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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Regional Oil and gas Aerial Methane Synthesis model (ROAMS) v2.0

The Regional Oil and gas Aerial Methane Synthesis model is a tool to convert the results of wide-area, source-resolved aerial methane remote sensing surveys of oil and natural gas infrastructure in a given region into methane emissions inventories (estimates of the magnitude and breakdown of methane emissions from the surveyed infrastructure). The tool leverages databases of source-resolved methane emissions detected in aerial surveys, aerial survey coverage information (which areas were measured and when), data summarizing surveyed oil and natural gas infrastructure and production (derived from third-party databases), as well as state-of-the-art mechanistic emissions simulation tools to characterize emissions too small for the aerial system to see. The regional methane emissions estimates produced by this tool are much more granular in both space and asset type than common satellite- or flux tower-based regional estimates. Unlike other tools for converting site-level measurements into regional emissions estimates, our unique geostatistical approach integrates aerially measured emissions with limited need for statistical extrapolation, which can be highly sensitive to modeler assumptions. As a result, ROAMS-based estimates of regional methane emissions from oil and gas activity are widely viewed as highly credible, as evidenced by the success of Dr. Sherwin's recent paper in Nature.

Sherwin, Evan [Lawrence Berkeley National Laborato↗
DOE OSTI2025

Regional Oil and gas Aerial Methane Synthesis model (Analytica) (ROAMS Analytica) v1.5.2

The Regional Oil and gas Aerial Methane Synthesis model (Analytica) is a tool to convert the results of wide-area, source-resolved aerial methane remote sensing surveys of oil and natural gas infrastructure in a given region into methane emissions inventories (estimates of the magnitude and breakdown of methane emissions from the surveyed infrastructure). This version is written in the Analytica programming language, and this version accompanies a correction in preparation for submission to Sherwin et al. 2024 (Nature). The tool leverages databases of source-resolved methane emissions detected in aerial surveys, aerial survey coverage information (which areas were measured and when), data summarizing surveyed oil and natural gas infrastructure and production (derived from third-party databases), as well as state-of-the-art mechanistic emissions simulation tools to characterize emissions too small for the aerial system to see. The regional methane emissions estimates produced by this tool are much more granular in both space and asset type than common satellite- or flux tower-based regional estimates. Unlike other tools for converting site-level measurements into regional emissions estimates, our unique geostatistical approach integrates aerially measured emissions with limited need for statistical extrapolation, which can be highly sensitive to modeler assumptions. As a result, ROAMS-based estimates of regional methane emissions from oil and gas activity are widely viewed as highly credible, as evidenced by the success of Dr. Sherwin's recent paper in Nature.

Sherwin, Evan [Lawrence Berkeley National Laborato↗
DOE OSTI2022

Aero-Optics of Hypersonic Turbulent Boundary Layers

Aero-optics refers to optical distortions due to index-of-refraction gradients that are induced by aerodynamic density gradients. At hypersonic flow conditions, the bulk velocity is many times the speed of sound and density gradients may originate from shock waves, compressible turbulent structures, acoustic waves, thermal variations, etc. Due to the combination of these factors, aero-optic distortions are expected to differ from those common to sub-sonic and lower super-sonic speeds. This report summarizes the results from a 2019-2022 Laboratory Directed Research and Development (LDRD) project led by Sandia National Laboratories in collaboration with the University of Notre Dame, New Mexico State University, and the Georgia Institute of Technology. Efforts extended experimental and simulation methodologies for the study of turbulent hypersonic boundary layers. Notable experimental advancements include development of spectral de-aliasing techniques for highspeed wavefront measurements, a Spatially Selective Wavefront Sensor (SSWFS) technique, new experimental data at Mach 8 and 14, a Quadrature Fringe Imaging Interferometer (QFII) technique for time-resolved index-of-refraction measures, and application of QFII to shock-heated air. At the same time, model advancements include aero-optic analysis of several Direct Numerical Simulation (DNS) datasets from Mach 0.5 to 14 and development of wall-modeled Large Eddy Simulation (LES) techniques for aero-optic predictions. At Mach 8 measured and predicted root mean square Optical Path Differences agree within confidence bounds but are higher than semi-empirical trends extrapolated from lower Mach conditions. Overall, results show that aero-optic effects in the hypersonic flow regime are not simple extensions from prior knowledge at lower speeds and instead reflect the added complexity of compressible hypersonic flow physics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Geodyn Material Library: Pseudocap models for dry porous tocks

