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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 OSTI2026

Offshore Wind Farm Turbine and Energy Storage Optimization

Abstract This paper evaluates the technical and economic feasibility of repurposing decommissioned offshore oil and gas platforms as electrical substations for offshore wind projects in the U.S. Gulf of America, a region characterized by relatively low and highly variable wind speeds, extensive legacy offshore infrastructure, and exposure to merchant electricity markets. A unified techno-economic framework is developed using the Repurposing Offshore Infrastructure for Continued Energy (ROICE) Economic Model (REM) to integrate Gulfspecific wind resource assessment, commercial wind turbine performance, offshore infrastructure cost modeling, and wholesale electricity market exposure. Gulf wind speed data are vertically extrapolated to turbine hub height and combined with manufacturer power curves to compute annual energy production and capacity factors across a broad portfolio of commercial turbines, enabling identification of turbine designs best suited for low-wind offshore environments. Hourly electricity price data from the Midcontinent Independent System Operator (MISO) day-ahead market are incorporated to characterize revenue potential, price volatility, and the temporal alignment between wind generation and market conditions. In addition, a conceptual framework for offshore battery energy storage system (BESS) integration is developed to support future investigation of market-responsive energy shifting at repurposed platforms. Results from the turbine evaluation demonstrate that machines with lower cut-in wind speeds and earlier ‘rated-power’ characteristics significantly outperform larger, industry-standard offshore turbines for the same net power under Gulf wind conditions, underscoring the need for region-specific technology selection. Market analysis further reveals substantial price variability and limited intrinsic alignment between wind production and high-price periods, motivating consideration of operational flexibility mechanisms. While storage optimization is not implemented in this study, the REM framework establishes a transparent and replicable foundation for co-evaluating turbine selection, infrastructure constraints, and market exposure, providing a practical pathway for assessing the potential role of repurposed offshore platforms in enabling economically viable offshore wind development in the Gulf of America.

02 PETROLEUM↗
DOE OSTI2022

Characterization and Accelerated Life Testing of a New Solid Oxide Electrolysis Cell

The project aims to develop mechanistic degradation models that realistically predict long-term Solid Oxide Electrolysis Cell (SOEC) durability, using input data from accelerated electrochemical life testing combined with quantitative microstructural and microchemical evaluation. A promising SOEC type will be further developed. The understanding achieved by combining experimental results and theory will be used to guide improvements in long-term SOEC durability, and validate that this technology can reach DOE H 2 production cost and durability targets. Planned outcomes include: (1) new experimental accelerated testing data on SOEC degradation at the oxygen electrode, fuel electrode, and electrolyte; (2) predictive theory of degradation developed based on the accelerated test data; (3) new electrode materials and processing methods that minimize degradation rate; (4) determination of the impact of SOEC electrolyte thickness and electrode composition/microstructure on performance and degradation; (5) determination of the operating conditions where degradation is minimized; and (6) validation of degradation models for extrapolating accelerated test data to predict long-term durability.

08 HYDROGEN↗
DOE OSTI2024

Statin Drug‐Drug Interactions: Pharmacokinetic Basis of FDA Labeling Recommendations and Comparison Across Common Tertiary Clinical Resources

Abstract Statins are widely prescribed and highly susceptible to pharmacokinetic (PK)‐based drug–drug interactions (DDIs). To date, there has not been a comprehensive analysis of the basis upon which statin DDI recommendations in US Food and Drug Administration (FDA) prescribing information (PI) are derived. We have conducted such an analysis. We also assessed the degree of concordance of statin DDI recommendations in FDA PI and those provided in common tertiary clinical resources. We catalogued statin DDI information, including PK data and management recommendations, for statin precipitant drugs approved from 2010 to 2021, available from FDA PI and tertiary clinical resource databases. Recommendations were categorized and mapped with associated PK data to assess consistency in the PK basis for labeling recommendations. From the 80 precipitant drugs evaluated, 180 statin DDIs were identified in FDA PI. Dedicated clinical DDI studies were conducted for 54% (n = 97) of these DDIs and 34% (n = 61) of DDI recommendations were extrapolated from clinical data with other statins. Overall, we found that PK‐based statin recommendations were consistent across PI. These findings highlight regulatory precedence for translating information across statins without conducting dedicated clinical DDI studies, which may support future efforts toward streamlining the approach to investigation and labeling of statin DDIs. In addition, with the exception of some notable discrepancies, general concordance was observed between FDA and tertiary resources regarding “Dose Adjustment” and “Avoid Coadministration” recommendations. However, further analyses are warranted across other DDI pairs to determine whether discordance can routinely lead to different clinical recommendations depending on the drug information resource.

