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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Evaluation of Nominal Energy Storage at Existing Hydropower Reservoirs in the US

Long-term planning and operation of hydropower reservoirs require an understanding of both water and energy storage. As energy storage needs of the evolving grid increase, we must account for the water and energy storage potential of these reservoirs. Given the limitations of current data on existing hydropower, we compile statistics related to storage volume and hydraulic head from publicly available data sets and examine differences in descriptions of US hydropower storage. Assembled characteristics are used to calculate nominal energy storage capacity, a simple measure of potential to generate power from a given volume of water, not factoring in detailed constraints. Inventory-based estimates of energy storage are calculated at 2,075 dams, which helps put the potential for US hydropower to support energy storage in context with similar evaluations in other regions and with other energy storage technologies. The national energy storage capacity ranges between 34.5 and 45.1 TWh depending on the information used, with 52% of energy storage located at the 10 largest reservoirs in the US. Energy storage capacities are also calculated at 236 dams with historical volume and elevation data. Finally, reservoir inflows provide context for the storage volumes and sensitivities to hydrologic variability. Larger reservoirs with greater storage volume to inflow ratios are concentrated in the Western US, but the majority of hydropower reservoirs store less than the annual inflow. We address several infrastructure and water resource informatics challenges and highlight remaining issues, including representing seasonal or shorter variability in water volumes and representing connected hydropower facilities.

13 HYDRO ENERGY↗
DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗
DOE OSTI2020

Microstructure reconstruction of battery polymer separators by fusing 2D and 3D image data for transport property analysis

A new approach to generate high-fidelity 3D microstructure reconstructions by leveraging resolution and sample volume characteristics from 2D and 3D microscopy methods is presented here. This approach is employed to model the microstructure of a highly orthotropic polypropylene separator used in lithium-ion batteries, which have challenging multi-scale features of fibrils (<100 nm) and lamellae (>100 nm) to resolve in 3D. Phase contrast nano X-ray computed tomographic data are used to reconstruct the lamellae phase, while 2D scanning electron microscopy data are used to characterize sub-100 nm microstructure features such as the thin fibrils that are beyond the effective resolution of X-ray computed tomography. Fibril geometries are reconstructed stochastically based on the 2D higher resolution data, and integrated with the lamellae geometries in 3D space. Transport property analyses are performed to investigate the bias of microstructure models without considering the fibrils. A sensitivity study is also conducted to facilitate understanding of the relationship between microstructure characteristics and transport properties.

25 ENERGY STORAGE↗
DOE OSTI2024

Machine learning-guided design of direct methanol fuel cells with a platinum group metal-free cathode

Direct methanol fuel cells (DMFCs) offer a promising solution for clean electricity generation, particularly in small electronics and remote auxiliary power units. However, optimizing their efficiency and performance is challenging due to the complex interactions between various factors. Here, we present a novel approach that integrates experiments with machine learning to model and predict the performance of these fuel cells using atomically dispersed platinum group metal (PGM)-free catalysts at the cathode. Further, our machine learning models, trained on diverse input parameters, allow for the comprehensive optimization of DMFC performance prior to fabrication and testing. Through extensive experimental validation, we demonstrate that this data-driven approach accurately predicts key performance metrics, such as maximum power output and polarization curves. By combining our models with interpretable game-theory methods, we provide deep insights into the factors governing fuel cell performance, ultimately paving the way for the design of scalable and efficient DMFC technologies.

25 ENERGY STORAGE↗
DOE OSTI2022

Perspective—Is Sustainable Electrowinning of Neodymium Metal Achievable?

Rare–earth metals such as neodymium are central to numerous technologically important applications such as electrified transportation, renewable energy harvesting, and magnetic data storage. Presently, neodymium metal is produced via electrolysis of Nd 2 O 3 –containing NdF 3 –LiF molten salts at high temperatures. However, this conventional electrolytic route has several drawbacks including emissions of greenhouse gases such as CO 2 and perfluorocarbons, which are harmful to the environment. In this perspective, an alternate concept based on electrolysis of NdCl 3 –containing chloride–based molten salts is explored and its potential advantages are discussed. Notably, NdCl 3 can be obtained via direct non–carbothermic chlorination of Nd 2 O 3 , supported by thermodynamic calculations and experimental evidence from literature. The chloride–based molten salt electrolysis route also offers the possibility of incorporating a non–consumable or dimensionally stable anode leading to reduced electrical energy consumption, stable cell operation and ease of process scalability.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-throughput solubility determination for data-driven materials design and discovery in redox flow battery research

Solubility is crucial for redox flow batteries because it affects their energy density. A data-driven approach based on artificial intelligence/machine learning models can accelerate the development of highly soluble redox-active materials, but the lack of relevant, large-quantity data makes accurate solubility prediction difficult. To overcome this deficiency, we developed a high-throughput experimentation process that combines a robotically controlled platform with high-throughput methodology to collect large-scale and high-quality solubility data. We demonstrate the potential utility and applicability of this high-throughput process by measuring the aqueous and non-aqueous solubilities of redox-active materials and studying the effect of additives on their solubilities for both aqueous and non-aqueous redox flow battery applications. A redox flow battery based on our optimized negative electrolyte formulation and a ferrocyanide-positive electrolyte offers highly stable performance over 18 days (>100 cycles) with consistent capacity and a 24% boost in energy density.

