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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Natrium Demonstration Reactor Support [Abstract]

TerraPower, LLC (TerraPower, Participant) and other private industry partners endeavor to design, license, construct, and operate a sodium-cooled fast-spectrum nuclear reactor technology demonstration plant called Natrium. This demonstration plant is supported by the U.S. Department of Energy (DOE) through the Advanced Reactor Demonstration Program (ARDP; DE-FOA-0002271). TerraPower, together with its technology co-developer GE Hitachi Nuclear Energy (GEH) and engineering and construction partner Bechtel, submitted a proposal under the program’s Advanced Reactor Demonstration Pathway for its Natrium reactor and energy system and recently received an award. TerraPower is partnering with Battelle Memorial Institute, the Management and Operating Contractor of Pacific Northwest National Laboratory (PNNL, Contractor) under the Natrium project to provide critical research outcomes necessary to demonstrate the reactor technology. Over the expected five-year timeframe of the project, PNNL will provide TerraPower and its partners with vital support in the areas of post-irradiation examination (PIE) of specimens irradiated in test reactors. These efforts will be combined and managed as a program titled “Natrium Demonstration Reactor Support.”

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Advanced Computing Annual Report 2025 [Slides]

In Fiscal Year (FY) 2025, the National Laboratory of the Rockies (NLR) continued to advance computing as a cornerstone of energy innovation, expanding the Kestrel high-performance computing (HPC) system to 56 peak petaflops. This growth strengthened Kestrel's role as a national asset for applied energy research, enabling larger, more complex simulations and accelerating the integration of artificial intelligence (AI) methods across the laboratory's computing portfolio. In FY 2025, AI was a component of most projects running on Kestrel, underscoring its central role in modern energy science and engineering. Kestrel supported a broad and diverse set of 507 modeling and simulation projects, engaging 855 researchers across the U.S. Department of Energy's (DOE's) Office of Critical Minerals and Energy Innovation (CMEI) portfolio and other offices, as well as partners from industry, academia, and utilities. These efforts span critical materials discovery, energy systems modeling, grid modernization, advanced manufacturing, and other areas essential to strengthening U.S. energy security and competitiveness. Together, these collaborations produced 708 technical outputs, including 293 peer-reviewed publications, reflecting both the depth and impact of the science enabled by NLR's computing capabilities. This year's report highlights the growing importance and benefit of AI throughout NLR's research programs and features work by early career researchers who are helping shape the future of computing-enabled energy innovation. Explore these sections and the many project successes captured in the pages that follow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Chemical Heterointerface Engineering on Hybrid Electrode Materials for Electrochemical Energy Storage

Abstract The chemical heterointerfaces in hybrid electrode materials play an important role in overcoming the intrinsic drawbacks of individual materials and thus expedite the in‐depth development of electrochemical energy storage. Benefiting from the three enhancement effects of accelerating charge transport, increasing the number of storage sites, and reinforcing structural stability, the chemical heterointerfaces have attracted extensive interest and the electrochemical performances of hybrid electrode materials have been significantly optimized. In this review, recent advances regarding chemical heterointerface engineering in hybrid electrode materials are systematically summarized. Especially, the intrinsic behaviors of chemical heterointerfaces on hybrid electrode materials are refined based on built‐in electric field, van der Waals interaction, lattice mismatch and connection, electron cloud bias and chemical bond, and their combination. The strategies for introducing chemical heterointerfaces are classified into in situ local transformation, in situ growth, cosynthesis, and other strategy. The recent progress about the chemical heterointerfaces engineering specially focusing on metal‐ion batteries, supercapacitors, and Li–S batteries are introduced in detail. Furthermore, the classification and characterization of chemical heterointerfaces are briefly described. Finally, the emerging challenges and perspectives about future directions of chemical heterointerface engineering are proposed.

Li, Wenbin↗
DOE OSTI2020

Investigation of fouling mechanisms for diesel engine exhaust heat recovery

The effect of diesel engine exhaust fouling on heat and mass exchangers for waste heat recovery is investigated. Experiments are conducted for a range of operating conditions relevant to the use of the waste heat from a diesel engine for powering a 2.71 kW capacity absorption heat pump. Thermal resistances of fouling layers as well as their effects on pressure drop are determined, and the underlying mechanisms for deposition and removal are investigated. Fouling layer effects are most severe for the lowest coolant temperatures and 100% generator load. At these conditions, the fouling thermal resistance is approximately 70% of the total resistance, and the ratio of the steady-state pressure drop to the baseline pressure drop is 3.25. Flow induced shear is found to be the primary growth inhibiting mechanism at high exhaust velocities. Findings from this study can guide heat exchanger design for a variety of waste heat recovery systems.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2020

Distribution of rare earth elements in fly ash derived from the combustion of Illinois Basin coals