Mease, James↗
DOE OSTI2020

TMAP modeling of D release from baked multi-layer Be–D co-deposits

Single-layer Beryllium–Deuterium (Be–D) co-deposits were analyzed using Thermal Desorption mass Spectrometry (TDS) as outlined in the companion paper. This work details the TMAP modeling and the analysis of selected samples that received double-layer (DL) coatings to study co-deposit thickness and prior bake effects. Here, TDS data were simulated with detrap energies 0.8, 0.9, and 1.0 eV. In DLs formed at 393 K, modeling revealed that under-layers baked at 623 K for either 2 or 20 h had little pumping effect on over-layers, suggesting empty trap removal during bake. Extrapolated to ITER, data and analysis suggest less impact on the ongoing ITER bake efficiency than previously predicted.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Thermophysical Properties of NaCl–UCl 3 –PuCl 3 Molten Salts: A Combined Computational and Experimental Study

Actinide-bearing molten salts for use as fuels are an essential part of next generation molten salt reactors. Yet, numerous multicomponent salt mixtures are underdeveloped or have not been investigated. Here, this study, based on a combination of experimental and modeling techniques, is dedicated to determining and understanding a variety of properties of the ternary system of NaCl–UCl 3 –PuCl 3 , which represents a scenario for burnup of NaCl–UCl 3 fuel, at two compositions (∼10 and 5 mol % PuCl 3 in eutectic NaCl–UCl 3 pseudobinary) and a range of temperatures. Evaluation of the heat flow and mass loss data showed the 0.61NaCl–0.30UCl 3 –0.09PuCl 3 salt had a melting temperature of 551 ± 5 °C. Two additional thermal effects were observed occurring at approximately 410 and 494 °C. The transition occurring at 410 °C may be due to the presence of oxide in the salt. Extrapolation of thermodynamic data indicates the transition occurring at 494 °C is due to the formation of a liquid phase. Experimental testing determined the density of this system is a linear function of temperature and can be represented by the equation ρ = 4.014–0.0010T(°C), R 2 = 0.992. Additionally, by using atomistic modeling, we found that increasing the PuCl 3 content from 5 to 10 mol % led to the formation of larger Pu 3+ clusters and slower transport of ions.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Kinetics and transport of hydrogen in graphite at high temperature and the effects of oxidation, irradiation and isotopics

The kinetics of uptake and desorption impact the performance of graphite as a vector for tritium in high-temperature fission reactors and in the blanket of fusion reactors. Graphite components in these reactors are exposed to temperatures > 500 °C and H 2 partial pressures of few Pa and desorption temperatures are limited to < 1600 °C; limited data is available at these conditions. Here we review the mechanisms for uptake in, transport and desorption of hydrogen from graphite at high temperature, compiling data on uptake rates, diffusion coefficients and activation energies and providing a discussion of the impact of irradiation, pre-oxidation and isotope. At FHR conditions, trapping impacts uptake rates, leading to a reduction in apparent diffusivity by 35 to 80% compared to higher partial-pressure uptake. Timelines for desorption are not clearly defined; extrapolating from available data, at 1150 °C desorbing 80% of tritium uptaken at FHR conditions may take from 100 to 10,000 h.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Oxygen diffusion in garnet: Experimental calibration and implications for timescales of metamorphic processes and retention of primary O isotopic signatures

