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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Validation of the DESI-DR1 3x2-pt analysis: scale cut and shear ratio tests

Combined survey analyses of galaxy clustering and weak gravitational lensing (3x2-pt studies) will allow new and accurate tests of the standard cosmological model. However, careful validation is necessary to ensure that these cosmological constraints are not biased by uncertainties associated with the modelling of astrophysical or systematic effects. In this study we validate the combined 3x2-pt analysis of the Dark Energy Spectroscopic Instrument Data Release 1 (DESI-DR1) spectroscopic galaxy clustering and overlapping weak lensing datasets from the Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES), and the Hyper-Suprime-Cam Survey (HSC). By propagating the modelling uncertainties associated with the non-linear matter power spectrum, non-linear galaxy bias and baryon feedback, we design scale cuts to ensure that measurements of the matter density and the amplitude of the matter power spectrum are biased by less than 30% of the statistical error. We also test the internal consistency of the data and weak lensing systematics by performing new measurements of the lensing shear ratio. We demonstrate that the DESI-DR1 shear ratios can be successfully fit by the same model used to describe cosmic shear correlations, and analyse the additional information that can be extracted about the source redshift distributions and intrinsic alignment parameters. This study serves as crucial preparation for the upcoming cosmological parameter analysis of these datasets.

Emas, N. [Swinburne U., Ctr. Astrophys. Supercompu↗
DOE OSTI2020

Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in-situ and remote sensing datasets

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Reconciling Chord Length Distributions and Area Distributions for Fields of Fractal Cumulus Clouds

While the total cover of broken cloud fields can in principle be obtained from one-dimensional measurements, the cloud size distribution normally differs between two-dimensional (area) and one-dimensional retrieval (chord length) methods. In this study, we use output from high-resolution Large Eddy Simulations to generate a transfer function between the two. We retrieve chord lengths and areas for many clouds, and plot the one as a function of the other, and vice versa. We find that the cloud area distribution conditional on the chord length behaves like a gamma distribution with well-behaved parameters, with a mean μ=1.1L and a shape parameter β=L−0.645. Using this information, we are able to generate a transfer function that can adjust the chord length distribution so that it comes much closer to the cloud area distribution. Our transfer function improves the error in predicting the mean cloud size, and is performs without strong biases for smaller sample sizes. However, we find that the method is still has difficulties in accurately predicting the frequency of occurrence of the largest cloud sizes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Measurement of Device Parameters Using Image Recovery Techniques in Large-Scale IC Devices

Devices that respond to radiation on a cell level will produce histograms showing the relative frequency of cell damage as a function of damage. The measured distribution is the convolution of distributions from radiation responses, measurement noise, and manufacturing parameters. A method of extracting device characteristics and parameters from measured distributions via mathematical and image subtraction techniques is described.

radiation↗
DOE OSTI2021

Simultaneous inference of the compressibility and inelastic response of tantalum under extreme loading

We study the deformation of tantalum under extreme loading conditions. Experimental velocity data are drawn from both ramp loading experiments on Sandia’s Z-machine and gas gun compression experiments. The drive conditions enable the study of materials under pressures greater than 100 GPa. We provide a detailed forward model of the experiments including a model of the magnetic drive for the Z-machine. Utilizing these experiments, we simultaneously infer several different types of physically motivated parameters describing equation of state, plasticity, and anelasticity via the computational device of Bayesian model calibration. Characteristics of the resulting calculated posterior distributions illustrate relationships among the parameters of interest via the degree of cross correlation. The calibrated velocity traces display good agreement with the experiments up to experimental uncertainty as well as improvement over previous calibrations. Examining the Z-shots and gun-shots together and separately reveals a trade-off between accuracy and transferability across different experimental conditions. Implications for model calibration, limitations from model form, and suggestions for improvements are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Pair momentum dependence of a tilted source in heavy-ion collisions

