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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 OSTI2020

Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models

Monitoring the ecological impacts of environmental pollution and the effectiveness of remediation efforts requires identifying relationships between contaminants and the disruption of biological processes in populations, communities, or ecosystems. Wildlife are useful bioindicators, but traditional comparative experimental approaches rely on a staunch and typically unverifiable assumption that, in the absence of contaminants, reference and contaminated sites would support the same densities of bioindicators, thereby inferring direct causation from indirect data. We demonstrate the utility of spatial capture-recapture (SCR) models for overcoming these issues, testing if community density of common small mammal bioindicators was directly influenced by soil chemical concentrations. By modeling density as an inhomogeneous Poisson point process, we found evidence for an inverse spatial relationship between Peromyscus density and soil mercury concentrations, but not other chemicals, such as polychlorinated biphenyls, at a site formerly occupied by a nuclear reactor. Although the coefficient point estimate supported Peromyscus density being lower where mercury concentrations were higher (β = –0.44), the 95% confidence interval overlapped zero, suggesting no effect was also compatible with our data. Estimated density from the most parsimonious model (2.88 mice/ha; 95% CI = 1.63–5.08), which did not support a density-chemical relationship, was within the range of reported densities for Peromyscus that did not inhabit contaminated sites elsewhere. Environmental pollution remains a global threat to biodiversity and ecosystem and human health, and our study provides an illustrative example of the utility of SCR models for investigating the effects that chemicals may have on wildlife bioindicator populations and communities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Analyzing line-of-sight selection biases in galaxy-scale strong lensing with external convergence and shear

The upcoming Vera Rubin Observatory Legacy Survey of Space and Time (LSST) will dramatically increase the number of strong gravitational lensing systems, requiring precise modeling of line-of-sight (LOS) effects to mitigate biases in lensing observations and cosmological inferences. We develop a method to construct joint distributions of external convergence (κ ext ) and shear (γ ext ) for strong lensing LOS by aggregating large-scale structure simulations with high-resolution halo renderings and non-linear correction. Our approach captures both smooth background matter and perturbations from halos, enabling accurate modeling of LOS effects. Here, we apply non-linear LOS corrections to κ ext and γ ext that address the non-additive lensing effects caused by objects along the LOS in strong lensing. We find that, with a minimum image separation of 1.0'', non-linear LOS correction due to the presence of a dominant deflector slightly increases the ratio of quadruple to double lenses; this non-linear LOS correction also introduces systematic biases of ∼ 0.1% for galaxy-AGN lens in the inferred Hubble constant (H 0 ) if not accounted for. We also observe a 0.66% bias for galaxy-galaxy lenses on H 0 , and even larger biases 1.02% for galaxy-AGN systems if LOS effects are not accounted for. These results highlight the importance of LOS for precision cosmology. The publicly available code and datasets provide tools for incorporating LOS effects in future analyses.

Hubble constant↗
DOE OSTI2020

Modeling regional precipitation over the Indus River basin of Pakistan using statistical downscaling

Complex processes govern spatiotemporal distribution of precipitation within the high-mountainous headwater regions (commonly known as the upper Indus basin (UIB)), of the Indus River basin of Pakistan. Reliable precipitation simulations particularly over the UIB present a major scientific challenge due to regional complexity and inadequate observational coverage. Here, we present a statistical downscaling approach to model observed precipitation of the entire Indus basin, with a focus on UIB within available data constraints. Taking advantage of recent high altitude (HA) observatories, we perform precipitation regionalization using K-means cluster analysis to demonstrate effectiveness of low-altitude stations to provide useful precipitation inferences over more uncertain and hydrologically important HA of the UIB. We further employ generalized linear models (GLM) with gamma and Tweedie distributions to identify major dynamic and thermodynamic drivers from a reanalysis dataset within a robust cross-validation framework that explain observed spatiotemporal precipitation patterns across the Indus basin. Final statistical models demonstrate higher predictability to resolve precipitation variability over wetter southern Himalayans and different lower Indus regions, by mainly using different dynamic predictors. The modeling framework also shows an adequate performance over more complex and uncertain trans-Himalayans and the northwestern regions of the UIB, particularly during the seasons dominated by the westerly circulations. However, the cryosphere-dominated trans-Himalayan regions, which largely govern the basin hydrology, require relatively complex models that contain dynamic and thermodynamic circulations. Furthermore, we also analyzed relevant atmospheric circulations during precipitation anomalies over the UIB, to evaluate physical consistency of the statistical models, as an additional measure of reliability. Overall, our results suggest that such circulation-based statistical downscaling has the potential to improve our understanding towards distinct features of the regional-scale precipitation across the upper and lower Indus basin. Additionally, such understanding should help to assess the response of this complex, data-scarce, and climate-sensitive river basin amid future climatic changes, to serve communal and scientific interests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Thermal Experiments for Fractured Rock Characterization: Theoretical Analysis and Inverse Modeling

