehensive techno-economic assessment of powering data center load demand using a combination of SMRs and IBRs with grid-connected and islanded mode. This study utilized Idaho National Laboratory’s (INL) HPC data center hourly load profiles and Xendee microgrid optimization platform to conduct the analysis. In this configuration, SMRs serves as the primary base load power source, consistently providing a steady supply of electricity necessary to meet the minimum load demand of the data center with support from the IBRs. Key performance indicators such as Levelized Cost of Electricity (LCOE), Net Present Value (NPV) has been calculated to assess the economic feasibility. The findings from this research will underscore the strategic benefits of integrating SMR plant with DERs – particularly for critical infrastructure load such as data centers.

14 - SOLAR ENERGY↗

AmeriFlux FLUXNET-1F US-GL1 Stannard Rock

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-GL1 Stannard Rock. This is the FLUXNET version of the carbon flux data for the site US-GL1 Stannard Rock produced by applying the standard ONEFlux (1F) software. Site Description - Stannard Rock is located 39 km from the nearest shore (Keweenaw Peninsula) in Lake Superior, 44 miles NNE of Marquette, Michigan, and 24 miles ESE of Manitou Island. The site is located on the historic Stannard Rock Lighthouse, which was completed in 1882. Eddy covariance instrumentation was installed in 2008 by a network of scientists from both US and Canada, eventually to be called the Great Lakes Evaporation Network (GLEN). The intent of GLEN has been to provide observations of over-lake meteorology and evaporation, improve forecasting of Great Lakes water levels, and support a wide variety of stakeholders, including the National Weather Service (NWS), Environment and Climate Change Canada, National Oceanic and Atmospheric Administration, U.S. Coast Guard, recreational boaters and commercial shipping, emergency management officials, and the Great Lakes research community. The eddy covariance station along with other ancillary meteorological instrumentation is located at an approximate elevation of 39.2 meters above mean lake water level. Meteorological data from the lighthouse are sent to the National Data Buoy Center, where they can be viewed in real-time at http://www.ndbc.noaa.gov/station_page.php?station=stdm4 Uncorrected half-hour fluxes were computed directly on the logger using a 30-minute block averaging period as high-frequency data were not available. The half hour flux measurements downloaded from the datalogger were post-processed with the following filters and corrections. Latent and sensible heat and carbon dioxide fluxes were corrected with 2-D coordinate rotation. Although the primary objective of data collection at Stannard Rock was to quantify the evaporative flux, additional preliminary measurements of the carbon dioxide concentration and flux from the LI-7500 are also included in this dataset but it is advised to use the carbon data with caution. Carbon data reported in this dataset includes turbulent fluxes of CO2 with no storage correction (FC, µmol m-2 s-1) and CO2 density in mole fraction of wet air (CO2), which was originally output on the datalogger as average CO2 density (mg m-2 s-1) and converted into µmol mol-1 using air temperature and pressure in post-processing. Webb, Pearman, and Leuning terms were applied to account for density fluctuations for water vapor and CO2. Sonic path length, high-frequency attenuation and sensor separation were accounted for according to Horst and Massman, and the oxygen absorption correction for the KH2O hygrometer was also applied. Latent and sensible heat fluxes were assumed to be unrealistic above an absolute value of 1000 W m-2 and were removed. Both carbon flux (FC) and carbon dioxide mole fraction in wet air (CO2) and were assumed to be unrealistic above 1000 µmol m-2 s-1 and 1000 µmol mol-1 respectively. Spikes in latent and sensible heat and carbon fluxes and densities (often due to periods of precipitation) were identified by computing the mean and standard deviation over a moving, overlapping window of 336 half-hours (7 days), similar to Shao et al., and were removed when the flux was more than 1.5 standard deviations from the moving window’s mean. While Vickers and Mahrt use a threshold of 3.5 standard deviations from the mean, a conservative value of 1.5 was chosen due to the noisy nature of over-lake data at this particular site. This process was repeated twice for latent and sensible heat, and carbon dioxide fluxes and densities and therefore it is possible that some real, realistic data was filtered out in this process. No detrending was performed. As per AmeriFlux standards, no friction velocity (USTAR, m s-1) filtering was performed.

