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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 OSTI2025

Oak Ridge National Laboratory EAGLE-I TM : Modeling Electric Utility County Customers for Situational Awareness

During natural hazard events (hurricanes, wildfires, earthquakes, etc.) and recent man-made events (e.g., cyber attacks), the exchange of near real-time, spatially refined data within the response community is critical. The EAGLE-I$^{TM}$ platform is one tool that facilitates this data for decision makers within the energy sector. While much information can be collected and integrated into the system directly, other pertinent data must be augmented by other derived data products to enhance the information and allow for a consistent evaluation of on-the-ground conditions. One such data set that requires the addition of other derived data is the electric utility customer outage data that is aggregated to the county level within the EAGLE-I application. Without a county customer data set, outages can only be compared on total counts, which gives greater importance to higher population outages. Including an electric utility customer data set at the county level allows for these outage counts to be converted to percent outages and brings a consistent classification of outages and equal importance to all outages. To achieve this, several available data sets were combined and spatial disaggregation techniques were employed to model customer estimates at the county scale. This paper presents the approach to produce this data for the United States and lessons learned from working with these disparate data sets. Data validation is provided, where possible, and limitations of the model and possible improvements are discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Response of ZrC to swift heavy ion irradiation

Zirconium carbide (ZrC) is commonly used for energy sector research, as well as a surrogate for the proposed advanced nuclear fuel candidate uranium carbide. Here this study investigates structural modifications to nanocrystalline and microcrystalline ZrC resulting from dense electronic excitations induced by swift heavy ion exposure. Samples were irradiated with 946 MeV Au ions to various fluences up to 6 × 10 13 ions cm –2 and characterized using synchrotron-based x-ray diffraction. The evolution of the unit-cell parameter and heterogeneous microstrain were evaluated as a function of fluence and compared with those of nanocrystalline and microcrystalline CeO 2 (a surrogate for UO 2 fuel) irradiated under identical conditions. Distinct differences were observed in the radiation responses of the carbide and oxide across both grain sizes. Most notably, microcrystalline ZrC exhibits swelling characterized by two distinct regimes, which does not result in saturation at the ion fluences achieved. This contrasts with CeO 2 , which exhibits the well-documented direct-impact defect accumulation mechanism, reaching a steady-state saturation of swelling at higher fluences. Nanocrystalline CeO 2 undergoes more pronounced swelling compared with microcrystalline CeO 2 , in contrast to nanocrystalline ZrC, which exhibits only minimal unit-cell changes. These results demonstrate that swift heavy ion-induced structural changes can be quite different in carbides and oxides, which must be considered when extrapolating fission-fragment type damage in current fuels to advanced fuels.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

A semantics-driven framework to enable demand flexibility control applications in real buildings

Decarbonising and digitalising the energy sector requires scalable and interoperable Demand Flexibility (DF) applications. Semantic models are promising technologies for achieving these goals, but existing studies focused on DF applications exhibit limitations. These include dependence on bespoke ontologies, lack of computational methods to generate semantic models, ineffective temporal data management and absence of platforms that use these models to easily develop, configure and deploy controls in real buildings. This paper introduces a semantics-driven framework to enable DF control applications in real buildings. The framework supports the generation of semantic models that adhere to Brick and SAREF while using metadata from Building Information Models (BIM) and Building Automation Systems (BAS). The work also introduces a web platform that leverages these models and an actor and microservices architecture to streamline the development, configuration and deployment of DF controls. The paper demonstrates the framework through a case study, illustrating its ability to integrate diverse data sources, execute DF actuation in a real building, and promote modularity for easy reuse, extension, and customisation of applications. The paper also discusses the alignment between Brick and SAREF, the value of leveraging BIM data sources, and the framework's benefits over existing approaches, demonstrating a 75% reduction in effort for developing, configuring, and deploying building controls.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Modeling cross-beam energy transfer with sector ray tracing

Ray-based cross-beam energy transfer (CBET) models are an essential feature of the radiation-hydrodynamic codes used to simulate inertial confinement fusion implosions, but full 3D ray-based CBET calculations can have a prohibitively high computational cost. Sector ray tracing can be used to reduce the cost by orders of magnitude in cases where the coronal plasma and laser drive can be approximated as spherically symmetric. An extension of sector ray tracing (section ray tracing) can be used to relax the assumption of a spherically symmetric drive while still retaining most of the computational savings of a sector ray trace. We discuss the foundations of sector and section ray tracing and compare them to full ray tracing.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Application of electrospinning for the fabrication of proton-exchange membrane fuel cell electrodes

