SEARCH · Search NASA

Results for “Statistical Modelling”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,315 records · Page 73

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Effective electric field: Quantifying the sensitivity of searches for new -odd physics with EuCl 3 ∙ 6H 2 O

Laboratory-scale precision experiments are a promising approach to searching for physics beyond the standard model. Noncentrosymmetric solids offer favorable statistical sensitivity for efforts that search for new fields, whose interactions violate the discrete parity and time-reversal symmetries. One example is the cosmic axion spin precession experiment (CASPEr), which can be sensitive to the defining interaction of the quantum chromodynamics (QCD) axion dark matter with gluons in atomic nuclei. The effective electric field is the parameter that quantifies the sensitivity of such experiments to new physics. We describe the theoretical approach to calculating the effective electric field for noncentrosymmetric sites in ionic insulating solids. We consider the specific example of the crystal, which is a particularly promising material. The optimistic estimate of the effective electric field for the isotope in this crystal is . The calculation uncertainty is estimated to be two orders of magnitude, dominated by the evaluation of the europium nuclear Schiff moment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Emergent magnetic ordering and topological frustration in quasicrystal artificial spin ices

Artificial spin ices (ASIs) are nanoscale geometrically engineered arrays of magnetic bars or islands that display magnetic frustration and provide a fertile system for modeling and testing many complex statistical mechanics theories and emergent phenomena. Aperiodic ASIs display spatially varying magnetic frustration as a result of the aperiodicity. We have explored the magnetization reversal of quasicrystalline ASIs (QC-ASIs) patterned on P2 and P3 Penrose tilings, using a statistical graph-theory approach that enables quantitative comparison between the two tilings. Our approach offers insight into the QC-ASIs. We show that the P2 tiling leads to enhanced topological frustration compared to the P3 tiling, and we elucidate the emergence of local antiferromagnetic ordering as well as disordered states in these ferromagnetic aperiodic lattices. Furthermore our method enables quantitative exploration of frustration behavior on any periodic or aperiodic lattice, thereby opening pathways to study collective behavior in various topologically frustrated systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Quantifying concordance of correlated cosmological data sets

We develop estimators of agreement and disagreement between correlated cosmological data sets. These account for data correlations when computing the significance of both tensions and excess confirmation while remaining statistically optimal. We discuss and thoroughly characterize different approaches commenting on the ones that have the best behavior in practical applications. We complement the calculation of their statistical distribution within the Gaussian model with one estimator that takes non-Gaussianities fully into account. To illustrate the use of our techniques, we apply these estimators to supernovae measurements of the distance-redshift relation, absolutely calibrated by the local distance ladder. The suite of best estimators that we discuss finds results that are in excellent agreement between estimators and find no indications of significant internal inconsistencies in this data set above the 1% probability threshold. Furthermore, this shows the robustness of local determinations of the Hubble constant to features in the distance-redshift relation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Microphysics of shock-grain interaction for inertial confinement fusion ablators in a fluid approach

Ablator materials used for inertial confinement fusion, such as high-density carbon (HDC) and beryllium, have grain structure which may lead to small-scale density nonuniformity and the generation of perturbations when the materials are shocked and compressed. Here, we use a combination of a linear theory of shock interaction with density nonuniformity [Velikovich et al., Phys. Plasmas 14, 072706 (2007)] and numerical simulations to study shock interaction with a model representation of HDC grains. While the shock-grain interaction is nonlinear, the linear theory shows some key features of the shock-grain interaction, which also hold for the (nonlinear) simulations. The postshock perturbations are made up of sonic reflections off of grain boundaries and vorticity deposition along them, with the latter dominating the perturbed energy content. The mean (per mass) postshock perturbed kinetic energy decreases with increasing grain size, but energy will be deposited at increasing spatial scale. From the perspective of the postshock perturbed energy, the detailed linear theory largely supports a proposed method [S. Davidovits et al., Phys. Plasmas 29, 112708 (2022)] for deresolving the grains (in a similar grains model) that treats the grains statistically. Finally, our simulation results highlight the influence of thermal conduction on the perturbation dynamics at grain scales.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

First Measurement of the Michel Parameter ξ ' in the τ - → μ - ν ¯ μ ν τ Decay at Belle

We report the first measurement of the Michel parameter ξ' in the $\tau$ - →μ - ⁢ $\overline{v}$ μ ⁢v $\tau$ decay with a new method proposed just recently. The measurement is based on the reconstruction of the $\tau$ - →μ -⁢ $\overline{v}$ μ v $\tau$ events with subsequent muon decay in flight in the Belle central drift chamber. The analyzed data sample of 988 fb -1 collected by the Belle detector corresponds to approximately 912×10 6 $\tau$ + ⁢$\tau$ - pairs. We measure ξ'=0.22±0.94⁢(stat)±0.42⁢(syst), which is in agreement with the standard model prediction of ξ' =1. Statistical uncertainty dominates in this study, being a limiting factor, while systematic uncertainty is well under control. Our analysis proved the practicability of this promising method and its prospects for further precise measurement in future experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Mechanical Properties of Irradiated U-10 wt. %Mo Alloy Degraded by Porosity Development