This report describes the second edition of the Pseudocap Strength models for porous rocks implemented in GEODYN material library. The first model was developed in 2007 and calibrated for concrete. Then, the model parameters were calibrated based on triaxial tests reported for limestones and sandstones of various porosities. In these models some key parameters were chosen as functions of the reference porosity,Φ. Since then multiple modifications were implemented in the model, therefore, it has been recalibrated for some common porous materials such as limestones, sandstones, alluvium, tuffs and granite. Two types of models are described in this repot. The first type (called Pseudocap Model or PM) is for rocks from a specific location. Parameters were calibrated for several specific geologic materials. The second type (called Generic Pseudocap Model or GPM) is useful for the sites where only basic information (rock type, porosity) is available. Generic models include built-in correlations between porosities and other mechanical properties observed for certain rock types. The models of both types were validated by comparing not only to quasi-static triaxial tests for these materials but also to shock Hugoniot data and spherical explosion data for some materials. All models were derived in the frame of isotropic plasticity. They are designed to be used in explicit finite element/difference codes. Tangent stiffness tensor is not provided but can be calculated numerically for the model to be used in implicit finite element codes. For an isotropic material the stress can be decomposed into volumetric and deviatoric parts. The volumetric part is modeled using an Equation of state (EOS) which calculates the pressure and the bulk sound speed as functions of the internal specific energy and density. Here a simple, Mie-Gruneisen EOS is presented, but tabulated EOS (LEOS) provided by the library can be used as well. The stress is limited by the yield surface which depends on three invariants of the stress tensor and specific internal energy. In addition, to capture the strain-rate dependence a simple multiplier is used for the yield surface which depends on the equivalent plastic strain rate. The failure surface (the ultimate yield, Y f , defined later) is chosen in the Hoek-Brown form, commonly used in rock mechanics. It includes measurable parameters such as Unconfined Compressive Strength (UCS) as well as scale parameters characterizing the quality of the rock such as GSI (Geologic Strength Index). Thus, even though the model is calibrated for small samples it offers a way to extrapolate the strength to the field scale using geological characterization of the rock mass. The model captures effects of brittle-ductile transition in rocks by introducing a cap multiplier to the yield function. The rate of dilatancy (bulking) is proportional to the slope of the yield surface affected by the cap. Therefore, it takes place only at low confinements when the pressure is less than the brittle-ductile transition pressure, P BD . On the contrary, the porous compaction takes place at pressures higher than P BD . The cap moves as the porosity is compacted or new porosity is generated due to dilatancy. The porous compaction is modeled using an evolution equation which includes deviatoric stress so that the onset of compaction corresponds to the cap surface. The model captures effects of shear-enhanced compaction which is an important for porous rocks. Section 2 describes the modeling framework and Section 3 presents the model calibration procedure. Section 4 compares experimental data for various rocks versus model predictions. The model parameters used for this comparison are given in Appendix. The files with material constants are available with the latest GEODYN material library distribution.

58 GEOSCIENCES↗
DOE OSTI2025

Porosity, swelling, and composition evolution in high-burnup monolithic U-Mo fuel

The microstructural progression of very high-burnup (>8 × 10 21 fissions/cm 3 ) monolithic uranium-molybdenum (U-10wt.%Mo) was analyzed, providing crucial insights into the behavior of post-recrystallized nuclear fuel, where scant data exists. Three focused ion beam cuboids sourced from a fuel plate with varying local burnups of 8.86 × 10 21 , 9.05 × 10 21 , and 9.36 × 10 21 fissions/cm 3 were characterized. The porosity and composition of the samples were evaluated to characterize the evolution of the microstructure as a function of fission density and different locations on the fuel plate, while simultaneously isolating plate-specific parameters such as Zr diffusion barrier thickness, hot-isostatic-pressing conditions, enrichment, and reactor conditions. The porosity was segmented, and the three-dimensional distribution of the porosity was extrapolated from the two-dimensional segmentation. The composition was assessed and quantified using energy-dispersive X-ray spectroscopy areal mapping. The porosity fraction increased as a function of the burnup from 27.77±0.51, 35.12±1.54, and 37.71±0.44 % for 8.86 × 10 21 , 9.05 × 10 21 , and 9.36 × 10 21 fissions/cm 3 , respectively. When compared to literature, the porosity volume fraction plateaus at burnups greater than 6 × 10 21 fissions/cm 3 , while the pore size grows linearly as a function of fission density. The number of large pores increased in number density as a function of burnup, while the smallest pores (<0.3 µm) increased up to 9.05 × 10 21 fissions/cm 3 , followed by a decrease at 9.36 × 10 21 fissions/cm 3 . The delamination and cracking in the fuel plate propagated through an interconnected porosity sublayer identified ∼5 µm from the diffusion barrier. The local swelling of the specimens was within or near the prediction bounds of the Robinson-Williams model for local swelling. The fission products, strontium, barium, cerium, and cesium, precipitated into the pores, while neodymium accumulated adjacent to the pores. Furthermore, these findings have direct implications for the development of fuel performance codes and the accurate documentation of the microstructure evolution in high burnup U-Mo, thus enhancing the safety and efficiency of nuclear fuel usage.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Implementation of compound refractive lenses for large field-of-view x-ray phase-contrast imaging during hypervelocity impact experiments

