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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Unsupervised Clustering and Supervised Regression Learning to Select High Temperature Oxidation-Resistant Materials

High temperature oxidation and corrosion degradation mechanisms dictate the lifetime of materials critical to energy production. The combination of modeling and experimental approaches such as machine learning (ML) and data analytics, with sufficient experimental data, can accelerate the development of new materials while limiting its cost. In the present work, ML will be applied to two high temperature oxidation data libraries (Oak Ridge National Laboratory and National Air and Space Administration) that comprised of about 5000 mass change sample datasheets for a variety of materials and temperatures in dry air and air + 10 % H2O. A python code was developed to prepare the data for machine learning by collecting and formatting oxidation rate constants, alloy compositions and environment of exposure into a single data frame. Scikit-learn library and Statistics and Machine Learning Toolbox within MathWorks were then used to perform unsupervised clustering and supervised regression learning. The impact of dataset distribution on the performance of the developed ML models was evaluated. Potential strategies to improve the predictions and enhance extrapolative capability of the previously trained model were investigated.

Romedenne, Marie [ORNL] (ORCID:0000000317936561)↗
DOE OSTI2019

Comparing smoothing techniques for extracting MOSFET threshold voltage

Measurement noise acts as a barrier to the accurate calculation of threshold voltage by derivative-based extraction methods. We examined several smoothing techniques and their effects on measurement noise in threshold voltage extracted by the linear extrapolation method and the g m /I d method. We applied these techniques on a set of SiC power MOSFET devices before and after they had undergone accelerated thermal testing. The smoothing methods examined are least-squares polynomial fitting, low-pass filtering using Butterworth and Chebyshev fillers, and differential smoothing. These methods are compared by their shift in threshold voltage compared to unsmoothed data and their standard deviation among the measurements of each device. All methods performed poorly at smoothing when extracting via the g m /I d due to the large amounts of high-magnitude subthreshold noise, mostly tending to extract threshold voltages far below the expected value. Differential smoothing provided results closest to the expected value with an absolute shift of 1.2 V on average. The smoothing methods performed better with the LE method. On a failed device, all smoothing methods except polynomial smoothing preserved the large threshold voltage anomaly for four out of five measurements. These results show that choice of smoothing method, if used must be evaluated based on the device and extraction method.

42 ENGINEERING↗
DOE OSTI2024

Solar PV Oil and Gas Pipeline Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from oil and gas pipelines. A setback requirement is a minimum distance from a pipeline that an energy project may be developed. As of April 2022, no ordinances were discovered for any counties. Such ordinances are likely to arise as regulations continue to expand. Therefore, this dataset applies a 30-meter setback, sourced from trends in other infrastructure. A TIF data file and a PNG map of the data are provided, 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 OSTI2021

Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parametrized from MD simulations of complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four order of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example in the case of large molecules or solids. In this work, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parametrize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation timescales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parametrized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.

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

A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes

Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.

Chemical engineering↗
DOE OSTI2022

Hydride-based thermal energy storage

Abstract The potential and research surrounding metal hydride (MH) based thermal energy storage is discussed, focusing on next generation thermo-chemical energy storage (TCES) for concentrated solar power. The site availability model to represent the reaction mechanisms of both the forward and backward MH reaction is presented, where this model is extrapolated to a small pilot scale reactor, detailing how a TCES could function/operate in a real-world setting using a conventional shell & tube reactor approach. Further, the important parameter of effective thermal conductivity is explored using an innovative multi-scale model, to providing extensive and relevant experimental data useful for reactor and system design. Promising high temperature MH material configurations may be tuned by either destabilisation, such as using additions to Ca and Sr based hydrides, or by stabilisation, such as fluorine addition to NaH, MgH 2 , or NaMgH 3 . This versatile thermodynamic tuning is discussed, including the challenges in accurately measuring the material characteristics at elevated temperatures (500 –700 °C). Attention to scale up is explored, including generic design and prototype considerations, and an example of a novel pilot-scale pillow-plate reactor currently in development; where materials used are discussed, overall tank design scope and system integration.