Abstract Field‐scale properties of fractured rocks play a crucial role in many subsurface applications, yet methodologies for identification of the statistical parameters of a discrete fracture network (DFN) are scarce. We present an inversion technique to infer two such parameters, fracture density and fractal dimension, from cross‐borehole thermal experiments data. It is based on a particle‐based heat‐transfer model, whose evaluation is accelerated with a deep neural network (DNN) surrogate that is integrated into a grid search. The DNN is trained on a small number of the heat‐transfer model runs and predicts the cumulative density function of the thermal field. The latter is used to compute fine posterior distributions of the (to be estimated) parameters. Our synthetic experiments reveal that fracture density is well constrained by data, while fractal dimension is harder to determine. Adding nonuniform prior information related to the DFN connectivity improves the inference of this parameter.

Zhou, Zitong↗
DOE OSTI2020

Hydraulic characterization of a fault zone from fracture distribution

A quantitative assessment of how faults control the migration of geofluids is critical in many areas of geosciences. Here we integrated geological fieldwork, quantitative analysis of the fractures distribution and numerical modeling to build a geometrical representation of a fault zone and to characterize its hydraulic properties. Our target is a fault located in the Majella Mountain (Italy). We collected 21 scan lines across the fault profile in order to characterize its architecture. The numerical modeling of the fracture network of the damage zones and their hydraulic parameters was performed using both commercial (Move ® ) and open source software (dfnWorks and PFLOTRAN). Move ® was used to build a representative model of the fault zone using fracture spacing as a proxy, and to model the hydraulic parameters of the different fault domains. dfnWorks and PFLOTRAN were employed to infer the hydraulic parameters of the damage zones of the fault and then upscale these properties to an equivalent continuum domain, suitable for fluid flow simulations through the whole fault zone. Our findings show how even in a relatively small area it is possible to describe changes in terms of hydraulic properties of a fault zone and to build models capable to represent these variations.

58 GEOSCIENCES↗
DOE OSTI2016

Measurement of effective sheath width around the cutoff probe based on electromagnetic simulation

We inferred the effective sheath width using the cutoff probe and incorporating a full-wave three-dimensional electromagnetic (EM) simulation. The EM simulation reproduced the experimentally obtained plasma-sheath resonance (PSR) on the microwave transmission (S{sub 21}) spectrum well. The PSR frequency has a one-to-one correspondence with the width of the vacuum layer assumed to be the effective sheath in the EM simulation model. The sheath width was estimated by matching the S{sub 21} spectra of the experiment and the EM simulation for different widths of the sheath. We found that the inferred sheath widths quantitatively and qualitatively agree with the sheath width measured by incorporating an equivalent circuit model. These results demonstrate the excellent potential of the cutoff probe for inferring the effective sheath width from its experimental spectrum data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Radiochemical transport analysis of gamma spectroscopic data to support estimation of molten salt reactor off-gas inventories

This work introduces a novel application of radiochronometry to estimate nuclide inventories in molten salt reactor off-gas systems based on gamma spectroscopic data from the Molten Salt Reactor Experiment. By analyzing isotopic, isobaric, and isomeric activity ratios, key depletion model parameters related to species transport within the reactor system could be inferred. The findings demonstrate the potential of leveraging a limited subset of gamma spectroscopy measurements to accurately estimate nuclide inventories throughout the off-gas system. The approach can be useful in reactor design activities and support analyses relevant to operations, safety, security, and safeguards.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

A survey on degradation modeling, prognosis, and prognostics-driven maintenance in wind energy systems

Wind energy generation proliferated over the past decades, introducing unique challenges and opportunities for failure prediction, operation and maintenance. Decision-makers are continuously looking into new methods to infer failure mechanisms and behaviors of wind turbine components to detect and intervene in the failures before they happen. Evidently, degradation modeling and prognosis become engaging topics for researchers and practitioners to prevent catastrophic failures. Prognostics-driven approaches predict the time of failure for the components (e.g., predicting remaining useful life), which provides significant insights for scheduling of operations and maintenance activities. Integrating these prognostics-driven insights into wind farm operations and maintenance presents a substantial challenge, demanding careful consideration of numerous factors such as accessibility, crew routing, and spare part logistics. This study provides state-of-the-art review for degradation modeling, prognosis, and prognostics-driven maintenance techniques for wind energy systems. The discussed techniques align with the United Nations' sustainable development goals, in particular Goal 7 (Affordable and Clean Energy), by enhancing effectiveness and sustainability of wind energy operations. This work also showcases open research questions related to degradation modeling, prognosis, and prognostics-driven maintenance.