Knowledge of oxygen diffusion in garnet is crucial for a correct interpretation of oxygen isotope signatures in natural samples. A series of experiments was undertaken to determine the diffusivity of oxygen in garnet, which remains poorly constrained. The first suite included high-pressure (HP), nominally dry experiments performed in piston-cylinder apparatus at: (1) T = 1050–1600 °C and P = 1.5 GPa and (2) T = 1500 °C and P = 2.5 GPa using yttrium aluminum garnet (YAG; Y3Al5O12) cubes. Second, HP H2O-saturated experiments were conducted at T = 900 °C and P = 1.0–1.5 GPa, wherein YAG crystals were packed into a YAG + Corundum powder, along with 18O-enriched H2O. Third, 1 atm experiments with YAG cubes were performed in a gas-mixing furnace at T = 1500–1600 °C under Ar flux. Finally, an experiment at T = 900 °C and P = 1.0 GPa was done using a pyrope cube embedded into pyrope powder and 18O-enriched H2O. Experiments using grossular were not successful. Profiles of 18O/(18O+16O) in the experimental charges were analyzed with three different secondary ion mass spectrometers (SIMS): sensitive high-resolution ion microprobe (SHRIMP II and SI), CAMECA IMS-1280, and NanoSIMS. Considering only the measured length of 18O diffusion profiles, similar results were obtained for YAG and pyrope annealed at 900 °C, suggesting limited effects of chemical composition on oxygen diffusivity. However, in both garnet types, several profiles deviate from the error function geometry, suggesting that the behavior of O in garnet cannot be fully described as simple concentration-independent diffusion, certainly in YAG and likely in natural pyrope as well. The experimental results are better described by invoking O diffusion via two distinct pathways with an inter-site reaction allowing O to move between these pathways. Modeling this process yields two diffusion coefficients (D values) for O, one of which is approximately two orders of magnitude higher than the other. Taken together, Arrhenius relationships are:logDm2s-1=-7.2(±1.3)+(-321(±32)kJmol-12.303RT) for the slow pathway, andlogDm2s-1=-5.4(±0.7)+(-321(±20)kJmol-12.303RT) for the fast pathway. We interpret the two pathways as representing diffusion following vacancy and inter-stitial mechanisms, respectively. Regardless, our new data suggest that the slow mechanism is prevalent in garnet with natural compositions, and thus is likely to control the retentivity of oxygen isotopic signatures in natural samples. The diffusivity of oxygen is similar to Fe-Mn diffusivity in garnet at 1000–1100 °C and Ca diffusivity at 850 °C. However, the activation energy for O diffusion is larger, leading to lower diffusivities at P-T conditions characterizing crustal metamorphism. Therefore, original O isotopic signatures can be retained in garnets showing major element zoning partially re-equilibrated by diffusion, with the uncertainty caveat of extrapolating the experimental data to lower temperature conditions.

Geochemistry & Geophysics↗
DOE OSTI2021

Early Battery Performance Prediction for Mixed Use Charging Profiles Using Hierarchal Machine Learning

A key step limiting how fast batteries can be deployed is the time necessary to provide evaluation and validation of performance. Using data analysis approaches, such as machine learning, the validation process can be accelerated. However, questions on the validity of projecting models trained on limited data or simple cycling profiles, such as constant current cycling, to real-world scenarios with complex loads remains. Here, we present the ability to predict performance with less than 1.2% mean absolute percent error when trained on cells aged using complex electric vehicle discharge profiles, and either AC Level 2 charge or DC Fast charge profiles, using only the first 45 cycles, namely 5% of the total testing time. While error is low across the projections, this study also highlights that battery lifetime analysis using only cycling data may not extrapolate safely to certain real-world conditions due to the impact of calendar degradation.

25 ENERGY STORAGE↗
DOE OSTI2021

Atomic understanding of structural deformations upon ablation of graphene

We investigate the atomic rearrangement in graphene under femtosecond pulse illumination with reactive molecular dynamics simulations and compare with ultra-fast laser ablation experiments. To model the impact of the laser pulse irradiation, heat is locally applied to a selected area of the graphene layer and the resulting structural deformation is simulated as a function of time, providing a detailed understanding of the bond breaking process under laser illumination and subsequent re-equilibration afterAQ3 the pulse is turned off. Analysis of the atomic dynamics indicates that the types of defects formed depend on the pulse energy and exposure duration. By varying the exposed area, we determine that the shape of the ablated area is not only a function of the pulse energy, but also of the beam spot size and pulse repetition. Furthermore, we apply a machine learning approach to extrapolate our simulated data to experimental length scales and reproduce the trends in ablated area as a function of temperature. Furthermore, our study provides a first step towards understanding the design parameters for graphene nano-patterning.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Effect of neutron beamline irradiation on hydrogen equilibrium vapor pressure over hydrided Zircaloy-4