This study examined rare earth element (REE) trends for Illinois Basin coal-sourced fly ashes, with the goal of understanding the elemental composition and resource potential for various fly ash fractions. Illinois Basin coals have a high volatile C through A bituminous rank with a moderate ash content (slightly>12% ash (dry basis)), about 3% sulfur, and, in general, lower concentrations of hazardous and other trace elements than many Central Appalachian coals. Fly ash from the combustion of Illinois Basin coals tends to have a high Fe 2 O 3 content owing to the amount of pyrite in the feed coals. The rare earth element (REE) concentrations in Illinois Basin coal-sourced fly ashes are less than that for fly ashes from the combustion of Central Appalachian coals. The Upper continental crust-corrected fly ashes show an H-type enrichment, a positive Eu N /Eu N * , and, in some cases, a sharp Gd peak. For comparison, a suite of fly ashes from the combustion of a blend of eastern Kentucky coals had an H-type enrichment, a positive Eu N /Eu N * , but only a minimal Gd peak. In contrast, fly ash from the combustion of the Fire Clay coal, a REE-rich coal, had a negative Eu N /Eu N * and a sharp Gd peak. Finally, these results highlight the importance of feed coal composition on trace element contents of respective combustion fly ash fractions and also the unique REE enrichment patterns of the Illinois Basin fly ashes relative to the better studied fly ashes of eastern Kentucky and Central Appalachia.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Thermodynamic modeling of MDEA(aq)-NH 3 (aq)-K 2 CO 3 (aq)-CO 2 (aq) using the Extended UNIQUAC model

Here, the development of advanced processes for carbon dioxide capture requires reliable thermodynamic models to ensure accurate process design. This work presents an optimized parameter set of the Extended UNIQUAC model for advanced mixed solvent solutions containing CO 2 -MDEA-NH 3 -K 2 CO 3 in water. The model was parameterized using vapor–liquid equilibrium, pure component saturation pressure, excess enthalpy, heat of absorption, molar heat capacity, apparent molar heat capacity, and solid–liquid equilibrium. These thermal and equilibrium properties cover temperatures between 253.6 K to 600 K and pressures up to 75.6 bar. The current model represents an extension of previous versions that were suitable for modeling aqueous NH 3 and K 2 CO 3 solutions. This parametrization accounts for molecular interactions between MDEA and these two components, which allows for the design of advanced capture processes. This paper demonstrates that the new model parameters are valid within the range of operational conditions typically found in CO 2 capture processes.

42 ENGINEERING↗
DOE OSTI2021

A generalized machine learning workflow to visualize mechanical discontinuity

Accurate detection and mapping of mechanical discontinuity in materials has widespread industrial and research applications. Herein, we developed a generalized machine-learning framework for visualizing single mechanical discontinuity embedded in material of any composition, velocity, density, porosity, and size with limited data. The proposed visualization of discontinuity requires accurate estimations of the length, location, and orientation of the embedded discontinuity by processing multipoint wave-transmission measurements. k-Wave simulator is used to create a large dataset of elastic waveforms recorded during multi-point wave-transmission measurements through materials containing single mechanical discontinuity. k-Wave simulator considers the wave attenuation, dispersion, and mode conversion in wave motion. Discrete wavelet transform (DWT) and statistical feature extraction are essential for data preprocessing prior to the data-driven model development. DWT also minimizes the effect of noise. Using hyper-parameter tuning and cross validation, gradient boosting regression can visualize the mechanical discontinuity with an accuracy of 0.85, in terms of coefficient of determination. A double-layered neural network-based regression has better performance with an accuracy of 0.95. Use of convolutional neural network converts the predictive task from a waveform processing to an image processing problem. Convolutional neural network achieved a generalization performance of 0.91. The proposed generalized workflow requires robust simulation of wave propagation, signal processing, feature engineering, and model evaluation. Sensors closest to the source and those located opposite the source are the most significant for the desired visualization. Notably, the sensors closest to the source capture the non-linear associations, whereas the sensor on the border opposite to the source capture the linear associations between the measured waveforms and the properties of the mechanical discontinuity.

42 ENGINEERING↗
DOE OSTI2021

Non-fullerene acceptor organic photovoltaics with intrinsic operational lifetimes over 30 years

Abstract Organic photovoltaic cells (OPVs) have the potential of becoming a productive renewable energy technology if the requirements of low cost, high efficiency and prolonged lifetime are simultaneously fulfilled. So far, the remaining unfulfilled promise of this technology is its inadequate operational lifetime. Here, we demonstrate that the instability of NFA solar cells arises primarily from chemical changes at organic/inorganic interfaces bounding the bulk heterojunction active region. Encapsulated devices stabilized by additional protective buffer layers as well as the integration of a simple solution processed ultraviolet filtering layer, maintain 94% of their initial efficiency under simulated, 1 sun intensity, AM1.5 G irradiation for 1900 hours at 55 °C. Accelerated aging is also induced by exposure of light illumination intensities up to 27 suns, and operation temperatures as high as 65 °C. An extrapolated intrinsic lifetime of > 5.6 × 10 4 h is obtained, which is equivalent to 30 years outdoor exposure.