25 ENERGY STORAGE↗
DOE OSTI2022

Increased Arctic NO3− Availability as a Hydrogeomorphic Consequence of Permafrost Degradation and Landscape Drying

Climate-driven permafrost thaw alters the strongly coupled carbon and nitrogen cycles within the Arctic tundra, influencing the availability of limiting nutrients including nitrate (NO3−). Researchers have identified two primary mechanisms that increase nitrogen and NO3− availability within permafrost soils: (1) the ‘frozen feast’, where previously frozen organic material becomes available as it thaws, and (2) ‘shrubification’, where expansion of nitrogen-fixing shrubs promotes increased soil nitrogen. Through the synthesis of original and previously published observational data, and the application of multiple geospatial approaches, this study investigates and highlights a third mechanism that increases NO3− availability: the hydrogeomorphic evolution of polygonal permafrost landscapes. Permafrost thaw drives changes in microtopography, increasing the drainage of topographic highs, thus increasing oxic conditions that promote NO3− production and accumulation. We extrapolate relationships between NO3− and soil moisture in elevated topographic features within our study area and the broader Alaskan Coastal Plain and investigate potential changes in NO3− availability in response to possible hydrogeomorphic evolution scenarios of permafrost landscapes. These approximations indicate that such changes could increase Arctic tundra NO3− availability by ~250–1000%. Thus, hydrogeomorphic changes that accompany continued permafrost degradation in polygonal permafrost landscapes will substantially increase soil pore water NO3− availability and boost future fertilization and productivity in the Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Low-lying states and total internal partition sums of CH

The electronic structure and spin-orbit states of the CH radical have been systematically investigated using multi-reference configuration interaction (MRCI) and single-reference coupled-cluster (CC) methods. These calculations were performed in conjunction with large correlation-consistent basis sets of quadruple-, quintuple-, and sextuple-ζ quality. To achieve high accuracy, electronic energies for all states were extrapolated to the complete basis set (CBS) limit, enabling the detailed construction of potential energy curves and determination of reliable spectroscopic constants. Spin-orbit coupling effects were explicitly incorporated, and vibrational energy levels were computed via Numerov analysis. Furthermore, the resulting values exhibit good to excellent agreement with available experimental data. Dipole moment and transition dipole moment curves were evaluated to assess the opacity characteristics of CH, revealing that transitions such as Χ 2 Π (u′′ = 0) → Α 2 Δ (u′ = 0), Χ 2 Π (u′′ = 0) → Β 2 Σ − (u′ = 0), Χ 2 Π (u′′ = 0) → C 2 Σ + (u′ = 0), and Χ 2 Π (u′′ = 0) → D 2 Σ + (u′ = 3) are particularly probable. Finally, the total internal partition function sum (TIPS) of CH was computed over a broad temperature range (10–30,000 K) based on our high-accuracy ab initio results.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Transport coefficients of warm dense matter from Kohn-Sham density functional theory

We present a comprehensive study of transport coefficients including DC electrical conductivity and related optical properties, electrical contribution to the thermal conductivity, and the shear viscosity via ab initio molecular dynamics and density functional theory calculations on the “priority 1” cases from the “Second Charged-Particle Transport Coefficient Workshop” [Stanek et al., Phys. Plasmas (to be published 2024)]. The purpose of this work is to carefully document the entire workflow used to generate our reported transport coefficients, up to and including our definitions of finite size and statistical convergence, extrapolation techniques, and choice of thermodynamic ensembles. In pursuit of accurate optical properties, we also present a novel, simple, and highly accurate algorithm for evaluating the Kramers–Kronig relations. These heuristics are often not discussed in the literature, and it is hoped that this work will facilitate the reproducibility of our data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

A consistent, Bayesian, approach to the cross section probability distribution in the unresolved resonance region

