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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Methods to Evaluate Subcolumn Profiles Based on Two-Point Diagnostics

In atmospheric models, stochastic generation of subgrid-scale profiles or “subcolumns” has been used for a variety of purposes. Such subcolumns can be generated from subgrid probability density functions (PDFs) at different vertical levels, when such PDFs are available. To do so, the generator needs to decide how strongly points should be correlated in the vertical, that is, how much the values should be overlapped. This is sometimes called “PDF overlap.” To assess vertical correlation in a simplified, observable setting, here the vertical correlation of vertical velocity in subcloud layers is examined. Doppler lidar is used to evaluate the vertical profiles of vertical velocity produced by a large-eddy simulation (LES) model and the Subgrid Importance Latin Hypercube Sampler (SILHS) subcolumn generator. In order to diagnose unrealistic features in subcolumn profiles, various statistical diagnostics are examined here, including the bivariate PDF of vertical velocity at two separated points (i.e., altitudes), the two-point velocity correlation, the integral correlation length, the PDF of two-point velocity differences, and the skewness and kurtosis of two-point velocity differences. The profiles produced by LES match lidar well, except that they are too smooth at small scales. The profiles produced by SILHS exhibit sharp jumps from updraft to downdraft that are not observed in the lidar data. To reduce the generation of these unrealistically sharp jumps, the SILHS sampling method is revised. The diagnostics confirm that the revised sampling method reduces the overprediction of sharp jumps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Point-of-use filtration units as drinking water distribution system sentinels

Abstract Municipal drinking water distribution systems (DWDSs) and associated premise plumbing (PP) systems are vulnerable to proliferation of opportunistic pathogens, even when chemical disinfection residuals are present, thus presenting a public health risk. Monitoring the structure of microbial communities of drinking water is challenging because of limited continuous access to faucets, pipes, and storage tanks. We propose a scalable household sampling method, which uses spent activated carbon and reverse osmosis (RO) membrane point-of-use (POU) filters to evaluate mid- to long-term occurrence of microorganisms in PP systems that are relevant to consumer exposure. As a proof of concept, POU filter microbiomes were collected from four different locations and analyzed with 16S rRNA gene amplicon sequencing. The analyses revealed distinct microbial communities, with occasional detection of potential pathogens. The findings highlight the importance of local, and if possible, continuous monitoring within and across distribution systems. The continuous operation of POU filters offers an advantage in capturing species that may be missed by instantaneous sampling methods. We suggest that water utilities, public institutions, and regulatory agencies take advantage of end-of-life POU filters for microbial monitoring. This approach can be easily implemented to ensure drinking water safety, especially from microbes of emerging concerns; e.g., pathogenic Legionella and Mycobacterium species.

42 ENGINEERING↗
DOE OSTI2025

Round Robin Analysis of Uranium Isotopics on Cotton Swipes Measured Using Microextraction-Based Sampling Versus Conventional Bulk Analysis

A comparison of microextraction sampling methods to directly analyze uranium isotopics on cotton swipes was performed concurrently with traditional bulk-processing methods. For the microextraction sampling approach, two different detection platforms were evaluated, a quadrupole-based inductively coupled plasma mass spectrometer (ICP-MS) and the liquid sampling-atmospheric pressure glow discharge (LS-APGD) coupled to an Orbitrap mass spectrometer. Results presented from this innovative sampling approach (i.e., microextraction) are compared with a more traditional approach employed for analysis of cotton-based environmental swipes, namely bulk ashing/digestion, separation, and subsequent analysis by high-precision multi-collector ICP-MS. Overall, the microextraction approach proved to be a reliable and accurate means to determine isotopic ratios of uranium collected on cotton swipes. The ICP-MS-based detection had relative standard deviations of <0.65% for the major isotopic determinations, whereas the LS-APGD-Orbitrap method had relative standard deviations of <3%. The percent relative difference for the 235 U/ 238 U ratios, in comparison to the expected values, was <1% for ICP-MS and <4% for the microplasma-Orbitrap method. Additionally, the microextraction ICP-MS accurately (<2%) and precisely (<5%) determined the minor isotopic compositions (i.e., 234 U/ 238 U and 236 U/ 238 U).

ICP-MS↗
DOE OSTI2024

Apparatus and methods for sample analysis with multi-gradient microfluidics

A device for analyzing biological samples comprises first, second, third, and fourth layers. The first layer comprises a sample chamber in which a sample is positioned. The second layer comprises first, second, and third channels. A third, porous layer is positioned between the first layer and the second layer. A fourth layer composed of a substantially liquid-impermeable material is positioned between the second layer and the third layer. The fourth layer includes first and second pass-through channels that are aligned with the first and second channel, respectively. Fluids that flow in the first and second channels pass through the pass-through channels and diffuse into the sample chamber, establishing a chemical concentration gradient therein. A gas in the sample chamber can diffuse through the third and fourth layers and interact with a fluid flowing in the third channel, establishing a gas concentration gradient in the sample chamber.

