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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Testing in Support of the Development of EPP Plus SMT Design Method at ORNL (FY2021)

Experiments in support of the development of the integrated Elastic–Perfectly Plastic (EPP) plus Simplified Model Test (SMT) design methodology, referred to as the EPP+SMT method, continued in FY21. This report focuses on the methodology for developing the EPP+SMT creep-fatigue (CF) design curves at low strain ranges. The creep damage-based method and dissipated work-based method were used to evaluate the available CF data at the low strain range region. A set of failure criteria were determined, and a simple extrapolation method was developed to predict the CF life cycles at low strain ranges that are not accessible by experiments due to the extraordinarily long failure times at the low strain region (thousands to hundreds of thousands of years) and the inability of the test machine to control these small strain ranges due to the signal to noise issues. An experimental method with the concept of block-strain range CF testing was proposed to generate the information needed to extrapolate the CF design curves to low strain ranges. Based on this new testing approach, a preliminary EPP+SMT CF design curve was developed for Alloy 617 at 950°C with tension hold time of 100 s. The analysis in this report shows the potential of generating a set of EPP+SMT CF design curves with different hold times within a reasonable amount of time and testing effort. Based on such a progress, a hold time extrapolation procedure for low strain ranges will be developed in FY22 and critical testing will be carried out to complete the development of the EPP+SMT CF design curves for Alloy 617.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An out-of-distribution-aware autoencoder model for reduced chemical kinetics

While detailed chemical kinetic models have been successful in representing rates of chemical reactions in continuum scale computational fluid dynamics (CFD) simulations, applying the models in simulations for engineering device conditions is computationally prohibitive. To reduce the cost, data-driven methods, e.g., autoencoders, have been used to construct reduced chemical kinetic models for CFD simulations. Despite their success, data-driven methods rely heavily on training data sets and can be unreliable when used in out-of-distribution (OOD) regions (i.e., when extrapolating outside of the training set). In this paper, we present an enhanced autoencoder model for combustion chemical kinetics with uncertainty quantification to enable the detection of model usage in OOD regions, and thereby creating an OOD-aware autoencoder model that contributes to more robust CFD simulations of reacting flows.Here, we first demonstrate the effectiveness of the method in OOD detection in two well-known datasets, MNIST and Fashion-MNIST, in comparison with the deep ensemble method, and then present the OOD-aware autoencoder for reduced chemistry model in syngas combustion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Confronting Domain Shift in Trained Neural Networks

Neural networks (NNs) are known as universal function approximators and can interpolate nonlinear functions between observed data points. However, when the target domain for deployment shifts from the training domain and NNs must extrapolate, the results are notoriously poor. Prior work Martinez et al. (2019) has shown that NN uncertainty estimates can be used to correct binary predictions in shifted domains without retraining the model. We hypothesize that this approach can be extended to correct real-valued time series predictions. As an exemplar, we consider two mechanical systems with nonlinear dynamics. The first system consists of a spring-mass system where the stiffness changes abruptly, and the second is a real experimental system with a frictional joint that is an open challenge for structural dynamicists to model efficiently. Our experiments will test whether 1) NN uncertainty estimates can identify when the input domain has shifted from the training domain and 2) whether the information used to calculate uncertainty estimates can be used to correct the NN’s time series predictions. While the method as proposed did not significantly improve predictions, our results did show potential for modifications that could improve models’ predictions and play a role in structural health monitoring systems that directly impact public safety.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗
DOE OSTI2020

Improving the probability tables of the cross section of near closed-shell nuclei [Slides]

The level density distribution of near closed-shell nuclei is much lower than the typical nucleus, therefore, the cross sections show significant fluctuations, and these fluctuations are not predictable. The current methodology used to describe such behavior and construct the probability table of the cross section is based on the extrapolation of the average resonance widths and average resonance spacings from the resonance region and use these parameters to construct the probability table. Although this is a standard and widely used technique, it does not take into account the existing experimental data, such for total and the elastic cross section. Our goal is to extend the current theory and provide a more general approach to compute the probability distribution function of the cross section combining the existing probability tables and the available experimental data. Results will be presented for 90 Zr.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Detailed study of quark-hadron duality in spin structure functions of the proton and neutron

