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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Validation of the two-region Rossi-alpha model for reflected assemblies

Nuclear criticality safety, nonproliferation and safeguards, emergency response, and stockpile stewardship utilize estimates of the k eff multiplication factor. The value of k eff cannot be directly measured, but it can be inferred from the prompt neutron period. One modality of measuring the prompt neutron period is the Rossi-alpha method, which is predicated on fitting a histogram of detection time differences due to the nonrandom temporal distribution of same-fission-chain neutrons. Recent works have developed the motivation and theory to expand traditional one-region point kinetic models resulting in one-exponential histogram fits to two-region models that result in two-exponential fits. This paper validates the new two-region model using organic scintillator measurements of copper-reflected weapons-grade plutonium (0:83 ≤ k eff ≤ 0:94) and high-density- polyethylene-reflected highly enriched uranium (0:73 ≤ k eff ≤ 0:95). Furthermore, the results show that more thermal systems have shorter prompt neutron periods in the core region due to increased induced-fission probabilities for moderated neutrons. Here, a new parameter introduced by the two- region model is also shown to be correlated to the amount of reflection, and may be used to infer assembly properties such as type and amount of reflector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Geographic Distribution of Populus trichocarpa Genotypes by ADMIXTURE Ancestry

An interactive map showing Populus trichocarpa GWAS population structure estimated by ADMIXTURE (k=3, selected as optimal from k=2-11). Sampling locations are colored by their predominant ancestry proportion among the three inferred populations and geographic origins are searchable by genotype or river system using the search bar.

Admixture↗
DOE OSTI2023

Deriving spatially explicit direct and indirect interaction networks from animal movement data

Abstract Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems [“GPS”]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous‐time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions—individuals occurring at the same location, but at different times—while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease‐relevant interaction networks for two behaviorally differentiated species, wild pigs ( Sus scrofa ) that can host African Swine Fever and mule deer ( Odocoileus hemionus ) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30‐min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM‐Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine‐scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer–resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A flexible class of priors for orthonormal matrices with basis function-specific structure

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure a priori. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2024

Avalanche statistics of fluctuation-induced fluxes from the SLPM and the W7-AS stellarator

Measurements of fluctuating floating potentials and ion saturation currents at different radial locations in the Santander Linear Plasma Machine (Castellanos et al 2005 Plasma Phys. Control. Fusion47 2067) and at the edge of the W7-AS stellarator by means of radially movable Langmuir probes allow to infer the corresponding fluctuation-induced radial flux temporal series. Avalanche-like transport events are identified in the time series and statistically characterized in terms of avalanche size/duration/quiet-time distributions and size-duration scaling relations. Transport is diffusive in the inner and intermediate radial region of the SLPM r < r tr ≈ 2.6 cm, undergoing a transition at r tr , becoming non-diffusive in the outermost region of the device, r > r tr . Here, the results obtained at the edge of the W7-AS stellarator are similar to those found in SLPM for r > r tr , i.e. consistent with what would be expected for scale-free, self-similar plasma transport dynamics near a critical state.

Avalanches↗
DOE OSTI2025

Missing baryons recovered: A measurement of the gas fraction in galaxies and groups with the kinematic Sunyaev-Zel’dovich effect and CMB lensing

We present new constraints on the halo masses and matter density profiles of DESI galaxy groups by cross-correlating samples of luminous red galaxies (LRGs) and bright galaxy survey (BGS) galaxies with the publicly available CMB lensing convergence map from ACT DR6. This provides an independent, lensing-based calibration of halo masses, complementary to methods relying on clustering or dynamics. We derive constraints on the mean halo mass for three DESI-selected samples, finding log⁡(𝑀 halo /(𝑀 ⊙ /ℎ)) ≈ 13.18, 13.03 and 13.02 for the main LRG, extended LRG, and BGS samples, respectively. Using a halo model approach, we also compare the projected galaxy-matter density profiles with previously reported gas profiles inferred from measurements of the kinematic Sunyaev-Zel’dovich (kSZ) effect. This work addresses one of the key uncertainties in interpreting kSZ signals—the unknown host halo mass distribution—by providing an independent and consistent mass calibration. The agreement between the gas and total mass profiles at large aperture suggests that sufficiently far from the group center (2–3 virial radii), we recover all the baryons, offering a resolution to the missing baryon problem. We further study the cumulative gas fractions for all galaxies as well as for the most massive galaxy groups in the sample [log⁡(𝑀 halo /(𝑀 ⊙ /ℎ)) ≈ 13.5], finding values that are physically sensible and in agreement with previous findings using kSZ and x-ray data: compared to the TNG300 simulation, the observed gas fractions are systematically lower at fixed radius by ≳ 4⁢𝜎, providing compelling, independent evidence for stronger baryonic feedback in the real Universe. These findings highlight the power of combining CMB lensing with galaxy surveys to probe the interplay between baryons and dark matter in group-sized halos.