Here, the effect of neutron irradiation on the hydrogen equilibrium vapor pressure over hydrided samples of nickel-plated Zircaloy-4 was evaluated at the Washington State University Research Reactor’s epithermal beamline facility. To enable this study, a measurement capability was designed and assembled to obtain low pressure (<0.1 Pa) measurements over the temperature range of interest (i.e., ~300° - 400°C). Measurement results were in good agreement with tends extrapolated from previous data obtained at higher temperatures as well as recent thermodynamic data. No irradiation enhancement of vapor pressure was observed under the incident flux of epithermal neutrons over this temperature range.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Scaling CFB risers: Beyond the data using microstructure similarity

Scaling circulating fluidized bed risers has been a point of contention for nearly a century. There have been numerous attempts to define various methodologies. These have all fallen short of providing a robust approach, primarily due to a lack of maintaining microstructure dynamics – a key factor in maintaining interphase heat and mass transfer. Recent work by the author put forward an approach based upon preserving dynamic similarity at different scales by ensuring that the microstructure remained similar by maintaining statistical and chaotic parameters across the scale. That work relied on a dimensionless regime map in which the x-axis was defined by the ratio of the solids flux to the saturation carrying capacity. This latter property was only good within the range of the data, and it became evident that extrapolation beyond the limits of the data could induce unrealistic conditions. Therefore, literature was reviewed to develop a better correlation for the saturation carrying capacity. In doing so, a critical riser diameter concept was developed and applied to the correlations for both Geldart Group A and B materials. The critical diameter for Geldart Group A and B materials is 0.2 m and 0.3 m, respectively. Here the paper then gives three examples on how to use the scaling approach to maintain dynamic similarity across the scales.

02 PETROLEUM↗
DOE OSTI2020

Updated External Exposure Dose Coefficients

In 2018, the Environmental Protection Agency (EPA) updated and expanded the Federal Guidance Report (FGR) No. 12 to No. 15; tabulating age-specific external exposure effective dose rate coefficients for 1,252 radionuclides in the air, water, and soil (EPA 2018). However, the EPA discovered errors in the tables of dose coefficients for soil which led to the report being retracted for correction. This led to the adoption of “a uniform method of data-smoothing and extrapolation of monoenergetic organ doses at low energies across all exposure pathways; the originally-published dose coefficients utilized different methods for each media type” (EPA 2019). This resulted in all the external exposure dose coefficients being updated and FGR 15 being republished in August 2019 with corrected tables.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Initial Alloy 709 constitutive models for use with the ASME design by inelastic analysis and EPP+SMT design methods

This report details a preliminary inelastic constitutive model describing the behavior of Alloy 709. This model will serve two purposes: (1) integration into Nonmandatory Appendix HBB-Z of the ASME Boiler & Pressure Vessel Code Section III, Division 5 and (2) extrapolating cyclic test data to difficult to measure conditions for formulating improved creep-fatigue design methods. For both applications, the model must accurately capture the material behavior across a wide range of temperatures and a variety of test conditions, both monotonic and cyclic. For this purpose we adopt a universal model form under consideration to standardize the description of high temperature constitutive models in the ASME Code. This report briefly restates that model form and how we calibrate the model against the test data, summarizes the test database, and validates the final, trained model by comparison to the experimental tests.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-Informed Neural Network Solution of Point Kinetics Equations for a Nuclear Reactor Digital Twin

A digital twin (DT) for nuclear reactor monitoring can be implemented using either a differential equations-based physics model or a data-driven machine learning model. The challenge of a physics-model-based DT consists of achieving sufficient model fidelity to represent a complex experimental system, whereas the challenge of a data-driven DT consists of extensive training requirements and a potential lack of predictive ability. We investigate the performance of a hybrid approach, which is based on physics-informed neural networks (PINNs) that encode fundamental physical laws into the loss function of the neural network. We develop a PINN model to solve the point kinetic equations (PKEs), which are time-dependent, stiff, nonlinear, ordinary differential equations that constitute a nuclear reactor reduced-order model under the approximation of ignoring spatial dependence of the neutron flux. The PINN model solution of PKEs is developed to monitor the start-up transient of Purdue University Reactor Number One (PUR-1) using experimental parameters for the reactivity feedback schedule and the neutron source. The results demonstrate strong agreement between the PINN solution and finite difference numerical solution of PKEs. We investigate PINNs performance in both data interpolation and extrapolation. For the test cases considered, the extrapolation errors are comparable to those of interpolation predictions. Extrapolation accuracy decreases with increasing time interval.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Maintaining Microstructure – The Path to Successful Technology Maturation in Fluidized Systems