In this study, a plate-type nuclear fuel consisting of a solid monolithic foil of U-10 wt. %Mo is under development for use in the United States' high-performance research reactors. In support of developing this fuel, the fuel has been fabricated for the first time by a commercial fuel vendor and subsequently irradiated in a test reactor. This provides an opportunity to evaluate postirradiation mechanical properties of the commercially fabricated fuel. Four-point bend testing was conducted on the irradiated U-10Mo samples to generate the fuel material properties, including the modulus of elasticity and the bending strength. Although the material behaves in a brittle manner due to the accumulated porosity, a general trend of strength and modulus reduction was found as fission density increases. The data produced was evaluated using both Weibull statistics and a modulus degradation model with recommendations provided.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2019

Study of Pion Transverse Momentum Distributions in Ultra-Relativistic Heavy Ion Collisions: A USTFM Approach

The mid-rapidity (|y| < 0.5) transverse momentum distributions of identified charged pions produced in different collision systems at different centre-of-mass energies (√S{sub NN}) ranging from RHIC to LHC have been studied using our early proposed unified statistical thermal freeze-out model (USTFM). We have also studied the effect of different collision centralities on the mid-rapidity (|y| < 0.5) production of pions. Our model results are found to be in good agreement with the experimental data. Different thermal freeze-out conditions which include transverse flow, thermal freeze-out temperature, and velocity profile have been extracted by providing the best fit to the experimental data points. We have incorporated the effects of transverse as well as longitudinal flow in this analysis. The effects of heavier decay resonances are also taken into account. Moreover, the strangeness conservation criteria have also been imposed in this analysis of pion distributions. The calculated freeze-out parameters indicates that the freeze-out temperature increases with decreasing centrality, while the transverse flow velocity increases by increase in the centrality of the colliding system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Estimating Fine-Resolution Shortwave Broadband Albedo of Croplands from Harmonized Landsat and Sentinel-2 Data

Altered surface albedo due to land-cover conversions and management is a significant driver of global climate change. Albedo can be directly measured at ground stations, and remote sensing data can be used to scale-up albedo values to regional and global levels. Some previous studies have retrieved fine-resolution (10–30 m) instantaneous albedo and coarse-resolution (500–1000 m) daily mean albedo from remote sensing data, but they all required the input of Moderate Resolution Imaging Spectroradiometer (MODIS) albedo information at 500-m resolution, and none have assembled both instantaneous and daily albedo based exclusively on fine-resolution satellite data. Here, to address this issue, we compiled 387 instantaneous and 346 daily albedo records using field net radiometer measurements from the bioenergy croplands at the W. K. Kellogg Biological Station in southwest Michigan. We then connected these albedo records with a suite of variables derived from harmonized Landsat and Sentinel-2 data through two machine learning algorithms (random forest regression and extreme gradient boosting) to retrieve clear-sky instantaneous and daily shortwave broadband albedo. The performance statistics indicate reasonable accuracy of model results [root-mean-square error (RMSE)] around or below 0.03 except for snow-covered surfaces), suggesting that the retrieval of both instantaneous and daily albedo based exclusively on fine-resolution satellite data is promising. To facilitate the use of fine-resolution albedo products at the global level, future efforts need to include more albedo records of diverse surface cover types, as well as to accurately model daily albedo for cloudy days to address the “clear-sky bias.”

Harmonized Landsat and Sentinel-2↗

DOE OSTI2019

Update to the Long-Term Monitoring System Design Report for USDOE/NNSA Pantex Plant Groundwater Remedial Action Progress

This report presents proposed modifications to the long-term groundwater monitoring well network that was originally developed in 2009 and updated in 2014. The original network was developed using statistical methods, fate and transport modeling, and site-specific knowledge for the evaluation of response actions (corrective/remedial actions) for Pantex Plant and monitoring uncertainties near source areas. The 2014 update was based on an evaluation of perched aquifer monitoring system and data collected during the first Five-Year Review, as well as updated expected conditions based on changing aquifer. Similarly, this update proposes modifications based on an evaluation of the perched aquifer monitoring system during the second Five-Year Review along with updated expected conditions based on changing aquifer conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Total Measurement Uncertainty in Neutron Coincidence Multiplicity Analysis