The cross sections of neutron-induced reactions can be divided into three energy ranges: the resolved resonance region (RRR), the unresolved resonance region (URR), and the fast region. In general, the cross sections in the URR show significant fluctuations that cannot be predicted and cannot be experimentally resolved, thus, it is commonly assumed that the cross section at a specific energy is given by a probability distribution function (PDF) over a range of values that can span several orders of magnitude. The current methodology used to describe such behavior is to construct the PDF by stochastically generating resonance ladders and numerically measuring the PDF. The resonance ladders are sampled using known resonance statistical properties and average resonance widths and spacings extrapolated from the RRR. Although this is a standard and widely used technique, it is computationally very expensive, therefore, an alternative, analytical, approach would be preferable due to the considerable speed up of the computational time in real life applications. Moreover, the current methodology does not take into account existing experimental data, such for total and capture cross sections, that are available for many nuclei. Finally, this approach was developed to be used in reactor-scale applications and it is not suited for use in single-event applications. In this work we will rethink the entire approach to the PDF construction using a Bayesian mindset. This will allow us to provide a different definition of the PDF that allows a much faster calculation of the higher-temperature PDFs and a proper combination of theoretical and experimental PDFs following the probability theory. We will also show that our definition is well suited for single-event applications and we will make an explicit connection between our method and the standard approach. We do this by showing that the central limit theorem applies and our method leads to the same PDF obtained with the standard methodology, for a large number of events per history.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Pulsed-Neutron Experiments at the Inherently Safe Subcritical Assembly

The pulsed neutron technique is a powerful, dynamic method to assay the reactivity of a multiplying system. This work presents the novel application of the pulsed neutron technique to the Inherently Safe Subcritical Assembly, an experimental configuration accepted by the International Criticality Safety Benchmark Evaluation Project Handbook. The experiments were replicated with COG11.3, a continuous-energy Monte Carlo code. The pulsed neutron data were analyzed using the Sjöstrand and Gozani area-ratio methods and by extracting the prompt neutron decay constant. Subsequent static k-eigenvalue and 𝛼-eigenvalue simulations were also performed for the same configurations. Neutron detector dead-time effects from the experiments were corrected using the Backwards Extrapolation Method and shown to have a negligible impact on the estimated reactivities. The results highlight that capturing time-dependent effects like delayed neutron precursor buildup are essential to accurately reproduce experimental results. They also highlight the importance of shielding the detectors from generator source neutrons in deeply subcritical configurations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Local reduced-order modeling for electrostatic plasmas by physics-informed solution manifold decomposition

Despite advancements in high-performance computing and modern numerical algorithms, computational cost remains prohibitive for multi-query kinetic plasma simulations. Here, in this work, we develop data-driven reduced-order models (ROMs) for collisionless electrostatic plasma dynamics, based on the kinetic Vlasov-Poisson equation. Our ROM approach projects the equation onto a linear subspace defined by the proper orthogonal decomposition (POD) modes. We introduce an efficient tensorial method to update the nonlinear term using a precomputed third-order tensor. We capture multiscale behavior with a minimal number of POD modes by decomposing the solution manifold into multiple time windows and creating temporally local ROMs. We consider two strategies for decomposition: one based on the physical time and the other based on the electric field energy. Applied to the 1D1V Vlasov–Poisson simulations, that is, prescribed E-field, Landau damping, and two-stream instability, we demonstrate that our ROMs accurately capture the total energy of the system both for parametric and time extrapolation cases. The temporally local ROMs are more efficient and accurate than the single ROM. In addition, in the two-stream instability case, we show that the energy-windowing reduced-order model (EW-ROM) is more efficient and accurate than the time-windowing reduced-order model (TW-ROM). With the tensorial approach, EW-ROM solves the equation approximately 90 times faster than Eulerian simulations while maintaining a maximum relative error of 7.5% for the training data and 11% for the testing data.

Electrostatic plasmas↗
DOE OSTI2021

The denatured state of HIV ‐1 protease under native conditions

Abstract The denatured state of several proteins has been shown to display transient structures that are relevant for folding, stability, and aggregation. To detect them by nuclear magnetic resonance (NMR) spectroscopy, the denatured state must be stabilized by chemical agents or changes in temperature. This makes the environment different from that experienced in biologically relevant processes. Using high‐resolution heteronuclear NMR spectroscopy, we have characterized several denatured states of a monomeric variant of HIV‐1 protease, which is natively structured in water, induced by different concentrations of urea, guanidinium chloride, and acetic acid. We have extrapolated the chemical shifts and the relaxation parameters to the denaturant‐free denatured state at native conditions, showing that they converge to the same values. Subsequently, we characterized the conformational properties of this biologically relevant denatured state under native conditions by advanced molecular dynamics simulations and validated the results by comparison to experimental data. We show that the denatured state of HIV‐1 protease under native conditions displays rich patterns of transient native and non‐native structures, which could be of relevance to its guidance through a complex folding process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Taming nuclear mass models with Gaussian processes