14 SOLAR ENERGY↗
DOE OSTI2023

Mapping the pathways of photo-induced ion migration in organic-inorganic hybrid halide perovskites

Abstract Organic-inorganic hybrid perovskites exhibiting exceptional photovoltaic and optoelectronic properties are of fundamental and practical interest, owing to their tunability and low manufacturing cost. For practical applications, however, challenges such as material instability and the photocurrent hysteresis occurring in perovskite solar cells under light exposure need to be understood and addressed. While extensive investigations have suggested that ion migration is a plausible origin of these detrimental effects, detailed understanding of the ion migration pathways remains elusive. Here, we report the characterization of photo-induced ion migration in perovskites using in situ laser illumination inside a scanning electron microscope, coupled with secondary electron imaging, energy-dispersive X-ray spectroscopy and cathodoluminescence with varying primary electron energies. Using methylammonium lead iodide and formamidinium lead iodide as model systems, we observed photo-induced long-range migration of halide ions over hundreds of micrometers and elucidated the transport pathways of various ions both near the surface and inside the bulk of the samples, including a surprising finding of the vertical migration of lead ions. Our study provides insights into ion migration processes in perovskites that can aid perovskite material design and processing in future applications.

14 SOLAR ENERGY↗
DOE OSTI2024

Achieving gigawatt-scale green hydrogen production and seasonal storage at industrial locations across the U.S

Onsite production of gigawatt-scale wind- and solar-sourced hydrogen (H 2 ) at industrial locations depends on the ability to store and deliver otherwise-curtailed H 2 during times of power shortages. Thousands of tonnes of H 2 will require storage in regions where subsurface storage is scarce, which may only be possible using liquid organic H 2 carriers. We evaluate aboveground system with a focus on providing technical insights into toluene/methylcyclohexane (TOL/MCH) storage systems in locations suitable for gigawatt-scale wind- and solar-powered electrolyzer systems in the United States. Here we show that the levelized cost of storage, at a national median of US dollar $1.84/kg-H 2 is spatially heterogeneous, causing minor impact on the cost of H 2 supply in the Midwest, and significant impact in Central California and the Southeast. While TOL/MCH may be the cheapest aboveground bulk storage solution evaluated, upfront capital costs, modest energy efficiency, reliance on critical materials and pre-sulfided catalysts, and greenhouse gas emissions from heating are opportunities for further development.

14 SOLAR ENERGY↗
DOE OSTI2023

EnergyPlus™ v.23.2.0 2023 [SWR-17-23]

EnergyPlus™ is a whole building energy simulation program that engineers, architects, and researchers use to model both energy consumption and water use in buildings. This is the EnergyPlus Development Repository. The Department of Energy maintains a public website for EnergyPlus where you can find much more information about the program. https://energyplus.net/ For detailed developer information, consult the wiki. https://github.com/NREL/EnergyPlus/wiki Many users (and developers) of EnergyPlus are active on Unmet Hours, so that's a great place to start if you have a question about EnergyPlus or building simulation. https://unmethours.com/questions/ For more in-depth, developer-driven support, please utilize the EnergyPlus Helpdesk. https://energyplushelp.freshdesk.com/support/home

Woods, Jason↗
DOE OSTI2022

Synthesis Methods for Nanoparticle Morphology Control in Energy Applications

Lightweight nano-composite materials, nano-coatings, nanocatalysts, nano-structured materials have demonstrated an ability to reduce emissions and maximize clean energy production. Nanoparticles play an important role in engineering and decarbonization for energy applications, and a wide range of nanoparticle synthesis methods have been developed to include those that enable control over particle morphology. The ability to control nanoparticle morphology allows the tailoring and improvement of material properties that will accelerate efforts towards lowering carbon emissions by developing advanced catalysts for carbon sequestration and will enhance energy efficient processes and technologies. Synthesis methods aimed towards shape control of nanoparticles have demonstrated an ability to form spheres, rods, flower-like shapes, cubes, plates, shells, and chiral geometries. Processing methods used to form these morphologies include microwave assisted synthesis, solvothermal, hydrothermal, and a wide range of capping agents. A discussion of a few of these methods is given along with results and applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Lessons Learned From Techno-Economic Analysis of Solar Photovoltaics and Battery Energy Storage at a Vietnam Industrial Park