25 ENERGY STORAGE↗
DOE OSTI2022

Progress toward picosecond on-chip magnetic memory

Here, we offer a perspective on the prospects of ultrafast spintronics and opto-magnetism as a pathway to high-performance, energy-efficient, and non-volatile embedded memory in digital integrated circuit applications. Conventional spintronic devices, such as spin-transfer-torque magnetic-resistive random-access memory (STT-MRAM) and spin–orbit torque MRAM, are promising due to their non-volatility, energy-efficiency, and high endurance. STT-MRAMs are now entering into the commercial market; however, they are limited in write speed to the nanosecond timescale. Improvement in the write speed of spintronic devices can significantly increase their usefulness as viable alternatives to the existing CMOS-based devices. In this article, we discuss recent studies that advance the field of ultrafast spintronics and opto-magnetism. An optimized ferromagnet–ferrimagnet exchange-coupled magnetic stack, which can serve as the free layer of a magnetic tunnel junction (MTJ), can be optically switched in as fast as ~3 ps. Integration of ultrafast magnetic switching of a similar stack into an MTJ device has enabled electrical readout of the switched state using a relatively larger tunneling magnetoresistance ratio. Purely electronic ultrafast spin–orbit torque induced switching of a ferromagnet has been demonstrated using ~6 ps long charge current pulses. We conclude our Perspective by discussing some of the challenges that remain to be addressed to accelerate ultrafast spintronics technologies toward practical implementation in high-performance digital information processing systems.

42 ENGINEERING↗
DOE OSTI2023

Metal hydride composition-derived parameters as machine learning features for material design and H 2 storage

Though hydrogen is a promising energy carrier for a green future, many challenges persist. One is the difficulty in engineering storage solutions, with metal hydrides being a leading contender among solid-state strategies. To facilitate efficient searching of candidate materials, ridge regression, simple decision trees, random forest ensembles, and gradient boosting ensembles were employed to predict the energy of formation, with the random forest ensemble resulting in the lowest test set error. First, two public databases, Materials Project and HydPark, were searched for metal hydrides. Feature engineering was performed before the models were developed, resulting in electronegativity, density, atomic density, d-character, f-character, band gap, hydrogen weight fraction, magnetization, temperature, and pressure being retained. The models were then benchmarked by the lowest test error before a random forest ensemble was used to populate entries missing energy of formation. Furthermore, all were then scored by hydrogen storage capacity and energy of formation suitability. Readily available features including several derived from only the chemical formula which were found to be highly predictive. and so are promising for high-throughput screening of arbitrary novel hydride formulations and blends for thermodynamic feasibility.

25 ENERGY STORAGE↗
DOE OSTI2026

Statistical White-Line Analysis in High-Throughput TXM-XANES for Chemical State Quantification

The transmission X-ray microscopy (TXM) based X-ray absorption near-edge structure (XANES) technique provides three-dimensional mapping of element-specific chemical states at nanometer-scale spatial resolution and micrometer-scale fields of view. However, compared to conventional volume-averaged XANES (VA-XANES) measurements, the inherently small voxel size in TXM-XANES leads to a lower signal-to-noise ratio, making full-spectrum analysis computationally demanding and less robust. Here, we present the structural and compositional conditions for a statistical white-line analysis framework under which chemical state information can be directly extracted from the white-line peak position in voxel spectra without the need for voxel-wise background subtraction or normalization, under well-defined structural and compositional conditions. The method is validated on layered oxide cathode materials, where low-order polynomial fitting accurately reproduces white-line features, and the extracted energy distributions correlate strongly with VA-XANES results. This statistical approach enables high-throughput, dose-efficient, and noise-robust chemical state quantification in TXM-XANES, offering broad applicability to functional materials requiring nanoscale oxidation-state mapping.