Background: The response of hadrons, the bound states of the strong force (QCD), to external probes can be described in two different, complementary frameworks: as direct interactions with their fundamental constituents, quarks and gluons, or alternatively as elastic or inelastic coherent scattering that leaves the hadrons in their ground state or in one of their excited (resonance) states. The former picture emerges most clearly in hard processes with high momentum transfer, where the hadron response can be described by the perturbative expansion of QCD, while at lower energy and momentum transfers, the resonant excitations of the hadrons dominate the cross section. The overlap region between these two pictures, where both yield similar predictions, is referred to as quark-hadron duality and has been extensively studied in reactions involving unpolarized hadrons. Some limited information on this phenomenon also exists for polarized protons, deuterons, and 3 He nuclei, but not yet for neutrons. Purpose: In this paper, we present comprehensive and detailed results on the correspondence between the extrapolated deep inelastic structure function g 1 of both the proton and the neutron with the same quantity measured in the nucleon resonance region. Thanks to the fine binning and high precision of our data, and using a well-controlled perturbative QCD (pQCD) fit for the partonic prediction, we can make quantitative statements about the kinematic range of applicability of both local duality and global duality. Method: We use the most updated QCD global analysis results at high x from the Jefferson Lab Angular Momentum Collaboration to extrapolate the spin structure function g 1 into the nucleon resonance region and then integrate over various intervals in the scaling variable x. We compare the results with the large data set collected in the quark-hadron transition region by the CLAS Collaboration, including, for the first time, deconvoluted neutron data, integrated over the same intervals. We present this comparison as a function of the momentum transfer Q 2 . Results: We find that, depending on the integration interval and the minimum momentum transfer chosen, a clear transition to quark-hadron duality can be observed in both nucleon species. Furthermore, we show, for the first time, the approach to scaling behavior for g 1 measured in the resonance region at sufficiently high momentum transfer. Conclusions: Here, our results can be used to quantify the deviations from the applicability of pQCD for data taken at moderate energies and can help with extraction of quark distribution functions from such data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Measurement of coherent exclusive J/ψ → μ+μ− production in ultraperipheral Pb+Pb collisions at sNN=5.36 TeV with the ATLAS detector

The ATLAS experiment has performed a measurement of coherent exclusive J/ψ → μ+μ− production in ultraperipheral Pb+Pb collisions at sNN=5.36$$ \sqrt{s_{\textrm{NN}}}=5.36 $$ TeV. The data was recorded at the Large Hadron Collider (LHC) during 2023, and corresponds to an integrated luminosity of 79 μb−1. Exclusive J/ψ candidates were selected with a dedicated track-sensitive trigger based on the ATLAS transition radiation tracker. The analysis involves reconstruction of the dimuon invariant mass based on muon tracks from the inner detector, as the muon transverse momentum range of interest precludes the use of the standard muon reconstruction and identification algorithms. Differential cross sections are measured as a function of J/ψ rapidity and are compared with theoretical predictions. After extrapolation to sNN=5.02$$ \sqrt{s_{\textrm{NN}}}=5.02 $$ TeV, they are also compared with previous measurements performed by other experiments using data from LHC Run 2. While the results agree reasonably well with theoretical predictions, they are in tension with previous Run-2 results for the central rapidity region.

Aad, G↗
DOE OSTI2025

Stochastic room temperature creep of 316 L stainless steel

The creep behavior of 316 L stainless steel at room temperature was evaluated as a function of time and applied stress using a new high-throughput approach. Several common creep models were evaluated against the observations, leading to deeper analysis of a stress-dependent modified logarithmic creep model. Within this model, multiple sources of uncertainty were compared. Aleatoric stochastic variation between samples under nominally identical conditions was identified as the primary contributor to uncertainty in creep response. Under any particular set of conditions, the sample-to-sample variability in creep strain was as high as a factor of two, highlighting the engineering importance of characterizing large statistical datasets. The model's extrapolation capabilities were assessed by comparing predictions derived from calibration on partial, shorter-duration subsets of the data. In conclusion, these findings underscore the importance of accounting for stochastic effects in predictive modeling of aging phenomena.

High-throughput↗
DOE OSTI2026

Trivalent titanium in high-titanium lunar ilmenite

Lunar mare basalts are often rich in titanium, hosted predominantly within the mineral ilmenite (Fe 2+ Ti 4+ O 3 ). Here, we examine ilmenite in a ~3.8 billion-year-old mare basalt (Apollo rock 75035) using high-resolution electron microscopy and electron energy loss spectroscopy. A key finding is that 75035 ilmenite is itself enriched in Ti, beyond the end member of the conventional solid solution series. Using energy loss near-edge spectroscopy, we determine that the excess Ti is trivalent, with Ti 3+ accounting for 13% of the total Ti content. This discovery confirms the presence of trivalent Ti in lunar ilmenite, long hypothesized based on the Moon’s reducing environment. Accounting for the change in implied stoichiometry, a review of literature data suggests that Ti 3+ may be present in ilmenite across a wide range of lunar samples. We extrapolate known relationships from the literature to connect Ti 3+ to redox conditions, estimating the oxygen fugacity during crystallization of 75035 to be below the iron-wüstite buffer, ΔIW≤ − 1.6. Further quantifying the relationship between Ti valence state and oxygen fugacity would allow Ti 3+ -bearing ilmenite to serve as an oxybarometer able to access the reducing conditions found on many planetary bodies.