Synchrotron x-ray phase-contrast imaging (XPCI) offers time-resolved visualization of dynamic compression phenomena, but its intrinsically small field-of-view (FOV) limits the time that key features remain in frame. A novel approach to enlarge the FOV is achieved by positioning a two-dimensional parabolic compound refractive lens (CRL) upstream of the sample to deliberately defocus the white beam. Ray-tracing simulations and XPCI measurements show that this CRL configuration can expand the beam by ∼50% vertically and ∼15% horizontally based on the full width at half-maximum of the beam. Implementing the CRL, however, attenuates the photon flux and lowers signal-to-noise ratio (SNR). Task-based analysis using a calibration grid (30 μm dots) showed that both setups fail to consistently meet the Rose criterion (SNR ≥ 5) for features of this size in single-bunch imaging. Extrapolating the measured SNR Rose values suggests that the minimum consistently detectable feature lies closer to 30–40 μm for the standard XPCI setup and above 40 μm for CRL-XPCI. Despite this limitation, the CRL configuration nearly doubles the illuminated area, enabling simultaneous tracking of front and rear observations of boron carbide targets subjected to rod and sphere impacts at 1.0–2.6 km/s. Image tracking algorithms and photonic Doppler velocimetry were used to measure penetration and rear-surface velocity histories. Together, these measurements capture crack fronts, penetration, and material breakout, offering new benchmark data for validating high-strain-rate constitutive models of ceramic materials.

Ceramic materials↗
DOE OSTI2023

Bayesian Parameter Estimation of the k-ω Shear Stress Transport Model for Accurate Simulations of Impinging-Jet Heat Transfer

Given the lack of fusion-relevant component test facilities, current estimates of the thermo-fluid performance of plasma-facing components are based for the most part on numerical simulations. A major source of uncertainty in these simulations is the semiempirical turbulence (closure) models for the Reynolds stresses appearing in the governing Reynolds-averaged Navier-Stokes equations, which involve a set of constants that depend upon the flow. The objective of this study is to evaluate Bayesian parameter estimation of turbulence closure constants in ANSYS Fluent to model heat transfer in impinging jets. The Bayesian statistical calibration produces a probability distribution for these constants from experimental data; the maximum a posteriori estimates are then taken to be the calibrated constants, or parameters. The turbulence model constants are calibrated using an experimental study of a submerged jet of air impinging on a flat heated surface at Reynolds numbers Re = O(10 4 ) and impingement distance in jet diameters H/d = 2. Numerical predictions using the calibrated model parameters are then compared with those generated using the default constants. Predictions obtained with model parameters calibrated on datasets of two different sizes are compared to evaluate the effect of the number of calibration samples. Lastly, the extrapolative ability of the calibrated model is examined by predictions at a Re beyond the calibration values.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Characterization of 316L Stainless Steel after Solid Phase Processing using Ultrasonic NDE Method