Hadzhiyska, Boryana [Lawrence Berkeley National La↗
DOE OSTI2026

Multidimensional scaling informed by F -statistic: Visualizing grouped microbiome data with inference

Multidimensional scaling (MDS) is a widely used dimensionality reduction technique in microbial ecology data analysis that captures the multivariate structure of the data while preserving pairwise distances between samples. While improvements in MDS have enhanced the ability to reveal group-specific data patterns, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. Here, in this study, we introduce a new MDS-based ordination method, “F-informed MDS,” which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using semisynthetic datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and global data structures. Its application to a diatom-associated bacterial community suggests the role of this new method in interpreting the community’s response to the host. Our approach offers a well-founded refinement of MDS that aligns with statistical test results, which can be beneficial for broader multidimensional data analyses in microbiology and ecology. This new visualization tool can be incorporated into standard microbiome data analyses.

Biological and medical sciences↗
DOE OSTI2022

Normalizing flows for likelihood-free inference with fusion simulations

Fluid-based scrape-off layer transport codes, such as UEDGE, are heavily utilized in tokamak analysis and design, but typically require user-specified anomalous transport coefficients to match experiments. Determining the uniqueness of these parameters and the uncertainties in them to match experiments can provide valuable insights to fusion scientists. Here, we leverage recent work in the area of likelihood-free inference (‘simulation-based inference’) to train a neural network, which enables accurate statistical inference of the anomalous transport coefficients given experimental plasma profile input. UEDGE is treated as a black-box simulator and runs multiple times with anomalous transport coefficients sampled from priors, and the neural network is trained on these simulations to emulate the posterior. The neural network is trained as a normalizing flow model for density estimation, allowing it to accurately represent complicated, high-dimensional distribution functions. With a fixed simulation budget, we compare a single-round procedure to a multi-round approach that guides the training simulations toward a specific target observation. Finally, we discuss the future possibilities for use of amortized models, which train on a wide range of simulations and enable fast statistical inference for results during experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Kinetic simulation of a 50 mTorr capacitively coupled argon discharge over a range of frequencies and comparison to experiments

The effect of driving frequency in the range of 13.56–73 MHz on electron energy distribution and electron heating modes in a 50 mTorr capacitively coupled argon plasma discharge is studied using 1D-3V particle-in-cell simulations. Calculated electron energy probability functions exhibit three distinct “temperatures” for low-, mid-, and high-energy electrons at all the studied driving frequencies. When compared to published experimental data, the calculated probability functions show a reasonable agreement for the energy range resolved in the measurements (about 2–10 eV). Discrepancies due to limitations in experimental energy resolution outside this range lead to differences between computational and experimental values of the electron number density determined from the distribution functions, and the predicted effective electron temperature is within 25% of experimental values. The impedance of the discharge is interpreted in terms of a homogeneous equivalent circuit model, and the driving frequency dependence of the inferred combined sheath thickness is found to obey a known, theoretically derived, power law. The average power transferred from the field to the electrons (electron heating) is computed, and a region of negative heating near the sheath edge, particularly at higher driving frequencies, is identified. Analysis of the electron momentum equation shows that electron inertia, which on temporal averaging would be zero in a linear regime, is responsible for negative values of power deposition near the sheath edge at high driving frequencies due to the highly nonlinear behavior of the discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Local Atomic Structure in Photo-isomerized Ruthenium Sulfur Dioxide Complexes Revealed by Pair Distribution Function Analysis

SO 2 linkage photoisomerization in crystalline ruthenium-based complexes has demonstrated nanophotonic phenomena such as optical switching and nano-optomechanical transduction. Molecular insights into these materials have been explored largely via the characterization of their photoinduced crystal structures via in situ single-crystal X-ray diffraction, known as photocrystallography. Photoinduced molecular disorder is present, which photocrystallography can model to the extent that it is confined within the periodic boundary of a unit cell. However, non-periodic molecular disorder is suspected to exist as well. In situ total scattering experiments were therefore carried out on finely powdered crystals of four ruthenium-sulfur dioxide complexes. Data were modeled using light-minus-dark' difference pair distribution function analysis, which afforded photoinduced structural changes exclusively. This revealed structural features that were first compared against models of photoinduced crystal structures known a priori from photocrystallography. Furthermore, statistical inference was then employed, which evidenced generally good agreement between the total scattering data and the photocrystallographic models, while revealing real differences that are indicative of a structure with only a short-range order. Overall, our findings demonstrate that in situ light-induced total scattering experiments on finely powdered crystals are able to reveal the photoinduced structure. Overall, the evidence suggests that such structure could include a short-range order as well as photocrystallographic content. Our demonstration experiment offers a pathway to develop studies that capture the short-range order in linkage photoisomers, while we have outlined the procedure for testing the validity of associated structural models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