In noncentral heavy-ion collisions, the particle-emitting source can be tilted away from the beam direction, an effect that becomes particularly significant at collision energies of a few GeV and lower. This phenomenon, manifest itself in many observables such as directed flow, polarization, and vorticity, is therefore important to investigate. Here, in this paper, we study the consistency between the tilt extracted directly from the freeze-out distribution of pions and the tilt parameter obtained using the azimuthally sensitive femtoscopy (asHBT) method. Using the Ultrarelativistic Quantum Molecular Dynamics model, we demonstrate a strong dependence of the tilt parameter extracted with asHBT on the momentum of the particle pair. Considering the experimental challenges in accessing low particle momenta—where the tilt parameter extracted with asHBT closely matches the tilt of the freeze-out distribution of pions—we propose an exponential extrapolation method to obtain the tilt of the entire freeze-out distribution. This approach aims to enhance the accuracy of experimental measurements of tilt in noncentral heavy-ion collisions.

particle correlations & fluctuations↗
NASA NTRS1974

Calculation of the twist distribution of wings designed for cruise at transonic speeds

The use of linear theory in calculating the twist distribution of a wing designed for cruise at supercritical speeds is justified on the basis of the transonic equivalence rule. A modified version of Multhopp's subsonic lifting-surface theory was used to calculate the twist distribution. The lifting-surface theory is compared with both slender wing theory and experimental results. A study was also made of the effect of wing sweep on the twist distribution required to maintain an elliptic span load at cruise conditions. The important parameters used in establishing this twist distribution are identified.

Mann, M. J.↗
DOE OSTI2025

Uncertainty quantification and optimization of precipitating hydrometeor parameters for winter precipitation in a cloud microphysics scheme

The precipitating hydrometeor parameters used in cloud microphysics schemes carry inherent uncertainties. The quantification of these uncertainties, together with parameter optimization, can significantly improve precipitation forecasts. This study investigates the effects of 13 parameters in the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme, which define the hydrometeor characteristics such as fall velocity–diameter and mass–diameter relationships, as well as the shape parameter of the drop size distribution for precipitating particles such as rain, snow, and graupel on simulated winter precipitation. A comparison between the model's pre-defined parameters and observations from the International Collaborative Experiments for the PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign reveals that the fall velocity–diameter relationship for rain, the mass–diameter relationships for snow and graupel, and the shape parameters for all precipitating particles in the WDM6 scheme deviate from the median values observed by the two-dimensional video disdrometer (2DVD). To quantify parameter sensitivities, a perturbed parameter ensemble (PPE) of 256 simulations was conducted within parameter ranges constrained by 2DVD observations for three winter precipitation cases. Bayesian optimization was then applied to identify parameter sets that minimized the root mean square error (RMSE) for each case, achieving reductions of up to 30.2 %. These results demonstrate that ensemble-based uncertainty quantification and parameter optimization can help identify key parameters and provide a pathway to improving precipitation simulation performance. In addition, measurement sites can be strategically selected based on regions that show high sensitivity to variations in hydrometeor characteristic parameters.

Bayesian optimization↗
NASA NTRS1985

An APL program for the distribution of energy deposition by charged particles passing through thin absorbers

An APL program which numerically evaluates the probability density function (PDF) for the energy deposited in a thin absorber by a charged particle is proposed, with application to the construction, pointing, and control of spacecraft. With this program, the PDF of the restricted energy loss distribution of Watts (1973) is derived, and Vavilov's (1957) distribution is obtained by proper parameter selection. The method is demonstrated with the example of the effect of charged particle induced radiation on the Hubble Space Telescope (HST) pointing accuracy. A Monte Carlo study simulates the photon noise caused by charged particles passing through the photomultiplier tube window, and the stochastic variation of energy loss is introduced into the simulation by generating random energy losses from a power law distribution. The program eliminates annoying loop procedures, and model parameter sensitivity can be studied using the graphical output.