Spence, Chris [Environment and Climate Change Cana↗

Validation of Photovoltaic Modeling Tool Solargraf Against Measured Data

Solargraf is a cloud-based 3D design tool by Enphase Energy that allows users to design solar and storage systems with a variety of elements. Through a Cooperative Research and Development Agreement (CRADA), Enphase Energy collaborated with the National Renewable Energy Laboratory (NREL) to validate Solargraf's 3D design simulation against measured PV system performance. This study follows the same methodology of similar validation studies completed at NREL. The predicted performance results from simulations in both Solargraf and NREL's System Advisor Model (SAM) tool were compared with measured data to evaluate performance predictions.

14 SOLAR ENERGY↗

Data-Driven State of Health Estimation for Second-Life Batteries Using Interpolated Synthetic Data and Feature Selection

Accurate estimation of the State of Health (SOH) for second-life batteries (SLBs) is crucial given their increasing use in energy storage applications. Precise SOH prediction is essential for safe operation and robust battery management systems. A major challenge is the limited availability of datasets for building reliable degradation models. To address this, synthetic data generation through linear interpolation is performed to extend the available data, making it more representative of real-world battery operating conditions. By analyzing feature correlation with SOH, the most relevant features are selected for the model. The proposed approach employs a convolutional neural network (CNN) model trained on this interpolated, feature-selected dataset, using time series data of voltage, temperature, and current over a cycle. By focusing on highly correlated features, the model achieves over 95% accuracy, with mean absolute error and root mean squared error up to 2.27% and 2.64%, respectively, in SOH estimation for two battery datasets tested. These results highlight the potential of combining synthetic data generation and feature selection to enhance SOH predictions, showcasing the superior performance of the proposed CNN model for both new batteries and SLBs.

feature selection↗

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗

Evaluating the effects of heatwave events on hydrological processes in the contiguous United States (2003–2022)

Extreme heat and drought conditions are affecting water availability in many regions worldwide, leading to negative impacts on human societies, agriculture, and ecosystems. However, current research lacks comprehensive spatiotemporal analysis examining the interplay between multiple hydrological factors and heatwave events, especially in the context of climate change. This research broadly pertains to understanding the dynamics of hydrological factors and their potential responses to heatwave during warm seasons across the contiguous United States for the period from 2003 to 2022. Utilizing data from the Global Land Data Assimilation System (GLDAS), we analyzed surface runoff, evapotranspiration (ET), precipitation, Groundwater Storage (GWS), Root Zone Soil Moisture (RZSM), and Total Water Storage (TWS) to discern annual patterns and the impacts of heatwave. Further, the spatial patterns of heatwave highlighted a higher occurrence in the western, central, and northeastern U.S., with longer average durations in the western and south-central regions. These events are predominantly dry, characterized by low Relative Humidity (RH), except in the southeastern U.S., where heatwave coincide with high RH levels. Post-heatwave analysis indicated a reduction in GWS, TWS, RZSM, and ET, alongside an increase in surface runoff, RH, and precipitation. An in-depth examination of rainfall and temperature dynamics during heatwave revealed weak correlations between rainfall and temperature, as well as between rainfall and heatwave duration, highlighting the complex nature of these interactions. The study also found an enhanced probability of rainfall following heatwave, particularly in the eastern regions, drawing attention to the potential for increased flood risks post-heatwave. Our findings contribute to the growing body of knowledge on the impacts of heatwave on hydrological factors, providing valuable insights for climate change adaptation and water resource management strategies.

54 ENVIRONMENTAL SCIENCES↗

E3SM‐GCAM: A Synchronously Coupled Human Component in the E3SM Earth System Model Enables Novel Human‐Earth Feedback Research

Abstract Modeling human‐environment feedbacks is critical for assessing the effectiveness of climate change mitigation and adaptation strategies under a changing climate. The Energy Exascale Earth System Model (E3SM) now includes a human component, with the Global Change Analysis Model (GCAM) at its core, that is synchronously coupled with the land and atmosphere components through the E3SM coupling software. Terrestrial productivity is passed from E3SM to GCAM to make climate‐responsive land use and CO 2 emission projections for the next 5‐year period, which are interpolated and passed to E3SM annually. Key variables affected by the incorporation of these feedbacks include land use/cover change, crop prices, terrestrial carbon, local surface temperature, and climate extremes. Regional differences are more pronounced than global differences because the effects are driven primarily by differences in land use. This novel system enables a new type of scenario development and provides a powerful modeling framework that facilitates the addition of other feedbacks between these models. This system has the potential to explore how human responses to climate change impacts in a variety of sectors, including heating/cooling energy demand, water management, and energy production, may alter emissions trajectories and Earth system changes. Plain Language Summary Earth system models help us understand how humans are changing the climate. Currently, these models do not include human systems, so predetermined greenhouse gas, aerosol, and land use change data are input to these models. These data do not reflect human responses to changes projected by Earth system models. We have added a human component to an Earth system model to represent human responses to environmental change and calculate corresponding greenhouse gas and land use change data instead of using predetermined data. Including this human component changes projections of land use, land carbon storage, and regional climate. Key Points We have incorporated a novel, advanceable human component in an Earth system model to simulate human‐Earth feedbacks Including terrestrial productivity feedbacks from the Earth to the human systems affects land change, crop prices, carbon, and climate Regional effects of including terrestrial productivity feedbacks are greater than global effects because land change is the main driver