We propose a new set of nuclear mass predictions based on multiple theoretical mass models. By employing Gaussian process regression with the Matérn kernel, we achieved root-mean-square (rms) deviations below 100 keV for the training dataset. The best-performing mass models achieved rms deviations below 150 keV for the new precise mass data from AME2020, whereas the ensemble average showed robust performance across the nuclear chart. Our approach uniquely combines: (1) systematic refinement of eight mass models through their residuals, (2) physics-informed features, including magic numbers, nucleon parity numbers, neutron excess, and nuclear collectivity, and (3) theory-to-theory validation demonstrating robust extrapolation capability. We find that the Matérn kernel provides superior uncertainty quantification compared to the RBF kernel, with a length-scale analysis revealing enhanced inter-nuclei correlations. We provide complete mass predictions for all unknown nuclides in AME2020, offering valuable constraints for nuclear structure studies and astrophysical modeling when used with proper uncertainty propagation.

Gaussian processes↗
DOE OSTI2026

Machine learning based prediction of airflow maldistribution in air-to-refrigerant heat exchangers

Flow maldistribution is a common challenge in heat exchanger (HX) design and particularly important for air-to-refrigerant geometries where capacity losses can approach 65%. This has a major impact on central air conditioning systems, as compact duct design motivates the use of A-type HXs which are known to be affected by airflow maldistribution. Because velocity profiles are difficult to predict, components are often oversized leading to increased material cost, system footprint, and refrigerant charge. Several studies detail airflow maldistribution for individual HXs and packages, but findings cannot always be extrapolated to new designs. In this work, a machine learning (ML) based flow profile prediction framework is developed and applied to two common package configurations: (i) A-type and (ii) U-type HXs, across a broad range of HX geometries and flow rates. Porous media CFD simulations are validated against independent data for both package types as well as comprehensive in house measurements for a finless geometry with shape optimized non-round tubes, which validates the framework for new heat transfer surfaces. The ML models are trained on the porous media CFD simulations, predicting volumetric flow rate (VFR) within 1.1% and 1.9% with maximum relative L 2 norm errors of 0.48 and 0.65, respectively, while also delivering 10 5 speed up factor compared to full porous media CFD. HX level simulations show an up to 9% reduction in heat transfer from flow maldistribution, with greater losses occurring at smaller half apex angles. This framework enables rapid and highly accurate prediction of airflow maldistribution induced capacity degradation.

42 ENGINEERING↗
DOE OSTI2024

The Atacama Cosmology Telescope: Cosmology from Cross-correlations of unWISE Galaxies and ACT DR6 CMB Lensing

We present tomographic measurements of structure growth using cross-correlations of Atacama Cosmology Telescope (ACT) DR6 and Planck cosmic microwave background (CMB) lensing maps with the unWISE Blue and Green galaxy samples, which span the redshift ranges 0.2 ≲ z ≲ 1.1 and 0.3 ≲ z ≲ 1.8, respectively. We improve on prior unWISE cross-correlations not just by making use of the new, high-precision ACT DR6 lensing maps, but also by including additional spectroscopic data for redshift calibration and by analyzing our measurements with a more flexible theoretical model. We determine the amplitude of matter fluctuations at low redshifts (z ≃ 0.2–1.6), finding S 8 ≡ σ 8 (Ω m /0.3) 0.5 = 0.813 ± 0.021 using the ACT cross-correlation alone and S 8 = 0.810 ± 0.015 with a combination of Planck and ACT cross-correlations; these measurements are fully consistent with the predictions from primary CMB measurements assuming standard structure growth. The addition of baryon acoustic oscillation data breaks the degeneracy between σ 8 and Ω m , allowing us to measure σ 8 = 0.813 ± 0.020 from the cross-correlation of unWISE with ACT and σ 8 = 0.813 ± 0.015 from the combination of cross-correlations with ACT and Planck. These results also agree with the expectations from primary CMB extrapolations in ΛCDM cosmology; the consistency of σ 8 derived from our two redshift samples at z ∼ 0.6 and 1.1 provides a further check of our cosmological model. Our results suggest that structure formation on linear scales is well described by ΛCDM even down to low redshifts z ≲ 1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Two-stage formation-energy correction (NbZr, TaZr, VZr)