Through the Clean Energy Investment Accelerator (CEIA), engineers from the United States (U.S.) National Renewable Energy Laboratory (NREL) conducted a case study analysis evaluating the techno-economic feasibility of battery energy storage systems (BESS) at an industrial park in Vietnam. The analysis uses NREL's REopt platform, a distributed energy modeling and optimization tool, to identify the cost-optimal system sizing for BESS, in conjunction with onsite renewables such as solar photovoltaics (PV), to reduce electricity costs, increase onsite renewable energy generation utilization, and improve resilience to grid outages.

batteries↗
DOE OSTI2024

2022 Component Innovation Awardee: Pecos Wind Power

Distributed wind energy systems supply electricity for nearby homes and businesses, often in remote and rural regions. Systems capable of capturing energy in areas with low wind speeds make it possible to deploy these clean energy systems in many geographic locations. Pecos Wind Power's 85-kilowatt (kW) horizontal-axis PW85 wind turbine shows potential to reduce the levelized cost of energy by approximately 55% when compared to typical small wind systems while achieving utility-scale capacity factors of more than 40% in areas with lower wind speeds. To maximize cost competitiveness, the Pecos Wind Power turbine needs a blade optimized for low wind speeds. Through its 2022 Competitiveness Improvement Project (CIP) award, Pecos Wind Power and partner Wetzel Wind Energy Services will model, optimize, fabricate, and validate a 14.5-meter wind turbine blade made specifically for these applications. Three prior CIP awards supported Pecos Wind Power's initial development of the PW85 turbine.

CIP↗
DOE OSTI2024

Department of Energy/IDEA: More best-practices research

This article continues the overview of the U.S. Department of Energy and IDEA collaboration by highlighting additional case studies that demonstrate innovative engineering approaches in district energy systems. It focuses on how campuses are improving efficiency, integrating advanced technologies, and reducing energy use and emissions through data-driven strategies. The University of Cincinnati is featured for its highly efficient chilled water system, using advanced algorithms and upgraded “smart chillers” to optimize performance, reduce energy consumption, and lower costs. Wake Forest University emphasizes a data-informed modernization process, using system optimization and targeted building improvements to significantly boost efficiency and reduce overall energy use. Arizona Western College showcases creative solutions to extreme cooling demands, including system redesign, smart controls, and thermal storage to improve reliability and cut peak energy costs. Overall, the article demonstrates that combining innovative engineering, real-time data analysis, and system optimization can significantly enhance performance, reduce emissions, and deliver cost savings in district energy systems.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Resilient, Rural, and Revolutionary: Salisbury Square's Direct-Current Affordable Microgrid Community: Preprint

The technology to interconnect buildings with a dedicated direct-current (DC) power distribution network is in place today; what is missing is a turnkey approach to designing a DC microgrid - and the business models allowing such systems to be deployed, owned, and operated at scale. To close this gap, the Salisbury Square Development Team, comprising clean-energy experts, has engineered a resilient community DC microgrid for an affordable housing community in Randolph, Vermont. Ten single-family, occupant-owned residences and 12 multifamily rental units will share locally generated and stored solar energy via a DC power distribution bus capable of operating during extended grid outages. With a DC power distribution network in place, each home will be equipped with high-efficiency DC lighting and appliances, operating alongside alternating current (AC) appliances, even during an islanded mode of operation. To obtain a comprehensive understanding of what is possible and achievable, the Team collaborated with the local utility, regulatory agencies, a national laboratory, energy-as-service providers, and vendors. The collaborators evaluated microgrid typologies, business models, and energy modeling, and analyzed electrification and resilience. Further, the Team applied the URBANoptTM (Urban Renewable Building and Neighborhood optimization, NREL 2022) software development kit (SDK) to Salisbury Square's single-family and multifamily buildings to validate workflows and identify needs for advanced capability. This paper addresses the barriers to entry, scalability, and impact on residents and system ownership. It also examines the analysis that informed the design and engineering of the DC microgrid and the opportunities to streamline the process.

DER↗
DOE OSTI2021

Enhanced Coherence in Superconducting Circuits via Band Engineering

In superconducting circuits interrupted by Josephson junctions, the dependence of the energy spectrum on offset charges on different islands is 2e periodic through the Aharonov-Casher effect and resembles a crystal band structure that reflects the symmetries of the Josephson potential. We show that higher-harmonic Josephson elements described by a cos(2φ) energy-phase relation provide an increased freedom to tailor the shape of the Josephson potential and design spectra featuring multiplets of flat bands and Dirac points in the charge Brillouin zone. Flat bands provide noise-insensitive energy levels, and consequently, engineering band pairs with flat spectral gaps can help improve the coherence of the system. We discuss a modified version of a flux qubit that achieves, in principle, no decoherence from charge noise and introduce a flux qutrit that shows a spin-1 Dirac spectrum and is simultaneously quite robust to both charge and flux noise.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Optimal carbon storage reservoir management through deep reinforcement learning

Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decision-making problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Finally, experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.

15 GEOTHERMAL ENERGY↗