Scaling circulating fluidized bed risers has been a point of contention for nearly a century. There have been numerous attempts to define various methodologies. These have all fallen short of providing a robust approach, primarily due to a lack of maintaining microstructure dynamics – a key factor in maintain interphase heat and mass transfer. Recent work by the author put forward an approach based up maintaining dynamic similarity at different scales by ensuring that the microstructure remained similar by maintaining statistical and chaotic parameters across the scale. That work relied on a dimensionless regime map in which the x-axis was defined by the ratio of the solids flux to the saturation carrying capacity. This latter property was only good within the range of the data and it became evident that extrapolation beyond the limits of the data could induce unrealistic conditions. Therefore, literature was reviewed to develop a better correlation for the saturation carrying capacity. In doing so, a critical diameter concept was developed and applied to the correlations for both Geldart Group A and B materials. The critical diameter for Geldart Group A and B materials is 0.2 m and 0.3 m, respectively. The paper then gives examples on how to use the scaling approach to maintain dynamic similarity across the scales.<br>

Breault, Ronald↗
DOE OSTI2025

Combining physics-based and data-driven models for quantitatively accurate plasma profile prediction that extrapolates well; with application to DIII-D, AUG, and ITER tokamaks

For design, scenario planning, and control, ITER and all other envisioned tokamaks rely on a variety of statistical and physics-based models to extrapolate to unseen regimes; most notably from low plasma current to high. A 'meta-learning' methodology for combining the accuracy of data-driven models with the generalizability of physics-based models is described and tested, yielding a 5–10 percent improvement in performance beyond either alone for the task of extrapolating time-dependent plasma profile prediction from low- to high- plasma current DIII-D tokamak discharges. Meanwhile, it is shown that both machine learning models extrapolated far-distribution and state-of-the-art 'physics-based' profile predictors fare worse than merely assuming plasma profiles do not change from their initial values. Finally, a variety of other mechanisms for helping data-driven models generalize—transfer learning, adding contextual information from physics simulators, and adding data from the ASDEX Upgrade tokamak—are attempted for similar extrapolation tasks but, in the methodology used in this paper, yield no significant improvement beyond simple data-driven models. Results are summarized in figures 15 and 16.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

FY25 status report on the addition of candidate materials in Class B Code Case

This report provides the time-dependent allowable stress calculation strategy leveraging the limited creep rupture tests data generated to support the allowable stress for 100,000 hours in American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC), Section II, Part D. A variable confidence index procedure to extrapolate material properties to support 500,000 hours design life is discussed. Time-dependent allowable stresses for Class B component design and analysis are presented for Grade 1 and Grade 2 of Alloy 625. The presented data extrapolation and allowable stress calculation method will support new material addition using limited creep rupture data in the new ASME Boiler and BPVC, Section III, Division 5, Class B rules.

Part D↗
DOE OSTI2020

Decreasing wind speed extrapolation error via domain-specific feature extraction and selection

Abstract. Model uncertainty is a significant challenge in the wind energy industry and can lead to mischaracterization of millions of dollars' worth of wind resources. Machine learning methods, notably deep artificial neural networks (ANNs), are capable of modeling turbulent and chaotic systems and offer a promising tool to produce high-accuracy wind speed forecasts and extrapolations. This paper uses data collected by profiling Doppler lidars over three field campaigns to investigate the efficacy of using ANNs for wind speed vertical extrapolation in a variety of terrains, and it quantifies the role of domain knowledge in ANN extrapolation accuracy. A series of 11 meteorological parameters (features) are used as ANN inputs, and the resulting output accuracy is compared with that of both standard log-law and power-law extrapolations. It is found that extracted nondimensional inputs, namely turbulence intensity, current wind speed, and previous wind speed, are the features that most reliably improve the ANN's accuracy, providing up to a 65 % and 52 % increase in extrapolation accuracy over log-law and power-law predictions, respectively. The volume of input data is also deemed important for achieving robust results. One test case is analyzed in depth using dimensional and nondimensional features, showing that the feature nondimensionalization drastically improves network accuracy and robustness for sparsely sampled atmospheric cases.

17 WIND ENERGY↗