Altinpulluk, Nur Banu↗
DOE OSTI2023

Elucidating the Influence of Metal Surface Composition on Organic Adsorbate Binding Using Active Particle Dynamics

The adsorption strengths of organic compounds on metal surfaces are sensitive to the metal composition, and they play a central role in many catalytic reactions, helping to control the coverage of the reactant and altering the overall reaction rate. While adsorption energies are straightforward to measure and calculate in vacuum and gas-phase environments, adsorption energetics can be dramatically altered by the presence of solvent in liquid-phase reactions. However, the effects of metal composition on binding strengths in a liquid environment are less well understood, primarily due to the difficulty of accurate in situ measurements of organic binding on metal surfaces in the liquid phase. Here, we utilize the motion of active particles in water to probe the adsorption energies of an organic adsorbate (furfural) on a range of metal surfaces (pure Pd, pure Pt, and four PdAu alloy compositions) to elucidate the effect of metal composition. Janus particles with catalytic caps of particular metal compositions all exhibited active motion resulting from consumption of H 2 O 2 ; adsorbate binding was inferred through the decrease in the velocity of active motion and was modeled by a Langmuir adsorption isotherm. The measured adsorption affinities were used to extract the adsorption enthalpy of furfural on the different metals. The Pd surface was found to bind furfural more strongly than the Pt surface by some 10 kJ/mol. Furthermore, the adsorption of furfural on the alloys was found to increase monotonically in magnitude with Pd content. As a result, the data reported herein aid the development of accurate understanding of organic adsorption in the presence of solvent and the role of the metal surface in tuning adsorption strengths to optimize catalytic processes in the liquid phase.

36 MATERIALS SCIENCE↗
DOE OSTI2023

MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations

We provide a global, long-term carbon flux dataset of gross primary production and ecosystem respiration generated using meta-learning, called MetaFlux. The idea behind meta-learning stems from the need to learn efficiently given sparse data by learning how to learn broad features across tasks to better infer other poorly sampled ones. Using meta-trained ensemble of deep models, we generate global carbon products on daily and monthly timescales at a 0.25-degree spatial resolution from 2001 to 2021, through a combination of reanalysis and remote-sensing products. Site-level validation finds that MetaFlux ensembles have lower validation error by 5–7% compared to their non-meta-trained counterparts. In addition, they are more robust to extreme observations, with 4–24% lower errors. We also checked for seasonality, interannual variability, and correlation to solar-induced fluorescence of the upscaled product and found that MetaFlux outperformed other machine-learning based carbon product, especially in the tropics and semi-arids by 10–40%. Overall, MetaFlux can be used to study a wide range of biogeochemical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Analysis of laser shock experiments on precompressed samples using a quartz reference and application to warm dense hydrogen and helium

Megabar (1 Mbar = 100 GPa) laser shocks on precompressed samples allow reaching unprecedented high densities and moderately high ~10 3 –10 4 K temperatures. We describe in this paper a complete analysis framework for the velocimetry (VISAR) and pyrometry (SOP) data produced in these experiments. Since the precompression increases the initial density of both the sample of interest and the quartz reference for pressure-density, reflectivity, and temperature measurements, we describe analytical corrections based on available experimental data on warm dense silica and density-functional-theory based molecular dynamics computer simulations. Finally, using our improved analysis framework, we report a re-analysis of previously published data on warm dense hydrogen and helium, compare the newly inferred pressure, density, and temperature data with most advanced equation of state models and provide updated reflectivity values.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Characterization of an imploding cylindrical plasma for electron transport studies using x-ray emission spectroscopy

We report here on the characterization of the conditions of an imploding cylindrical plasma by time-resolved x-ray emission spectroscopy. Knowledge about this implosion platform can be applied to studies of particle transport for inertial confinement fusion schemes or to astrophysical plasmas. A cylindrical Cl-doped CH foam within a tube of solid CH was irradiated by 36 beams (I total ~5×10 14 W/cm 2 , 1.5ns square pulse, and E total ~ 16.2kJ) of the OMEGA-60 laser to radially compress the CH toward the axis. The analysis of the time-resolved spectra showed that the compression can be described by four distinct phases, each presenting different plasma conditions. First the ablation of the cylinder is dominant; second, the foam is heated and induces a significant jump in emission intensities; third, the temperature and density of the foam reaches a maximum; and finally, the plasma expands. Ranges for the plasma temperature were inferred with the atomic physics code SCRAM (Spectroscopic Collisional-Radiative Atomic Model) and the experimental data have been compared to hydrodynamic simulations performed with the 2D code FLASH, which showed a similar implosion dynamic over time.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Estimate of pre-thermal quench non-thermal electron density profile during Ar pellet shutdowns of low-density target plasmas in DIII-D