We report electrospun materials have been gaining great interest in the energy sector. Their tunability and robustness make them highly attractive, particularly for proton-exchange membrane fuel cell (PEMFC) electrodes. Conventional PEMFC electrodes, prepared by either spraying, painting, or slot-die coating, have not yet met the needs of large-scale PEMFC use. Electrospinning of fibrous materials has already shown great promise as an alternative methodology for electrode fabrication. Electrospinning has been used in fuel cell electrodes through two primary means: (1) segmented carbon or inorganic fibers to serve as precious metal catalyst support, and (2) high aspect ratio polymer/particle fibers to serve directly as the electrode. The use of electrospun fibrous electrodes has led to improved PEMFC durability and increased power output at low catalyst loadings, both of which are of paramount importance to large-scale commercialization of PEMFC electric vehicles.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A comparison of surrogate constitutive models for viscoplastic creep simulation of HT-9 steel

Mechanistic microstructure-informed constitutive models for the mechanical response of polycrystals are a cornerstone of computational materials science. However, as these models become increasingly more complex – often involving coupled differential equations describing the effect of specific deformation modes – their associated computational costs can become prohibitive, particularly in optimization or uncertainty quantification tasks that require numerous model evaluations. To address this challenge, surrogate constitutive models that balance accuracy and computational efficiency are highly desirable. Data-driven surrogate models, that learn the constitutive relation directly from data, have emerged as a promising solution. In this work, we develop two local surrogate models for the viscoplastic response of a steel: a piecewise response surface method and a mixture of experts model. These surrogates are designed to adapt to complex material behavior, which may vary with material parameters or operating conditions. The surrogate constitutive models are applied to creep simulations of HT-9 steel, an alloy of considerable interest to the nuclear energy sector due to its high tolerance to radiation damage, using training data generated from viscoplastic self-consistent (VPSC) simulations. In conclusion, we define a set of test metrics to numerically assess the accuracy of our surrogate models for predicting viscoplastic material behavior, and show that the mixture of experts model outperforms the piecewise response surface method in terms of accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Probabilistic feasibility assessment of sequestration reliance for climate targets

Countries worldwide are transforming their energy systems to achieve Carbon-Neutrality. Investing in renewable resources-based technologies and implementing Carbon Capture and Storage (CCS) are common strategies to achieve higher sequestration levels. Negative emissions through Bioenergy with CCS are expected to play an essential role in the transition to full decarbonization. On top of that, biomass is a limited resource that depends on environmental factors, which create uncertainties related to the amount that can be sustainably provided to the energy system. Here this study emphasizes the relevance of variability in carbon sequestration for achieving climate targets by 2050. Here this paper proposes a probabilistic approach that integrates the Global Change Analysis Model for Latin America (GCAM-LA) with a chance constraint approach. GCAM-LA is used to assess the impact on the energy sector of different limits of sustainable biomass and carbon budget scenarios. The risk associated with exceeding the sequestration capacity of a given region is modeled via Chance Constraint. Results show that electrification is an appropriate long-term decarbonization strategy. It smoothes the effects of uncertainty in sequestration capacity and responds to end-user demands. For this case study, higher levels of electrification are obtained at likelihood levels >66% for end-use sectors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The development of natural gas and hydrogen pipeline capital cost estimating equations

Natural gas pipeline cost data collected by the Oil and Gas Journal (O&GJ) for interstate pipelines constructed from 1980 through 2017 were used to develop capital cost estimating equations that are a function of pipeline diameter, length, and U.S. region. Equations were developed for material, labor, miscellaneous, and right-of-way costs, the four cost components in the O&GJ data, for six different regions of the United States (U.S.). Each equation is a function of pipeline diameter and length. Adjustment mechanisms were then developed for converting the natural gas pipeline equations into equations for estimating the costs of hydrogen pipelines. These adjustments were based in part on an analysis completed by the National Institute for Standards and Technology (NIST). Here the results of this work were used to update cost models in the Hydrogen Delivery Scenario Analysis Model (HDSAM), developed by Argonne National Laboratory for the U.S. Department of Energy’s Hydrogen Program. Our analysis shows a wide range of pipeline cost across different U.S. regions, especially with respect to labor and right-of-way costs. The developed cost formulas for hydrogen pipelines are both important and timely as hydrogen is being considered as a zero-carbon energy carrier with the potential to decarbonize all energy sectors, and the cost of hydrogen transportation is essential for techno-economic analysis of its potential use in these sectors.