E3SM↗

VISTA

SAND2025-14753O VISTA Image Management is a tool that serves as a thin wrapper around S3 storage, enabling teams to create projects, upload images, and add labels to data. It uses standard open-source libraries, employs conventional authentication and authorization methods, and is expected to run behind a reverse proxy that handles authentication. 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.

Garland, Anthony [Sandia National Lab. (SNL-CA), L↗

Deploying and Operating CephFS for Scientific Applications at Fermilab

Fermilab has been running a Ceph cluster in production for several years to support high-throughput scientific computing. Our primary use case is CephFS, which serves interactive data analysis workloads, with growing interest in using RGW for scalable object storage of scientific datasets. In this talk, we'll share lessons learned from successfully deploying and maintaining our Ceph cluster with cephadm, including challenges faced, performance tuning, and operational practices. We'll also present custom tools we've developed to streamline monitoring and management and discuss how Ceph fits into our broader storage architecture for large-scale scientific research.

Peisker, Alison [Fermilab]↗

TCCSP Site Characterization and Feasibility Report

The Tulare County Carbon Storage Project (TCCSP) focused on establishing the geological and commercial viability of a 50 million metric ton (MMT) carbon storage complex located in Tulare County, California. The geologic scope of work included collecting new and existing subsurface data, such as two-dimensional (2D) seismic data, advanced geophysical logs, core and fluid samples, and specialized core-analysis datasets, to support the integration and interpretation of findings from earlier tasks. These efforts contributed to the comprehensive feasibility study for commercial carbon capture and storage (CCS) development at the TCCSP site and in the surrounding region. The TCCSP project team evaluated the feasibility of the Lower Monterey Group (Santa Margarita Sand Member or its equivalent), Temblor Formation sand members (Olcese, Jewett, Vedder), and Lodo-Martinez Zone (Domengine Sandstone, Walker Formation, Lodo Formation, and the Martinez Sand) to serve as commercial CCS reservoirs along with potential for the Upper Monterey Group (Reef Ridge, Antelope Members) and the Kreyenhagen Formation to vertically seal underlying reservoirs from shallow above-zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗

Assessing the Performance and Impact of PV Technologies on Storage in Hybrid Renewable Systems

Traditional monofacial photovoltaic (mPV) systems are commonly adopted and well-documented because of their lower upfront costs in comparison to bifacial photovoltaic (bPV) systems. This study investigates how PV technologies impact energy storage in grid-scale hybrid renewable systems, focusing on optimizing and assessing the performance of mPV and bPV technologies integrated with pumped storage hydropower. Using Ludington City, Michigan as a case study and analyzing real-world data such as solar irradiance, ambient temperature, and utility-scale load profiles, the research highlights the operational and economic benefits of bPV systems. The results reveal that bPV systems can pump approximately 10.38% more water annually to the upper reservoir while achieving a lower levelized cost of energy ($0.0578/kWh for bPV vs. $0.0672/kWh for mPV). This study underscores the outstanding potential of bPV systems in enhancing energy storage and management strategies, contributing to a more sustainable and resilient renewable energy future.

13 HYDRO ENERGY↗

Large-Scale Hydrogen Storage Cyber Risk Assessment

Hydrogen storage systems may become more widely deployed throughout the country, and so it is possible that individual and interconnected systems will be exposed to cyber-attacks. These events can cause physical and financial harm to employees, people in the vicinity of the facility, and the company that owns the facility. The two main ways bad actors may access information or control from a hydrogen storage facility are through information technology and operations technology devices, the former of which refers to data and information from networked devices and the latter of which refers to onsite controls for the physical system. Both types of entryways into the system should be considered when companies conduct cyber risk assessments and when regulators develop or revise relevant codes and standards. This report analyzes cybersecurity risks associated with a generic hydrogen storage system by outlining the system's purpose and the importance of its cybersecurity. The hydrogen storage system architecture and communication protocols are provided to understand potential cyber vulnerabilities. Later, an event tree analysis is performed on hydrogen operation to identify system weaknesses by outlining potential attack scenarios. This report also identifies critical cyber assets related to different hydrogen operations followed by an examination of potential threats, and the impact of cyber assets on those operational assets.