mineralogy↗
DOE OSTI2022

Time-domain self-broadened and air-broadened nitrogen S-branch Raman linewidths at 80–200 K recorded in an underexpanded jet

We report pure-rotational N 2 –N 2 , N 2 –air, and O 2 –air S-branch linewidths for temperatures of 80–200 K by measuring the time-dependent decay of rotational Raman coherences in an isentropic free-jet expansion from a sonic nozzle. We recorded pure-rotational hybrid femtosecond/picosecond coherent anti-Stokes Raman scattering (fs/ps CARS) spectra along the axial centerline of the underexpanded jet, within the barrel shock region upstream of the Mach disk. The dephasing of the pure-rotational Raman coherence was monitored using probe-time-delay scans at different axial positions in the jet, corresponding to varying local temperatures and pressures. The local temperature was obtained by fitting CARS spectra acquired at zero probe time delay, where the impact of collisions was minimal. The measured decay of each available Raman transition was fit to a dephasing constant and corrected for the local pressure, which was obtained from the CARS-measured static temperature and thermodynamic relationships for isentropic expansion from the known stagnation state. Nitrogen self-broadened transitions decayed more rapidly than those broadened in air for all temperatures, corresponding to higher Raman linewidths. In general, the measured S-branch linewidths deviated significantly in absolute and relative magnitudes from those predicted by extrapolating the modified exponential gap model to low temperatures. The temperature dependence of the Raman linewidth for each measured rotational state of nitrogen (J ≤ 10) and oxygen (N ≤ 11) was fit to a temperature-dependent power law over the measurable temperature domain (80–200 K) and extrapolated to both higher rotational states and room temperature. Furthermore, the measured and modeled low-temperature linewidth data provided here will aid low temperature gas-phase pressure measurements with fs/ps CARS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Tropical Cyclone Super Resolution using conditional diffusion denoising probabilistic model from mesoscale simulation to LES

Accurate modeling of tropical cyclone wind fields is essential for the design, risk assessment, and operational planning of offshore energy infrastructure. While mesoscale simulations are widely used thanks to their computational efficiency, they lack the necessary resolution to capture key features such as wind shear and veer profiles as well as the distribution turbulent kinetic energy (TKE). High-fidelity large-eddy simulation (LES) models on the other hand, can resolve turbulent structures and provide a more accurate representation of the complex wind field, albeit at a higher computational cost. To address this modeling gap, we introduce a two-part generative framework to enhance the resolution and physics-capturing ability of mesoscale simulations. First, a reduced-order model based on Karhunen–Loève (KL) decomposition is used to extract dominant spatial modes from one-dimensional mean wind profiles. A multilayer perceptron (MLP) is trained to map mesoscale mode weights to their LES counterparts, enabling accurate reconstruction of vertical velocity profiles. Second, a conditional Diffusion Denoising Probabilistic Model (DDPM) is developed to super-resolve coarse and low-fidelity mesoscale velocity fields, recovering fine-scale turbulence structures and stress distributions. The framework is evaluated across different tropical cyclone intensity categories defined by the Saffir–Simpson scale and demonstrates strong performance in both interpolation and extrapolation tasks. The generated fields accurately reproduce spatial coherence, stress distributions, and spectral energy characteristics observed in LES data. By bridging the fidelity gap between mesoscale and LES outputs, this approach offers a scalable, data-driven solution for enhancing the representation of tropical cyclone wind fields, enabling more robust offshore energy infrastructure systems design in tropical-cyclone-prone areas.

17 WIND ENERGY↗
DOE OSTI2020

Best Practices for the Validation of U.S. Offshore Wind Resource Models

This report presents a comprehensive set of best practices for working with both modeled and measured US offshore wind resource data sets. We specifically target two key questions in this report. First, what are the best data sources and methods for validating modeled wind resource estimates? Second, what are the best methods for vertically extrapolating near-surface wind speed measurements to heights that span the rotor-swept area of modern offshore wind turbines?