03 NATURAL GAS↗
DOE OSTI2023

Cyber threat assessment of machine learning driven autonomous control systems of nuclear power plants

We report advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)-based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber–physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Fast-airflow tumble clothes dryer with small thermoelectric heat pump: Experimental evaluation

Residential clothes drying accounts for about 5% of the total residential-sector energy consumption in the United States. Most dryers use electric resistance heaters to dry clothes and have low efficiencies. Higher-efficiency dryers that use vapor compression heat pumps are expensive and complex and have not gained a large market share in the United States. A novel tumble clothes dryer using a small thermoelectric heat pump with faster airflow than typical dryers is presented in this work. The benchtop performance of the thermoelectric heat pump and high-speed blower are presented, and the development of the prototype dryer is described. The dryer was tested for efficiency and dry time for a range of airflow rates and applied currents to the thermoelectric heat pump. The combined efficiency factor was 5.09–6.29 lb BDW /kWh (specific moisture extraction rate of 1.23–1.53 kg w /kWh) with 100–138 min dry times for these tests. The measured efficiency was 36 %–68 % greater than the minimum efficiency standard in the United States, and compared with vapor compression heat pump–based clothes dryers, the prototype dryer had less expensive, less complex components and did not use refrigerants. Finally, the performance of this small thermoelectric heat pump clothes dryer is also compared with previous iterations of the thermoelectric tumble clothes dryer described in the literature.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Quantification of Hydrogen Isotopes Utilizing Raman Spectroscopy Paired with Chemometric Analysis for Application across Multiple Systems

On-line and real-time analysis of a chemical process is a major analytical challenge that can drastically change the way the chemical industry or chemical research operates. With in situ analyses, new and powerful understanding of chemistry can be gained; however, building robust tools for long-term monitoring faces many challenges that include compensating for instrument drift, instrument replacement, and sensor or probe replacement. Accounting for these changes by recollecting calibration data and rebuilding quantification models can be costly and time consuming. Here, in this study, methods to overcome these challenges are demonstrated with an application of Raman spectroscopy to monitoring hydrogen isotopes with varied speciation within dynamic gas streams. Specifically, chemical data science tools such as chemometric modeling are leveraged along with several examples of calibration transfer approaches. Furthermore, the optimization of instrument and sensors cell parameters for targeted gas phase analyses is discussed. While the particular focus on hydrogen is highly beneficial within the nuclear energy sector, mechanisms built and demonstrated here are widely applicable to optical spectroscopy monitoring in numerous other chemical systems that can be leveraged in other hazardous processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Computational Methods in Heterogeneous Catalysis

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. Here, we finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Scalable Hot-Water-Repellent Superhydrophobicity via Thermal Insulation

Superhydrophobic surfaces, which rely on a combination of surface texture and chemistry, often lose their repellent behavior when contacted by hot water (≳40 °C) because the impinging hot water replaces the requisite air layer within the surface texture via evaporation and recondensation. In contrast to previous approaches targeting this condensation-induced failure mode that rely on intricately tailored surface structures or complex chemical treatments, we present a scalable approach based on thermal design: the multilayered insulated superhydrophobic (MISH) coating mitigates condensation-induced failure by preventing heat transfer. Superhydrophobicity is retained at impinging water temperatures up to 90 °C, with durability demonstrated via long-term (>1 million impacts) droplet impingement experiments. We explain the mechanism for this approach with a detailed thermal model; the model reveals that the underlying physical behavior is self-similar across coating parameters and impinging fluid temperatures. Finally, the MISH coating accommodates curved geometries and large surfaces, and it is over 4 orders of magnitude less expensive than cleanroomnanofabricated alternatives, indicating promise for practical use in the energy sector, chemical processing, and the food and medical industries.

coating materials↗
DOE OSTI2024

Butene-Rich Alkene Formation from 2,3-Butanediol through Dioxolane Intermediates

The cost-effective production of sustainable aviation fuels (SAF) remains a major challenge within the energy sector. One approach to address this is the fermentation of biomass feedstocks into oxygenates followed by catalytic conversion to alkenes or other oligomerization precursors. 2,3-Butanediol (BDO) is a promising fermentation product due to its four-carbon nature, its decreased microorganism toxicity and associated higher maximum fermentation titers relative to other alcohols and oxygenates, and its capacity to be readily converted into butene isomers and longer chain alkenes. BDO conversion is currently constrained by separation challenges for BDO isolation due to its high boiling point and hydrophilicity. Here, this work expands upon previous BDO reactive separation via dioxolane formation over a solid acid catalyst by investigating the conversion of dioxolanes into alkene mixtures. Dioxolanes were formed from a range of aldehydes and subsequently converted over a Cu/ZSM-5 catalyst (448–523 K) via an ether cleavage, hydrogenation, and dehydration reaction network to form alkene-rich product mixtures (96% C 3+ alkene yield, 523 K). This selectivity is greater than that of direct BDO conversion to alkenes over an identical catalyst (89%, 523 K). C 3+ alkene selectivity is maximized between 498 and 523 K at complete dioxolane conversion without significant alkene hydrogenation to alkanes. The alkene product distributions can be tailored via both aldehyde selection during dioxolane formation and the dioxolane conversion reaction temperature. Alkene mixtures from dioxolane conversion predominantly reflect the carbon chain length and stereochemistry of BDO and the initial aldehyde at or below 498 K, yet higher reaction temperatures yield alkene mixtures of similar carbon chain distributions, regardless of initial aldehyde selection. Deactivation of the Cu/ZSM-5 catalyst is observed for multiple steps of the overall reaction network but can be minimized by facilitating the complete dioxolane-to-alkene reaction network at temperatures of at least 498 K.