ATLAS Data Analysis using a Parallel Workflow on Distributed Cloud-based Services with GPUs

A new type of parallel workflow is developed for the ATLAS experiment at the Large Hadron Collider, that makes use of distributed computing combined with a cloud-based infrastructure. This has been developed for a specific type of analysis using ATLAS data, one popularly referred to as Simulation-Based Inference (SBI). The JAX library is used for the parts of the workflow to compute gradients as well as accelerate program execution using just-in-time compilation, which becomes essential in a full SBI analysis and can also offer significant speed-ups in more traditional types of analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Chaotic neural dynamics facilitate probabilistic computations through sampling

Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Deep inference of simulated strong lenses in ground-based surveys

The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dark matter. However, leveraging the increased statistical power of such large samples will require further development of automated lens modeling techniques. We show that deep learning and simulation-based inference (SBI) methods produce informative and reliable estimates of parameter posteriors for strong lensing systems in ground-based surveys. We present the examination and comparison of two approaches to lens parameter estimation for strong galaxy-galaxy lenses — Neural Posterior Estimation (NPE) and Bayesian Neural Networks (BNNs). We perform inference on 1-, 5-, and 12-parameter lens models for ground-based imaging data that mimics the Dark Energy Survey (DES). We find that NPE outperforms BNNs, producing posterior distributions that are more accurate, precise, and well-calibrated for most parameters. For the 12-parameter NPE model, the calibration is consistently within <10% of optimal calibration for all parameters, while the BNN is rarely within 20% of optimal calibration for any of the parameters. Similarly, residuals for most of the parameters are smaller (by up to an order of magnitude) with the NPE model than the BNN model. This work takes important steps in the systematic comparison of methods for different levels of model complexity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

What goes up must come down: impacts of deposition in a sulfate geoengineering scenario

The problem of reducing the impacts of rising anthropogenic greenhouse gas on warming temperatures has led to the proposal of using stratospheric aerosols to reflect some of the incoming solar radiation back to space. The deliberate injection of sulfur into the stratosphere to form stratospheric sulfate aerosols, emulating volcanoes, will result in sulfate deposition to the surface. We consider here an extreme sulfate geoengineering scenario necessary to maintain temperatures at 2020 levels while greenhouse gas emissions continue to grow unabated. We show that the amount of stratospheric sulfate needed could be globally balanced by the predicted decrease in tropospheric anthropogenic SO 2 emissions, but the spatial distribution would move from industrialized regions to pristine areas. We show how these changes would affect ecosystems differently depending on present day observations of soil pH, which we use to infer the potential for acid-induced aluminum toxicity across the planet.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The impact of tomographic redshift bin width errors on cosmological probes

ABSTRACT Systematic errors in the galaxy redshift distribution n(z) can propagate to systematic errors in the derived cosmology. We characterize how the degenerate effects in tomographic bin widths and galaxy bias impart systematic errors on cosmology inference using observational data from the Deep Lens Survey. For this we use a combination of galaxy clustering and galaxy–galaxy lensing. We present two end-to-end analyses from the catalogue level to parameter estimation. We produce an initial cosmological inference using fiducial tomographic redshift bins derived from photometric redshifts, then compare this with a result where the redshift bins are empirically corrected using a set of spectroscopic redshifts. We find that the derived parameter S8 ≡ σ8(Ωm/.3)1/2 decreases from $0.841^{+0.062}_{-0.061}$ to $0.781^{0.061}_{0.054}$ upon correcting the n(z) errors in the second method.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

libEnsemble: A complete Python toolkit for dynamic ensembles of calculations

Almost all science and engineering applications eventually stop scaling: their runtime no longer decreases as available computational resources increase. Therefore, many applications will struggle to efficiently use emerging extreme-scale high-performance, parallel, and distributed systems. libEnsemble is a complete Python toolkit and workflow system for intelligently driving ensembles of experiments or simulations at massive scales. It enables and encourages multidisciplinary design, decision, and inference studies portably running on laptops, clusters, and supercomputers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR

Fast forecasting of the reservoir pressure distribution during geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from few wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D) geologic properties and predicts reservoir pressure with quantified uncertainties. We use a synthetic commercial-scale GCS model with bimodally distributed permeability and porosity to demonstrate the efficacy of the workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal fields. The computational efficiency of the workflow is boosted by two residual U-Net based surrogate models for surface displacement and reservoir pressure predictions, respectively. The workflow can complete data assimilation and reservoir pressure forecasting in half an hour on a personal computer.

25 ENERGY STORAGE↗