The Pacific Northwest National Laboratory undertook the Materials Characterization, Prediction, and Control Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems produced via advanced manufacturing methods, such as solid phase processing, for use in national security and advanced energy applications (Smith 2021). A motivation of the Materials Characterization, Prediction, and Control Project was to demonstrate ultrasonic testing as a nondestructive evaluation method to complement traditional destructive methods for characterizing material microstructure with emphasis on grain size determination using a method that may have future applications for real-time inline process monitoring. The objective of the work described in this report is to establish the process and an analysis method for measuring grain sizes of polycrystalline metals with ultrafine grains using ultrasonic shear wave backscattering, building on prior studies on coarser-grained material. The work involves five tasks: Measured ultrasonic backscattering experimentally for a series of 316L stainless steel specimens with various grain sizes made by friction stir processing. Calculated ultrasonic backscattering coefficients from experimental data based on a physical measurement model. Measured ground truth grain sizes of the specimens from electron backscatter diffraction grain boundary images using a generalization of the ASTM E112 (ASTM 2021) intercept method. Built a curve of ultrasonic backscattering coefficients versus the ground truth intercept-based grain sizes to determine the correlation between mean grain sizes and ultrasonic measurements. Demonstrated the ability of using the correlation curve to deduce grain sizes with measured ultrasonic backscattering coefficients for a few 316L stainless steel specimens whose grain sizes were unknown beforehand but were targeted to be an extrapolation to larger grain sizes than used to formulate the correlation curves. Experimental procedures and computational algorithms are developed and validated for these tasks. This work establishes an ultrasonic technique for characterizing material microstructure with ultrafine grains that are often resulted by solid-phase processing. The technique is nondestructive, and it has the potential to be used for real time inline process monitoring. This work successfully demonstrates the viability of an ultrasonic nondestructive evaluation method for microstructural characterization of material having ultrafine grain structure (as small as 1?mm) and produced by an advanced manufacturing method. This includes a demonstration of the method to extrapolate to other conditions. While not demonstrated here, the method is expected to be viable for in-line, or near-inline, process monitoring in advanced manufacturing applications with suitable consideration for access of instrumentation to the material being manufactured.

316 L Stainless Steel↗
DOE OSTI2025

Control Mechanisms for Self‐Sealing in Activated Clay‐Rich Faults Through Controlled Hydraulic Injection Experiment

Abstract In a high‐pressure injection fault activation experiment conducted at the Mont Terri underground research laboratory in Switzerland, the transmissivity of the Opalinus Clay fault significantly increased due to opening and shearing. The fluid injection, spanning a few hours, generated a 10 m radius fault activation patch. Subsequent pressure pulse tests conducted bi‐weekly for a year revealed the gradual return of fault transmissivity to its initial state. The study utilized fluid pressure decay analysis, optical fiber monitoring, continuous active source seismic measurements and borehole displacement sensors for measuring fault displacements. The fault zone exhibited a dilation of approximately 1.4 mm, associated with both normal and tangential movements during activation, resulting in a sudden transmissivity increase from 1 × 10 −12 to 3.2 × 10 −7 m 2 /s. Early post‐activation, transient compaction and the subsequent slow compaction were observed, transitioning to an extension regime. The pressure pulse tests demonstrated a rapid transmissivity drop by more than two orders of magnitude within the first 10 days, followed by a gradual and less pronounced decrease. Plastic shear and compaction dominated the transmissivity evolution until 70 days after injection ended, followed by a period where additional factors, such as clay mineral swelling, influenced the behavior. Extrapolation suggested a sealing process taking at least 50 years after the initial activation. Plain Language Summary A field‐scale fault activation experiment offers valuable insights into the elasto‐plastic processes governing the sealing of shale faults. The experiment reveals a rapid increase in the fault's transmissivity by approximately five orders of magnitude during activation. Subsequent observations show a gradual transmissivity decrease by about three orders of magnitude post‐activation, with slow long‐term plastic shear and compaction of the fault competing against secondary processes, notably clay mineral swelling. All conceptual models employed to interpret these field data converge on the estimation that the fault's return to its initial low transmissivity state would require a minimum of 50 years. Key Points High‐pressure injection fault activation experiment at the Mont Terri underground research laboratory Continuous transmissivity measurements record self‐sealing inside a clay‐rich fault zone Transmissivity undergoes a phase of domination by slow plastic compaction and shearing during the initial post‐activation period, with mineral swelling exerting its influence over the long term

Guglielmi, Yves↗
DOE OSTI2025

Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV

The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a reactor-relevant high-performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid instability limits. High-performance experiments at TCV show statistically significant improvements in relevant metrics. A predict-first experiment, increasing plasma current by 20% from baseline, demonstrates the NSSM’s ability to make small extrapolations. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty and demonstrates the relevance of SciML for fusion experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol

The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Probing the Self-Assembly dynamics of cellulose nanocrystals by X-ray photon correlation spectroscopy

Charge-stabilized colloidal cellulose nanocrystals (CNCs) can self-assemble into higher-ordered chiral nematic structures by varying the volume fraction. The assembly process exhibits distinct dynamics during the isotropic to liquid crystal phase transition, which can be elucidated using X-ray photon correlation spectroscopy (XPCS). Anionic CNCs were dispersed in propylene glycol (PG) and water spanning a range of volume fractions, encompassing several phase transitions. Coupled with traditional characterization techniques, XPCS was conducted to monitor the dynamic evolution of the different phases. Additionally, simulated XPCS results were obtained using colloidal rods and compared with the experimental data, offering additional insights into the dynamic behavior of the system. The results indicate that the particle dynamics of CNCs undergo a stepped decay in three stages during the self-assembly process in PG, coinciding with the observed phases. The phase transitions are associated with a total drop of Brownian diffusion rates by four orders of magnitude, a decrease of more than a thousand times slower than expected in an ideal system of repulsive Brownian rods. Given the similarity in the phase behaviors in CNCs dispersed in PG and in water, we hypothesize that these dynamic behaviors can be extrapolated to other polar solvent environments. Importantly, these findings represent the direct measurement of CNC dynamics using XPCS, underscoring the feasibility of directly assessing the dynamic behavior of other rod-like colloidal suspensions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Learning continuous models for continuous physics

Abstract Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict the dynamics of such systems. A core issue with this approach is that ML models are typically trained on discrete data, using ML methodologies that are not aware of underlying continuity properties. This results in models that often do not capture any underlying continuous dynamics—either of the system of interest, or indeed of any related system. To address this challenge, we develop a convergence test based on numerical analysis theory. Our test verifies whether a model has learned a function that accurately approximates an underlying continuous dynamics. Models that fail this test fail to capture relevant dynamics, rendering them of limited utility for many scientific prediction tasks; while models that pass this test enable both better interpolation and better extrapolation in multiple ways. Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

First results of the NEXT-100 detector using $^{83m}$Kr decays

The NEXT collaboration is investigating the double beta decay of $ ^{136}\mathrm Xe$ using high-pressure gas electroluminescent time projection chambers, which provide excellent energy resolution together with a robust topological signature. Operating at the Laboratorio Subterráneo de Canfranc (LSC) and building on the success of the NEXT-White detector, the NEXT-100 apparatus began commissioning in May 2024 and started operation with xenon at a pressure of 4 bar in October 2024. We report here the first results obtained with NEXT-100 using low-energy calibration data from $^{83\text {m}}\textrm{Kr}$ decays, which allow mapping of the detector response in the active volume and monitoring of its stability over time. After homogenizing the light response, we achieve an energy resolution of 4.37% FWHM at 41.5 keV for $^{83\text {m}}\textrm{Kr}$ point-like energy deposits contained in a radius of 425 mm. In a fiducial region representing the operating conditions of NEXT-100 at 10 bar we obtain an improved energy resolution of 4.16% FWHM. These results are in good agreement with that obtained in NEXT-White, and an $E^{-1/2}$ extrapolation to $Q_{\beta \beta }$ yields an energy resolution close to 0.5% FWHM, well below the 1% FWHM design target.

Martínez-Lema, G. [Donostia Intl. Phys. Ctr., San ↗
DOE OSTI2020

Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network

Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. Knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to improve understanding of atmospheric sulfur, aerosol/cloud dynamics, and albedo. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 82 996 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of latitude–longitude coordinates, time of day, time of year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, and silicate. Linear regressions of DMS against the environmental parameters show that on a global-scale mixed layer depth and solar radiation are the strongest predictors of DMS. These parameters capture ~9 % and ~7 % of the raw DMS data variance, respectively. Multilinear regression can capture more of the raw data variance (~39 %) but strongly underestimates DMS in high-concentration regions. In contrast, the artificial neural network captures ~66 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in surface ocean DMS with the highest concentrations and sea-to-air fluxes in the high-latitude summertime oceans. We estimate a lower global sea-to-air DMS flux (20.12±0.43 Tg S yr –1 ) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used. Our sensitivity test results show that DMS concentration does not change unidirectionally with each of the environmental parameters, which emphasizes the interactions among these parameters. The ANN model suggests that the flux of DMS from the ocean to the atmosphere will increase with global warming. Given that larger DMS fluxes induce greater cloud albedo, this corresponds to a negative climate feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Characterizing model uncertainties in simulated coast-to-offshore wind over the northeast U.S. using multi-platform measurements from the TCAP field campaign

Numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, are widely used to provide estimates of the offshore wind energy resource owing to their large spatial coverage compared to available observations. Nevertheless, spatiotemporal distribution of model biases is highly dependent on factors including model configuration, location, and the interplay of multi-scale physical processes. Here, in this study, we focus on the characterization of model uncertainties in simulated coast-to-offshore winds over the northeast U.S., by varying sea surface temperature (SST) forcings, surface layer (SL) and planetary boundary layer (PBL) parameterizations, as well as identifying biases that may be directly passed from initial and boundary conditions. Multiple measurements, including aircraft data collected during the U.S. Department of Energy's Two-Column Aerosol Project (TCAP) experiment, are used to constrain the model results and facilitate quantitative comparisons. Our analysis indicates while SST forcing has notable impacts on simulated air temperature and moisture within PBL, the modeled winds are in general more sensitive to the choices of SL and PBL physics than to SST. The model’s forcing data not only controls the vertical dependence of wind speed errors, but also alters regional variability in wind speed’s spatial correlation. Bias comparisons between ERA5 reanalysis and ensemble simulations revealed significant similarity, particularly in wind speed biases during winter, underscoring their dependency on initial and boundary conditions. Coastal and offshore near-surface wind speed biases tend to exhibit much higher similarity in winter than in summer due to the presence of much stronger and more persistent synoptic wind conditions. This study highlights the importance of accurate atmospheric forcing and parameterization choices in improving wind forecasts and suggests the potential for extrapolating coastal wind biases to offshore locations, aiding wind energy forecasting and informing the Wind Forecast Improvement Project-3 (WFIP3).

17 WIND ENERGY↗
DOE OSTI2022

Accuracy of power spectra in dissipationless cosmological simulations

Abstract We exploit a suite of large N-body simulations (up to N = 40963) performed with abacus, of scale-free models with a range of spectral indices n, to better understand and quantify convergence of the matter power spectrum. Using self-similarity to identify converged regions, we show that the maximal wavenumber resolved at a given level of accuracy increases monotonically as a function of time. At 1 per cent level it starts at early times from a fraction of $k_\Lambda$, the Nyquist wavenumber of the initial grid, and reaches at most, if the force softening is sufficiently small, ${\sim}2{-}3 k_\Lambda$ at the very latest times we evolve to. At the $5{{\ \rm per\ cent}}$ level, accuracy extends up to wavenumbers of order $5k_\Lambda$ at late times. Expressed as a suitable function of the scale-factor, accuracy shows a very simple n-dependence, allowing a extrapolation to place conservative bounds on the accuracy of N-body simulations of non-scale-free models like LCDM. We note that deviations due to discretization in the converged range are not well modelled by shot noise, and subtracting it in fact degrades accuracy. Quantitatively our findings are broadly in line with the conservative assumptions about resolution adopted by recent studies using large cosmological simulations (e.g. Euclid Flagship) aiming to constrain the mildly non-linear regime. On the other hand, we remark that conclusions about small-scale clustering (e.g. concerning the validity of stable clustering) obtained using PS data at wavenumbers larger than a few $k_\Lambda$ may need revision in light of our convergence analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Vickers hardness prediction from machine learning methods

Abstract The search for new superhard materials is of great interest for extreme industrial applications. However, the theoretical prediction of hardness is still a challenge for the scientific community, given the difficulty of modeling plastic behavior of solids. Different hardness models have been proposed over the years. Still, they are either too complicated to use, inaccurate when extrapolating to a wide variety of solids or require coding knowledge. In this investigation, we built a successful machine learning model that implements Gradient Boosting Regressor (GBR) to predict hardness and uses the mechanical properties of a solid (bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio) as input variables. The model was trained with an experimental Vickers hardness database of 143 materials, assuring various kinds of compounds. The input properties were calculated from the theoretical elastic tensor. The Materials Project’s database was explored to search for new superhard materials, and our results are in good agreement with the experimental data available. Other alternative models to compute hardness from mechanical properties are also discussed in this work. Our results are available in a free-access easy to use online application to be further used in future studies of new materials at www.hardnesscalculator.com .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