2,3-butanediol↗
DOE OSTI2022

Emergence of the spin polarized domains in the kagome lattice Heisenberg antiferromagnet Zn-barlowite (Zn 0.95 Cu 0.05 )Cu 3 (OD) 6 FBr

Kagome lattice Heisenberg antiferromagnets are known to be highly sensitive to perturbations caused by the structural disorder. NMR is a local probe ideally suited for investigating such disorder-induced effects, but in practice, large distributions in the conventional one-dimensional NMR data make it difficult to distinguish the intrinsic behavior expected for pristine kagome quantum spin liquids from disorder-induced effects. Here we report the development of a two-dimensional NMR data acquisition scheme applied to Zn-barlowite (Zn 0.95 Cu 0.05 )Cu 3 (OD) 6 FBr kagome lattice, and successfully correlate the distribution of the low energy spin excitations with that of the local spin susceptibility. We present evidence for the gradual growth of domains with a local spin polarization induced by 5% Cu 2+ defect spins occupying the interlayer non-magnetic Zn 2+ sites. These spin-polarized domains account for ~60% of the sample volume at 2 K, where gapless excitations induced by interlayer defects dominate the low-energy sector of spin excitations within the kagome planes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids

The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable resources, the reliable operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML)-based approaches towards reliable operation of future electric grids. The dataset is synthesized from a joint transmission and distribution electric grid to capture the increasingly important interactions and uncertainties of the grid dynamics, containing power, voltage and current measurements over multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML benchmarks on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbances; (ii) robust hierarchical forecasting of load and renewable energy; and (iii) realistic synthetic generation of physical-law-constrained measurements. We envision that this dataset will provide use-inspired ML research in safety-critical systems, while simultaneously enabling ML researchers to contribute towards decarbonization of energy sectors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Second-harmonic generation tensors from high-throughput density-functional perturbation theory

Optical materials play a key role in enabling modern optoelectronic technologies in a wide variety of domains such as the medical or the energy sector. Among them, nonlinear optical crystals are of primary importance to achieve a broader range of electromagnetic waves in the devices. However, numerous and contradicting requirements significantly limit the discovery of new potential candidates, which, in turn, hinders the technological development. In the present work, the static nonlinear susceptibility and dielectric tensor are computed via density-functional perturbation theory for a set of 579 inorganic semiconductors. The computational methodology is discussed and the provided database is described with respect to both its data distribution and its format. Several comparisons with both experimental and ab initio results from literature allow to confirm the reliability of our data. The aim of this work is to provide a relevant dataset to foster the identification of promising nonlinear optical crystals in order to motivate their subsequent experimental investigation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

A Practical Comparison of Data-Driven Prognostics Methods for Energy Systems

This study explores data-driven prognostics for nuclear power plant (NPP) condensers, focusing on tube fouling. We utilized the Asherah nuclear power plant simulator (ANS) to compare four methods: Random Forest (RF), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory Neural Network (LSTM). By simulating various fouling scenarios in the ANS, we generated data with different degradation rates under transient operations. The models were trained and tested on these data, with performance evaluated visually and numerically including uncertainty assessment. The LSTM model excelled, exhibiting minimal prediction noise and the most accurate remaining useful life estimates across all degradation levels. Its ability to capture long-term dependencies and produce cleaner outputs makes it a strong candidate, although accurate training data across the entire component lifespan are crucial. The RF model emerged as a robust alternative, providing reliable predictions with high confidence. The FCNN and SVR models, while less effective overall, showed potential under specific conditions. FCNN offers a less complex alternative to LSTM and might benefit from larger datasets. SVR excels in precision when the quality of the training data is high. Furthermore, this study highlights the operational benefits of advanced prognostics in the energy sector and emphasizes the need for further research in NPP condenser health management through real-life experiments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