Howell, L. W.↗
DOE OSTI2026

A Tutorial on Bayesian analysis of linear shock compression data

Gas gun and other shock compression experiments often produce shock wave velocity measurements that are linearly associated with particle velocity. Traditionally, this empirical relationship is quantified with a single Hugoniot curve that is estimated using least squares regression. However, for downstream modeling and simulation tasks, it is often more useful to have multiple Hugoniot curves in the pressure–volume plane that are consistent with the data. We employ Bayesian uncertainty quantification methods as a framework for propagating measurement uncertainty through to model parameters and predictions. Specifically, this Tutorial shows how to sample multiple Hugoniot curves in the pressure–volume plane that are consistent with the shock wave-particle velocity measurements in a two-step Bayesian approach. First, we obtain an analytical expression for the posterior distribution of the linear model parameters using Bayesian linear regression. Second, we propagate samples from the posterior distribution through the Rankine–Hugoniot equations to yield Hugoniot curves in the pressure–volume plane. The procedure is demonstrated with publicly available data on argon, copper, and nickel, and compared against bootstrapping and linear regression. The Bayesian procedure is shown to be interpretable, computationally inexpensive, and less sensitive than an alternative bootstrapping approach to the removal of the point in the copper dataset that has the largest particle velocity. As a Tutorial on Bayesian methodology for the shock compression community, we provide several derivations and explanations that make this paper self-contained, and make all code and data available at github.com/llnl/BALSCD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1979

Determination of the ground albedo and the index of absorption of atmospheric particulates by remote sensing. I - Theory

A statistical technique is developed for inferring the optimum values of the ground albedo and the effective imaginary term of the complex refractive index of atmospheric particulates. The procedure compares measurements of the ratio of the hemispheric diffuse to directly transmitted solar flux density at the earth's surface with radiative transfer computations of the same as suggested by Herman et al. (1975). A detailed study is presented which shows the extent to which the ratio of diffuse to direct solar radiation is sensitive to many of the radiative transfer parameters. Results indicate that the optical depth and size distribution of atmospheric aerosol particles are the two parameters which uniquely specify the radiation field to the point where ground albedo and index of absorption can be inferred. Varying the real part of the complex refractive index of atmospheric particulates as well as their vertical distribution is found to have a negligible effect on the diffuse-direct ratio. The statistical procedure utilizes a semi-analytic gradient search method from least-squares theory and includes a detailed error analysis.

King, M. D.↗
NASA NTRS1988

Boundary layer development as a function of chamber pressure in the NASA Lewis 1030:1 area ratio rocket nozzle

Through the use of theoretical predictions of fluid properties and experimental heat transfer and thrust measurements, the zones of laminar, transitional, and turbulent boundary layer flow were defined for the NASA Lewis 1030:1 area ratio rocket nozzle. Tests were performed on the nozzle at chamber pressures from 350 to 100 psia. For these conditions, the throat diameter Reynolds numbers varied from 300,000 to 1 million. The propellants used were gaseous hydrogen and gaseous oxygen. Thrust measurements and nozzle outer wall temperature measurements were taken during the 3-sec test runs. Comparison of experimental heat transfer and thrust data with the corresponding predictions from the Two-Dimensional Kinetics (TDK) nozzle analysis program indicated laminar flow in the nozzle at a throat diameter Reynolds number of 320,000 or chamber pressure of 360 psia. Comparison of experimental and predicted heat transfer data indicated transitional flow up to and including a chamber pressure of 1000 psia. Predicted values of the axisymmetric acceleration parameter within the convergent and divergent nozzle were consistent with the above results. Based upon an extrapolation of the heat transfer data and predicted distributions of the axisymmetric acceleration parameter, transitional flow was predicted up to a throat diameter Reynolds number of 220,000 or 2600-psia chamber pressure. Above 2600-psia chamber pressure, fully developed turbulent flow was predicted.

Smith, Tamara A.↗
NASA NTRS1988

Boundary layer development as a function of chamber pressure in the NASA Lewis 1030:1 area ratio rocket nozzle