17 WIND ENERGY↗
DOE OSTI2023

Design Criteria for High-Gradient Radio-Frequency Linacs

This article will review methods used at the SLAC National Accelerator Laboratory and other world accelerator laboratories to design high-gradient normal conducting accelerating structures. A quest for compact radio-frequency linacs fueled decades of studies toward a higher accelerating gradient. A major phenomena limiting the increase of the gradient is vacuum radio-frequency breakdown; therefore, this paper will address the breakdown physics and discuss approaches that reduce the breakdown probability. This discussion will cover both the electrical design and fabrication technology of the accelerating structures to achieve practical operating accelerating gradients in excess of 100 MV/m. Most of the data described here were obtained during the development of 11 GHz linacs for electron–positron linear colliders, so extrapolation of the results to other frequencies should be performed cautiously.

Dolgashev, Valery A. (ORCID:0000000166715402)↗
DOE OSTI2019

A Guideline for the Assessment of Uniaxial Creep and Creep-Fatigue Data and Models

Of primary concern to FE practitioners is a determination of which constitutive models are the “best”, capable of reproducing the mechanisms expected in an intended design accurately; as well as what experimental datasets are proper or “best” to use for fitting the constitutive parameters needed for the model(s) of interest. In addition, the inherent uncertainty in experiments and the repeatability and stability of extrapolations (for long-lived FE components) is a major concern. The goal of this project is to develop a guideline for the assessment of uniaxial creep and creep-fatigue data and models. The research objectives are RO1) develop an aggregated database of creep and creep-fatigue data for P91 steel and 316 stainless steel and perform a systematic assessment of the uncertainty and integrity of the datasets. RO2) employ the database to benchmark existing creep and creep-fatigue models to evaluate the model’s ability to predict standard and nonstandard boundary conditions.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

An extrapolation method for strain ranges and hold times in developing the EPP+SMT creep-fatigue design curves for Alloy 617

Experimental and numerical studies in developing the integrated Elastic–Perfectly Plastic (EPP) plus Simplified Model Test (SMT) design methodology, referred to as the EPP+SMT method, continued in FY2022. This report focuses on the methods for extrapolating the EPP+SMT creep-fatigue (CF) design curves at long hold times and low strain ranges. In this study, the available CF failure data on Alloy 617 at 950°C were analyzed to determine a set of CF failure criteria. At very low strain ranges and long hold times, CF failure data are not accessible by experiments because of the extraordinarily long test durations and the inability of the test machines to accurately control these small strain ranges. A CF experimental approach with the concept of block-strain range CF testing protocol was developed. Tests using this protocol were conducted to generate the needed information for calibrating material parameters of the numerical material models. The Time Fraction based method and Dissipated Energy method were used to extrapolate the CF life curves to low strain ranges and long hold times. Based on the new experimental approach and CF life prediction methods, the CF life curves with various hold times were developed for Alloy 617 at 950°C. In addition, an experiment was designed and is being performed to verify the predicted CF curves at 950°C. The extrapolation procedure will be applied at lower temperatures to complete the development of the EPP+SMT CF design curves for Alloy 617 in F2023.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Observations and Machine-Learned Models of Near-Surface Permafrost along the Koyukuk River, Alaska, USA

This dataset contains GeoTIFs (raster) and GeoPackages (vector) that map observations of near-surface permafrost and not-permafrost from a field campaign conducted near the village of Huslia, AK along the Koyukuk River and its floodplain in July 2018. These data were collected as part of a campaign to understand if and how permafrost impacts riverbank erosion. This problem cannot be assessed without knowing where permafrost exists. Permafrost was observed via frost probing (to a maximum depth of one meter), coring (to a maximum depth of two meters) and bank/bar excavations. An additional boat survey was performed wherein expert (Joel Rowland) judgment assessed the presence or absence of distinctive permafrost features (e.g., overhanging tundra mats, thermoerosional niching, ice wedges, active drainage of ice melt from soils). This dataset also contains the input features and results of two machine learning models (random forest and convolutional neural network) that extrapolate the observations to the full floodplain that may be useful for building, testing, or validating other machine-learned permafrost models. Permafrost data are provided as georasters of the same shape and geovectors (polylines/polygons) and are all projected into EPSG:32605. All data can be visualized with a GIS (QGIS, ArcGIS, etc.).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Solar PV Water Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from bodies of water based on county ordinances as of April 2022. A setback requirement is a minimum distance from water that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a default setback of 30 meters was applied in counties without specific water setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Array↗
DOE OSTI2024

Solar PV Structure Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from structures based on county ordinances as of April 2022. A setback requirement is a minimum distance from a structure that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a default setback of 61 meters was applied in counties without specific structure setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Array↗