The radial density profile of pre-thermal quench (pre-TQ) early-time runaway electrons (REs) is estimated by combining electron cyclotron emission (ECE) and soft x-ray (SXR) data during rapid shutdown of low-density (n e ≲ 10 13 cm -3 ) DIII-D target plasmas with cryogenic argon pellet injection. This technique is limited in these experiments to the pre-TQ phase and quickly loses validity during the TQ. Two different cases are studied: a high (10 keV) temperature target and a lower (4 keV) temperature target. The results indicate that early-time, low-energy (~10 keV) REs form ahead of the argon pellet as it enters the plasma, affecting the pellet ablation rate; it is hypothesized that this may be caused by rapid cross-field transport of argon ions ahead of the pellet. Fokker-Planck modeling of the two shots suggests that the RE current is quite significant during the pre-TQ phase (up to 50% of the total current). Comparison between modeled pre-TQ RE current and post-TQ RE current inferred from avalanche theory suggests that RE current increases during the high temperature target TQ but decreases during the low temperature target TQ. Here, the uncertainties in this estimate are large; but, if true, this suggests that TQ loss of REs can be larger than previously estimated in DIII-D.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Small angle scattering of diblock copolymers profiled by machine learning

We outline a machine learning strategy for quantitively determining the conformation of AB-type diblock copolymers with excluded volume effects using small angle scattering. Complemented by computer simulations, a correlation matrix connecting conformations of different copolymers according to their scattering features is established on the mathematical framework of a Gaussian process, a multivariate extension of the familiar univariate Gaussian distribution. We show that the relevant conformational characteristics of copolymers can be probabilistically inferred from their coherent scattering cross sections without any restriction imposed by model assumptions. This work not only facilitates the quantitative structural analysis of copolymer solutions but also provides the reliable benchmarking for the related theoretical development of scattering functions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Radiation burnthrough measurements to infer opacity at conditions close to the solar radiative zone–convective zone boundary

Recent measurements at the Sandia National Laboratory of the x-ray transmission of iron plasma have inferred opacities much higher than predicted by theory, which casts doubt on modeling of iron x-ray radiative opacity at conditions close to the solar convective zone-radiative zone boundary. An increased radiative opacity of the solar mixture, in particular iron, is a possible explanation for the disagreement in the position of the solar convection zone-radiative zone boundary as measured by helioseismology and predicted by modeling using the most recent photosphere analysis of the elemental composition. In this report we present data from radiation burnthrough experiments, which do not support a large increase in the opacity of iron at conditions close to the base of the solar convection zone and provide a constraint on the possible values of both the mean opacity and the opacity in the x-ray range of the Sandia experiments. The data agree with opacity values from current state-of-the-art opacity modeling using the CASSANDRA opacity code.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Population structure limits the use of genomic data for predicting phenotypes and managing genetic resources in forest trees

There is overwhelming evidence that forest trees are locally adapted to climate. Thus, genecological models based on population phenotypes have been used to measure local adaptation, infer genetic maladaptation to climate, and guide assisted migration. However, instead of phenotypes, there is increasing interest in using genomic data for gene resource management. We used whole-genome resequencing and common-garden experiments to understand the genetic architecture of adaptive traits in black cottonwood. We studied the potential of using genome-wide association studies (GWAS) and genomic prediction to detect causal loci, identify climate-adapted phenotypes, and inform gene resource management. We analyzed population structure by partitioning phenotypic and genomic (single-nucleotide polymorphism) variation among 840 genotypes collected from 91 stands along 16 rivers. Most phenotypic variation (60 to 81%) occurred among populations and was strongly associated with climate. Population phenotypes were predicted well using genomic data (e.g., predictive abilityr> 0.9) but almost as well using climate or geography (r> 0.8). In contrast, genomic prediction within populations was poor (r< 0.2). We identified many GWAS associations among populations, but most appeared to be spurious based on pooled within-population analyses. Hierarchical partitioning of linkage disequilibrium and haplotype sharing suggested that within-population genomic prediction and GWAS were poor because allele frequencies of causal loci and linked markers differed among populations. Given the urgent need to conserve natural populations and ecosystems, our results suggest that climate variables alone can be used to predict population phenotypes, delineate seed zones and deployment zones, and guide assisted migration.

Science & Technology - Other Topics↗