This bundle contains the scripts, the raw and corrected per-structure data, and the manuscript plots for the NbZr / TaZr / VZr BCC binary formation energies and the associated RMSDs. Why a two-stage correction is necessary: The "raw" formation energy of every relaxed VASP configuration is computed in the usual way, FE_raw(c) = E_alloy(c) - sum_i x_i * E_pure_i , where E_pure_i are the per-atom total energies of the pure-element reference structures (Nb, Ta, V, Zr in the same BCC supercell, with identical INCAR / KPOINTS / PAW choices). With perfectly consistent reference runs the raw FE should vanish at the two pure-element endpoints (x = 0 and x = 1) by construction. In practice this does not hold for two reasons that are present in our dataset: 1. Reference-energy inconsistency (composition-dependent bias). Even with identical input parameters, the pure-element runs (stored in `corrected_DFT_pure_element_runs/`) differ slightly from the values that would be implied by the alloy runs at near-pure compositions (a few meV/atom). This bias is approximately linear in concentration, because the residual error in E_pure_Nb (or E_pure_Ta / E_pure_V) propagates into FE_raw(c) as (1 - x) * dE_pure_1, and the corresponding error in E_pure_Zr propagates as x * dE_pure_2. Left uncorrected, this produces a non-physical "tilt" of FE_raw(x) and shifts the entire FE-vs-x cloud away from zero at the endpoints. 2. Endpoint anchoring against the audited true endpoints. The strict endpoint values (FE_x0_meVatom, FE_x1_meVatom in `corrected_fe_strict_endpoints_20260518/strict_endpoint_check_20260518.csv`) were re-derived from an independent cross-check of the pure-element runs. After stage 1 removes the linear bias, the near-pure compositions in the alloy dataset still extrapolate to values that differ slightly from these audited endpoints — because stage 1 is fit from a few near-end alloy bins, not from the audited pure-element references themselves. The README.txt file discusses how these issues are addressed by the two-stage correction, and describes folder layout, pipeline summary, and how to re-run.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Melting and phase relations of Fe-Ni-Si determined by a multi-technique approach

Many studies have suggested silicon as a candidate light element for the cores of Earth and Mercury. However, the effect of silicon on the melting temperatures of core materials and thermal profiles of cores is poorly understood, due to disagreements among melt detection techniques, uncertainties in sample pressure evolution during heating, and sparsity of studies investigating the combined effects of nickel and silicon on the phase diagram of iron. Here, in this study we develop a multi-technique approach for measuring the high-pressure melting and solid phase relations of iron alloys and apply it to Fe 0.8 Ni 0.1 Si 0.1 (Fe-11wt%Ni-5.3wt%Si), a composition compatible with recent estimates for the cores of Earth and Mercury. This approach combines results (20-83 GPa) from two atomic-level techniques: synchrotron Mossbauer spectroscopy (SMS) and synchrotron x-ray diffraction (XRD). Melting is independently detected by the loss of the Mossbauer signal, produced exclusively by solid-bound iron nuclei, and the onset of a liquid diffuse x-ray scattering signal. The use of a burst heating and background updating method for quantifying changes in the reference background during heating facilitates the determination of liquid diffuse signal onsets and leads to strong reproducibility and excellent agreement in melting temperatures determined separately by the two techniques. XRD measurements additionally constrain the hcp-fcc phase boundary and in-situ pressure evolution of the samples during heating. We apply our updated thermal pressure model to published SMS melting data on fcc-Fe and fcc-Fe 0.9 Ni 0.1 to precisely evaluate the effect of silicon on melting temperatures. We find that the addition of 10 mol% Si to Fe 0.9 Ni 0.1 reduces melting temperatures by ~250 K at low pressures (<60 GPa) and flattens the hcp-fcc phase boundary. Extrapolating our results, we constrain the location of the hcp-fcc-liquid quasi-triple point at 147±14 GPa and 3140±90 K, which implies a melting temperature reduction of 500 K compared with Fe 0.9 Ni 0.1 . The results demonstrate the advantages of combining complementary experimental techniques in investigations of melting under extreme conditions.

36 MATERIALS SCIENCE↗