08 HYDROGEN↗

Large-Scale Hydrogen Storage Cyber Risk Assessment

Hydrogen storage systems are becoming more widely deployed throughout the country, and as their presence continues to grow, it is possible that individual and interconnected systems will be exposed to cyber-attacks. These events can cause physical and financial harm to employees, people in the vicinity, and to the company that owns the facility. The two main mechanisms malicious actors may access information or control from a hydrogen storage facility are through information technology and operations technology devices, the former of which refers to data and information from networked devices and the latter of which refers to onsite controls for the physical system. Both types of entryways into the system should be considered when facility managers conduct cyber risk assessments and when regulators develop or revise relevant codes and standards. This report analyzes cybersecurity risks applicable to a wide variety of hydrogen storage systems by outlining the system's purpose and the importance of its cybersecurity. The hydrogen storage system architecture and communication protocols are provided to understand potential cyber vulnerabilities. Later, an event tree analysis is performed on hydrogen operation to identify system weaknesses by outlining potential attack scenarios. This report also identifies critical cyber assets related to different hydrogen operations followed by an examination of potential threats, and the impact of cyber assets on those operational assets.

08 HYDROGEN↗

X-ray Absorption Spectroscopy and Neutron Scattering Data, Mineral Incubation Experiment, Walker Branch Watershed, TN (2020 - 2021)

Iron (Fe) (oxyhydr)oxides are well-recognized contributors to soil carbon (C) storage, but the effects of manganese (Mn) oxides on carbon storage and transformation are relatively unexplored. Here, the relative capacities of Fe and Mn oxides to bind and stabilize soil organic C were directly compared using an in-situ incubation experiment. Quartz sands coated with either poorly crystalline Mn(III/IV) oxides or Fe(III) oxides, or left uncoated, were buried in a temperate forest soil for up to one year. This data package contains processed data outputs of carbon near edge x-ray absorption fine structure spectroscopy (C NEXAFS), iron and manganese x-ray absorption near edge structure spectroscopy (XANES), and small angle neutron scattering (SANS) data collected for initial oxide-coated and uncoated quartz sands and for those buried in the temperate forest soil for 365 days.

Energy (eV)↗

Scientific computing

Our research generates a vast amount of data that scientists must gather, store, analyze and interpret. These activities require expertise and the right tools. Fermilab is home to one of the largest tape robotic systems available today, providing more than 800 petabytes of storage capability. The lab is growing the tape facilities to meet expected demands of over 1,000 petabytes of additional data per year by the end of the decade. Fermilab also has the technology and computing power to efficiently pr ocess these data to facilitate scientific discoveries. Our computation al experts work closely with experimenters and the scientific commu - nity at large to provide and facilitate the use of the cutting-edge computing tools necessary for these discoveries.

Fermilab, Fermilab↗

Deep Learning-based Surrogate Model for Efficient Reservoir Simulation in Large-scale Geological Carbon Storage: Application in IBDP Dataset

This project introduces an advanced deep learning (DL)-based surrogate modeling approach to enhance the efficiency and accuracy of large-scale geological carbon storage (GCS) simulations. Using the Illinois Basin Decatur Project (IBDP) dataset as training data, the study employs a residual U-Net architecture to predict critical state variables such as pressure and CO₂ saturation, as well as CO₂ plume migration. By incorporating key geological parameters (e.g., porosity, permeability, and rock facies) and physics-informed inputs like the diffusive time of flight and time step, the DL model effectively reduces computational complexity while maintaining robust physical constraints. Compared to traditional simulators like Eclipse, the DL model achieves remarkable accuracy, with a root mean square error (RMSE) of 1.57 psi for pressure and 0.007 for saturation, and dramatically reduces computational time from hours to just 69.9 seconds for 50-step simulations. These results demonstrate the potential of innovative DL methodologies to improve the predictivity and operational efficiency of GCS simulations, providing a reliable foundation for decision-making in CCS operations. Supported by the SMART initiative, this project underscores the success of leveraging computational innovations to advance CCS technologies.

advanced deep learning↗