Through the use of theoretical predictions of fluid properties and experimental heat transfer and thrust measurements, the zones of laminar, transitional, and turbulent boundary layer flow were defined for the NASA Lewis 1039:1 area ratio rocket nozzle. Tests were performed on the nozzle at chamber pressures from 350 to 100 psia. For these conditions, the throat diameter Reynolds numbers varied from 300,000 to 1 million. The propellants used were gaseous hydrogen and gaseous oxygen. Thrust measurements and nozzle outer wall temperature measurements were taken during the 3-sec test runs. Comparison of experimental heat transfer and thrust data with the corresponding predictions from the Two-Dimensional Kinetics (TDK) nozzle analysis program indicated laminar flow in the nozzle at a throat diameter Reynolds number of 320,000 or chamber pressure of 360 psia. Comparison of experimental and predicted heat transfer data indicated transitional flow up to and including a chamber pressure of 1000 psia. Predicted values of the axisymmetric acceleration parameter within the convergent and divergent nozzle were consistent with the above results. Based upon an extrapolation of the heat transfer data and predicted distributions of the axisymmetric acceleration parameter, transitional flow was predicted up to a throat diameter Reynolds number of 220,000 or 2600-psia chamber pressure. Above 2600-psia chamber pressure, fully developed turbulent flow was predicted.

Smith, Tamara A.↗
NASA NTRS2023

Drought-Tolerant Succulent Plants as an Alternative Crop Under Future Global Warming Scenarios in Sub-Saharan Africa

Globally, we are facing an emerging climate crisis, with impacts to be notably felt in semiarid regions across the world. Cultivation of drought-adapted succulent plants has been suggested as a nature-based solution that could: (i) reduce land degradation, (ii) increase agricultural diversification and provide both economic and environmentally sustainable income through derived bioproducts and bioenergy, (iii) help mitigate atmospheric CO 2 emissions and (iv) increase soil sequestration of CO 2 . Identifying where succulents can grow and thrive is an important prerequisite for the advent of a sustainable alternative ‘bioeconomy’. Here, we first explore the viability of succulent cultivation in Africa under future climate projections to 2100 using species distribution modelling to identify climatic parameters of greatest importance and regions of environmental suitability. Minimum temperatures and temperature variability are shown to be key controls in defining the theoretical distribution of three succulent species explored, and under both current and future SSP5 8.5 projections, the conditions required for the growth of at least one of the species are met in most parts of sub-Saharan Africa. These results are supplemented with an analysis of potentially available land for alternative succulent crop cultivation. In total, up to 1.5 billion ha could be considered ecophysiologically suitable and available for succulent cultivation due to projected declines in rangeland biomass and yields of traditional crops. These findings may serve to highlight new opportunities for farmers, governments and key stakeholders in the agriculture and energy sectors to invest in sustainable bioeconomic alternatives that deliver on environmental, social and economic goals.

agriculture↗
DOE OSTI2024

Angular Distribution of Dimuons from Drell-Yan Production in p+Fe Interactions at 120 GeV Beam Energy

In the E906/SeaQuest Fermilab experiment, we report a measurement of the angular distributions by measuring the angular parameters $\lambda$, $\mu$, and $\nu$ of Drell-Yan dimuons produced using a 120 GeV proton beam incident on an iron target. The angular distribution in the naive Drell-Yan model does not show any $\cos2\phi$ dependency, where $\phi$ denotes the azimuthal angle of dimuons in the Collins-Soper frame. However, pion-induced Drell-Yan experiments, such as NA10 and E615, have observed a significant dependence on $\cos2\phi$. The Boer–Mulders function, a transverse momentum-dependent distribution function, represents the correlation between the transverse spin and the transverse momentum of the quark. A non-zero Boer-Mulders function or an improved higher-order Drell-Yan model considering QCD effects can produce a $\cos 2\phi$ modulation in the Drell-Yan angular distribution. To measure the angular distributions, we have used an event mixing method to construct the combin atorial background, which was then subtracted from the data to isolate the Drell-Yan signal. Following this, we corrected the detector, trigger, and reconstruction efficiencies using a doubly-iterative Bayesian Unfolding method. This iterative unfolding technique improves the response matrix based on the results of the previous unfolding step, ensuring robust convergence without exaggeration of uncertainties. The angular distributions of the dimuons were measured over the invariant mass range $5.0 < M_{\mu^+ \mu^-} < 8.0$ $GeV/c^2$, with dimuon transverse momentum $P_T < 2$ GeV/c and Feynman-x $-0.18 < x_F < 0.9$. The measured angular distributions are then compared with the QCD calculations for $p + \text{Fe}$ interactions, and proton-induced angular distribution measurements from other experiments. We have observed weak $\cos 2\phi$ modulations as a function of $P_T$. For $P_T > 1.0 \, \text{GeV}/c$, the predicted NNLO perturbative QCD value of $\nu$ is larger than what we have me asured at E906/SeaQuest. Moreover, we have not observed a strong dependence of $\nu$ on the kinematic variables, such as dimuon mass $M_{\mu^+ \mu^-}$ and Bjorken-$x$. The spin alignment of the virtual photon, $\lambda$, measured from the SeaQuest Drell-Yan $p+\text{Fe}$ data, is found to be strongly dependent on $P_T$, decreasing as $P_T$ increases. $\lambda$ also holds to the upper bound condition $\lambda < 1.0$ within the statistical uncertainty, showing a trend similar to that predicted by NNLO perturbative QCD. However, for $1.0 < P_T < 2.0 \, \text{GeV}/c$, the extracted $\lambda$ value from SeaQuest is smaller than that predicted by perturbative QCD at NNLO.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Conditional diffusion machine-learning framework for mapping valence electron distribution from convergent beam electron diffraction