TXM↗
DOE OSTI2025

A Safe Response to Renewable Energy Hazards

The International Association of Fire Fighters (IAFF) and Underwriters Laboratories, LLC (UL) in conjunction with UL Solutions initiated a joint project in 2022 under an agreement with the United States Department of Energy-Office of Energy Efficiency and Renewable Energy (DOE-EERE). This project focused on two separate and important initiatives related to energy efficiency in residential buildings. Initiative 1: Fire Performance on Energy Efficient Exterior Walls. Initiative 2: Firefighting Tactics in Residential Properties with Building Energy Storage Systems (BESS). The project’s first initiative addresses concerns surrounding new technologies with enhanced, energy-efficient exterior walls installed on residential properties. The concerns of fire rapidly traveling vertically up the exterior of these walls were examined. This addressed a growing concern from the first responder community that many times, the fires on the exterior of residential buildings have already evolved into an attic fire by the time of arrival – making it problematic to address the fire scenario. The test plan for Initiative 1 incorporated a modified version of an American Society for Testing and Materials (ASTM) test method, ASTM E2707, Standard Test Method for Determining Fire Penetration of Exterior Wall Assemblies Using a Direct Flame Impingement Exposure, as the foundation of the research. The test method involved a wall structure intended to represent retrofit construction to evaluate how fire would spread vertically or laterally. The second aspect of the UL-IAFF Project focuses on the fire service response to Residential Battery Energy Storage System (RBESS) incidents. These simulation tests were constructed in the large-scale fire test facility at UL Solutions’ Northbrook, IL campus. A baseline test was conducted that involved a test structure with no batteries—shelving units populated with standardized commodities, representing a typical garage with cellulosic and plastic contents. Three additional tests have been conducted to generate data with the contribution of energy storage system (ESS) batteries to compare fire and explosion hazards against the baseline test. Through this work, fire service tactical considerations can be explored. From the data, the team can determine 1) the visual indicators of a residential fire that has involved an RBESS (or, potentially, other large batteries) and 2) the impact of fire service-initiated ventilation of the structure on the fire conditions and explosion risks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

EASY-SHIFT v Alpha

The software is a generic, price- and load-responsive control algorithm integrating heat pumps with thermal energy storage. The algorithm leverages simple models of the system and easily accessible data to schedule operation of heat pumps and thermal energy storage in ways that minimize the cost of operating the heating/cooling system. This tool is specifically designed to be easy to interact with, and something that industry partners are able to adopt. There are two current state of the art approaches. Industry tends to develop very simple algorithms, with predetermined schedules that are not capable of changing operation in response to changes in operating environment. For example, a control designed to avoid high-price electricity from 5-8 PM will not be able to adapt if the high-price period changes to 4-9 PM. Academia commonly develops algorithms called Model predictive control (MPC). MPC requires extensive data and highly trained staff to develop a specific type of simulation model of the building, connect the building to optimization algorithms, and leverage powerful computers. Industry, with limited time/finance budgets for any project, is resistant to adopting MPC due to the associated high complexity and cost.

Grant, Peter [Lawrence Berkeley National Laborator↗
DOE OSTI2025

Technical Track on Biomass Carbon Removal and Storage (BiCRS): Mapping bioresources, phase 1 - Consistency check comparing Mission Innovation’s Data Visualization Tool for Bioresources and the Clean Energy Ministerial Biofuture Initiative Global Biomass data accessible via the US Department of Energy’s Bioenergy Knowledge Discovery Framework (KDF)

The Mission Innovation (MI) Carbon Dioxide Removal (CDR) Mission, Technical Track on Biomass Carbon Dioxide Removal and Storage (BiCRS), has produced a biomass resource database for its members. In parallel, Oak Ridge National Laboratory (ORNL) developed the International Feedstock Reporting data portal—herein referred to as the CEM Biofuture-KDF data—on behalf of the Clean Energy Ministerial Biofuture Initiative (CEM Biofuture), as a specific task under Biofuture’s 2024–25 Action Plan. This work was conducted at the request of CEM Biofuture and funded by the U.S. Department of Energy in support of that initiative, and it is hosted within DOE’s Knowledge Discovery Framework (KDF).

09 BIOMASS FUELS↗
DOE OSTI2024

Parallel IO Libraries for Managing HEP Experimental Data

The computing and storage requirements of the energy and intensity frontiers will grow significantly during the Run 4 & 5 and the HL-LHC era. Similarly, in the intensity frontier, with larger trig ger readouts during supernovae explosions, the Deep Underground Neutrino Experiment (DUNE) will have unique computing challenges that could be addressed by the use of parallel and accelerated dataprocessing capabilities. Most of the requirements of the energy and intensity frontier experiments rely on increasing the role of high performance computing (HPC) in the HEP community. In this presentation, we will describe our ongoing efforts that are focused on using HPC resources for the next generation HEP experiments. The HEPCCE (High Energy Physics-Center for Computational Excellence) IOS (Input/Output and Storage) group has been developing approaches to map HEP data to the HDF5 , an IO library optimized for the HPC platforms to store the intermediate HEP data. The complex HEP data products are serialized using ROOT to allow for experiment independent general mapping approaches of the HEP data to the HDF5 format. The mapping approaches can be optimized for high performance parallel IO. Similarly, simpler data can be directly mapped into the HDF5, which can also be suitable for offloading into the GPUs directly. We will present our works on both complex and simple data model models.

Bashyal, Amit↗