Neutron multiplicity counting is the most commonly used nondestructive assay technique for determining the plutonium mass within containers of scrap PuO 2 or mixed oxide (MOX). In multiplicity analysis, the 240 Pu eff mass, leakage multiplication, and alpha ratio (the ratio of [α, n]-to-spontaneous fission neutron production) are the three primary unknown sample properties. They must be determined simultaneously. To solve for these three unknowns in a multiplicity assay, three measured values are needed: the singles, doubles, and triples neutron count rates. While the analysis is limited to solving for three unknowns, there are many additional factors that impact the observed count rates and contribute to the measurement uncertainty. In this study we investigate the various uncertainty contributors for the multiplicity analysis through a combination of traditional uncertainty propagation techniques supplemented by Monte Carlo simulations to address the dependences not explicitly expressed by the point source model. Uncertainties arising from counting statistics, calibration parameters, calibration method, nuclear data, and various material characteristics (isotopic abundances, chemical form, density, and impurities) are considered. A Total Measurement Uncertainty (TMU) estimate is then developed from these uncertainty contributors. This study is confined to multiplicity analysis of items commonly encountered in international safeguards applications. That is, the study focused on Pu oxides and MOX materials for the masses ranging up to 4000 grams total Pu. Multiplicity measurements were simulated using MCNP V6 based on the Plutonium Scrap Multiplicity Counter (PSMC), Epithermal Multiplicity Counter (ENMC), Pyrochemical Multiplicity Counter, and Large Epithermal Multiplicity Counter (LEMC) for this study; however, this report focuses on the parameterization of the uncertainties for the PSMC. The performance differences between the PSMC and the other multiplicity counting systems are relatively small, primarily manifesting in the impact on measurement precision so that the evaluation developed for the PSMC can be applied to the other multiplicity counting systems. Finally an analysis tool, the Multiplicity TMU Estimator, was developed from this study to serve as an aid for evaluation of the total measurement uncertainty of multiplicity assay results obtained from the commonly used INCC acquisition and analysis software.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Quantitative Characterization of Hyper-Local Atmospheric Greenhouse Gas Sources

Atmospheric greenhouse gas (GHG) emissions are often characterized using stationary, tower-based sensors. Ground based sensors reside in the turbulent boundary layer and are subject to intense concentration impulses from hyper-local (<100m) point sources of emissions. These high frequency spikes are often filtered out in broader emission flux studies, losing valuable information about how hyper-local sources influence receptors. In this study, we investigated how empirical atmospheric data can be used to locate and quantify a concurrently measured hyper-local point source in a dense urban setting. An eddy covariance style tower and a low-cost sensor tower were deployed in various locations around an urban, hyper-local CO2/CH4 emissions source (a continuously measured restaurant exhaust vent). A model using different processing and statistical techniques was built to examine the most effective procedures for source isolation, directional location, and emission quantification. Using excess concentrations above a minimum baseline, we identify the source using bivariate polar plots and quantify the relationship between source size, receptor distance, and statistical proxies. Furthermore, we find that varying statistical thresholds allows for identification of less influential sources which are drowned out by larger or closer sources. Finally, we show that large sources can be effectively characterized using low-cost sensors, a valuable outcome informing how networks for monitoring larger areas could be implemented. This work may provide a basis for source identification and monitoring protocols for networks that feature sensors influenced by hyper-local point sources, subject to site-specific assumptions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Multivariate Analysis as a Tool for Validating Tester Matching

A method of applying Principal Component Analysis, Soft Independent Modeling of Class Analysis, and statistical analysis is described that can be applied to many types of testers to ascertain how well matched the performance of the testers in the analysis are to one another or how well matched a tester is to itself at a later time. This method is most useful for situations for which the same units have not been run across the testers being analyzed for matched performance.

Multari, Rosalie A [Sandia National Laboratories (↗

DOE OSTI2026

Symposium MT02: Statistical Mechanics-Based Computational Tools for the Study of Phase Transformation in Complex Materials (Final Report)

Symposium MT02 brought together a diverse and interdisciplinary community of scientists specializing in Statistical Mechanics-based computational modeling to investigate phase transformations in materials exhibiting complex disordered structures. As the demand for materials with extreme performance metrics grows—from aerospace components to next-generation optical fibers—the ability to predict microstructural evolution under non-equilibrium conditions has become paramount. The primary goal of this symposium was to identify, evaluate, and discuss advanced computational tools capable of designing precise manufacturing conditions to tailor material properties efficiently. By fostering a dialogue between computational theorists and experimentalists, the symposium sought to establish new protocols for predicting how processing history—such as cooling rates or strain paths—dictates the final microstructure.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Towards a Counting Point Detector for Nanosecond Coherent X-ray Science