Quantitative convergent beam electron diffraction (CBED) enables determination of aspherical valence electron distributions through refinement of low-order structure factors, which are highly sensitive to chemical bonding and charge density variations. However, conventional quantitative CBED (QCBED) requires solving a highly nonlinear inverse problem with many coupled parameters, and computationally intensive dynamical diffraction calculations, making it time-consuming and difficult to apply to complex systems. More broadly, reconstructing charge density and orbital electron distribution from diffraction data has long been a central challenge in both x-ray and electron crystallography. Here, in this study, we introduce an artificial-intelligence (AI)-based framework that replaces traditional refinement with a data-driven inverse solver. Using a large synthetic CBED dataset generated by Bloch-wave simulations, we train a conditional diffusion model to directly infer crystal structural parameters and multipole density formalism parameters, and hence valence electron distributions, from CBED patterns alone. By learning from forward simulations across realistic parameter space, the model effectively solves the inverse problem. Compared with direct regression approaches, the diffusion-based framework provides posterior parameter distributions for rigorous uncertainty quantification while preserving quantitative fidelity and reducing analysis time by orders of magnitude. By eliminating the need for external single-crystal x-ray diffraction data and complex nonlinear refinement, this approach enables practical, high-throughput, and in situ quantitative CBED, enabling real-time mapping of valence electron distributions and their correlation with functional responses in quantum and energy materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1980

Design and breadboard evaluation of the SPS reference phase control system concept

The total breadboard system includes one pilot transmitter, one pilot receiver, nine phase distribution units, and two power transponders. With this complement of equipment, segments of a typical phase distribution system can be assembled to facilitate the evaluation of significant system parameters. The achievable accuracy of a large phase distribution system, the sensitivity of the system to parameter variations, and the limitations of commercially available components in such applications were determined.

Hopkins, P. M.↗
DOE OSTI2025

Accelerating Hamiltonian Monte Carlo for Bayesian inference in neural networks and neural operators

Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s parameter space and the non-convexity of their posterior distributions. Therefore, various approximation techniques, such as variational inference (VI) or stochastic gradient MCMC, are often employed to infer the posterior distribution of the network parameters. Such approximations introduce inaccuracies in the inferred distributions, resulting in unreliable uncertainty estimates. In this work, we propose a hybrid approach that combines inexpensive VI and accurate HMC methods to efficiently and accurately quantify uncertainties in neural networks and neural operators. The proposed approach leverages an initial VI training on the full network. We examine the influence of individual parameters on the prediction uncertainty, which shows that a large proportion of the parameters do not contribute substantially to uncertainty in the network predictions. This information is then used to significantly reduce the dimension of the parameter space, and HMC is performed only for the subset of network parameters that strongly influence prediction uncertainties. This yields a framework for accelerating the full batch HMC for posterior inference in neural networks. We demonstrate the efficiency and accuracy of the proposed framework on deep neural networks and operator networks, showing that inference can be performed for large networks with tens to hundreds of thousands of parameters. Finally, we show that this method can effectively learn surrogates for complex physical systems by modeling the operator that maps from upstream conditions to wall-pressure data on a cone in hypersonic flow.

Bayesian inference↗