We present the technical realization of a high-speed hard X-ray single-photon counting-detection scheme based on a commercial avalanche silicon photodiode and high-speed oscilloscope. The development is motivated by the need to perform pulse-resolved photon-correlation and pump-probe studies at synchrotron sources with densely packed pulse patterns that result in high repetition rate pulses on the order of hundreds of MHz. Commissioning experiments are performed at the 1C PAL-KRISS beamline at PLS-II of South Korea operating at a burst mode maximum repetition rate of 500 MHz. In such a high count-rate measurement, detector dead-time can lead to a distortion of counting statistics. We are able to model the counting behavior of our detector under these conditions with a detector dead-time comparable to time between X-ray pulses, implying that nanosecond X-ray photon correlation spectroscopy should be possible at diffraction-limited light sources.

36 MATERIALS SCIENCE↗

DOE OSTI2019

An FEM-MLMC algorithm for a moving shutter diffraction in time stochastic model

We consider a moving shutter and non-deterministic generalization of the diffraction in time model introduced by Moshinsky several decades ago to study a class of quantum transients. We first develop a moving-mesh finite element method (FEM) to simulate the determisitic version of the model. We then apply the FEM and multilevel Monte Carlo (MLMC) algorithm to the stochastic moving-domain model for simulation of approximate statistical moments of the density profile of the stochastic transients.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Fast and accurate learned multiresolution dynamical downscaling for precipitation

Abstract. This study develops a neural-network-based approach for emulating high-resolution modeled precipitation data with comparable statistical properties but at greatly reduced computational cost. The key idea is to use combination of low- and high-resolution simulations (that differ not only in spatial resolution but also in geospatial patterns) to train a neural network to map from the former to the latter. Specifically, we define two types of CNNs, one that stacks variables directly and one that encodes each variable before stacking, and we train each CNN type both with a conventional loss function, such as mean square error (MSE), and with a conditional generative adversarial network (CGAN), for a total of four CNN variants. We compare the four new CNN-derived high-resolution precipitation results with precipitation generated from original high-resolution simulations, a bilinear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique. Results show that the SR technique produces results similar to those of the bilinear interpolator with smoother spatial and temporal distributions and smaller data variabilities and extremes than the original high-resolution simulations. While the new CNNs trained by MSE generate better results over some regions than the interpolator and SR technique do, their predictions are still biased from the original high-resolution simulations. The CNNs trained by CGAN generate more realistic and physically reasonable results, better capturing not only data variability in time and space but also extremes such as intense and long-lasting storms. The new proposed CNN-based downscaling approach can downscale precipitation from 50 to 12 km in 14 min for 30 years once the network is trained (training takes 4 h using 1 GPU), while the conventional dynamical downscaling would take 1 month using 600 CPU cores to generate simulations at the resolution of 12 km over the contiguous United States.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

DSGAN

This study develops a neural network-based approach for emulating high-resolution modeled precipitation data with comparable statistical properties but at greatly reduced computational cost. The key idea is to use combination of low-and high- resolution simulations to train a neural network to map from the former to the latter. Specifically, we define two types of CNNs, one that stacks variables directly and one that encodes each variable before stacking, and we train each CNN type both with a conventional loss function, such as mean square error (MSE), and with a conditional generative adversarial network (CGAN), for a total of four CNN variants. We compare the four new CNN-derived high-resolution precipitation results with precipitation generated from original high resolution simulations, a bilinear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique. Results show that the SR technique produces results similar to those of the bilinear interpolator with smoother spatial and temporal distributions and smaller data variabilities and extremes than the original high resolution simulations. While the new CNNs trained by MSE generate better results over some regions than the interpolator and SR technique do, their predictions are still not as close as the original high resolution simulations. The CNNs trained by CGAN generate more realistic and physically reasonable results, better capturing not only data variability in time and space but also extremes such as intense and long-lasting storms. The new proposed CNN-based downscaling approach can downscale precipitation from 50~km to 12~km in 14~min for 30~years

LIU, ZHENGCHUN↗

DOE OSTI2020

Assessing Current and Future Infrastructure Hazards

Project Objective Execute intelligent analytics via an advanced analytical framework, to assess the current state of offshore infrastructure, evaluate infrastructure life, and identify technologies to reduce infrastructure hazards, costs, and extend infrastructure life. Approach & Results thus Far• Build comprehensive dataset• Perform data-driven analytics to evaluate infrastructure integrity 1. Remaining lifespan 2. Likelihood of future risk• Apply data-driven advanced spatial, statistical, and Machine Learning (ML) models to quantify existing infrastructure integrity• Release data and models through a smart, online platform hosted by Energy Data eXchange (EDX)

Romeo, Lucy F.↗