Kim, Peter Wonhee↗
DOE OSTI2024

Efficient learning of accurate surrogates for simulations of complex systems

Machine learning methods are increasingly deployed to construct surrogate models for complex physical systems at a reduced computational cost. However, the predictive capability of these surrogates degrades in the presence of noisy, sparse or dynamic data. Here, we introduce an online learning method empowered by optimizer-driven sampling that has two advantages over current approaches: it ensures that all local extrema (including endpoints) of the model response surface are included in the training data, and it employs a continuous validation and update process in which surrogates undergo retraining when their performance falls below a validity threshold. We find, using benchmark functions, that optimizer-directed sampling generally outperforms traditional sampling methods in terms of accuracy around local extrema even when the scoring metric is biased towards assessing overall accuracy. Finally, the application to dense nuclear matter demonstrates that highly accurate surrogates for a nuclear equation-of-state model can be reliably autogenerated from expensive calculations using few model evaluations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. Here, we present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Field expedient stool collection methods for gut microbiome analysis in deployed military environments

ABSTRACT Field expedient devices and protocols for the collection, storage, and shipment of stool samples in deployed settings are needed for the advancement of microbiome research in military health. Relevant assessments include the evaluation of microbiome signatures associated with susceptibility to travelers’ diarrhea and recovery of gut function following infection. However, inherent biases in microbial measurements due to preservatives and sampling methods are unclear and should be assessed for an accurate evaluation of the microbiome. We performed shotgun metagenomic sequencing and compared the microbiome composition in paired fecal samples collected using Flinters Technology Associates (FTA) cards and OMNIgene (OG) Gut tubes, prior to and during international travel, from 49 adult participants, 39 of whom remained asymptomatic and 10 experienced travelers’ diarrhea. Higher concentrations of nucleic acid and sequencing libraries were observed in OG samples. A majority of genera (82.9%) were detected with both methods, and detections of genera limited to one collection method were not highly prevalent across samples and were present in extremely low relative abundances (<0.01%). Differences in beta diversity were largely explained by inter-individuality of microbiome composition, followed by the effect of collection method and timepoint-disease states. Differential abundance analysis indicated that Corynebacterium and Blautia were consistently higher in abundance across all groups with FTA and OG collection, respectively. The observed differences in microbiome composition between methods suggest the need for consistent and standardized protocols within a study. Overall, the data presented here could help guide the future design of fecal microbiome study protocols in field and military deployment settings. IMPORTANCE The assessment of field-deployable methods for fecal sample collection and storage is required to reliably capture samples collected in remote and austere locations. This study describes a comparative metagenomics analysis between samples collected by two different commercially available methods in a military-deployed setting. The results presented here are foundational for the future design of fecal microbiome study protocols in an operational context.

field study↗
DOE OSTI2025

Molten Salt Synthesis of Increased (100)-Facet and Polycrystalline Nickel Oxide Nanoparticles for the Oxygen Evolution Reaction: Impact of Facet and Crystallinity on Electrocatalysis

Nickel oxide nanocubes with increased (100) surface facet presence (NiO(100)) were synthesized through a molten salt synthesis procedure to probe their oxygen evolution reaction (OER) activity in order to investigate the relationship between the surface facet and OER performance. While altering the synthesis parameters to decrease NiO(100) particle sizes and agglomeration, a polycrystalline NiO nanoparticle system formed from using Li2O as a Lux-Flood base (labelled Li2O-MSS NiO, where MSS stands for molten salt synthesis). This novel synthesis was further elaborated and the obtained materials were also tested for OER activity. After thorough structural characterization to determine crystallinity, lattice spacings, and elemental distribution, their OER activity was compared versus high surface area NiO(111) nanosheets in a three-electrode rotating disk electrode (RDE) system. The activity trend of (111) > Li2O-MSS > (100) was observed. This decrease in activity of the nanocube and polycrystalline samples was explained by differences between theoretical and experimental conditions, differences in ink rheology and resulting catalyst layer properties, and significant agglomeration seen in the imaging of the sample. Methods for improving the OER activity of these samples are discussed in the conclusion of this study.

08 HYDROGEN↗
DOE OSTI2025

Efficient Monte Carlo event generation for neutrino-nucleus exclusive cross sections

Modern neutrino-nucleus cross section computations need to incorporate sophisticated nuclear models to achieve greater predictive precision. However, the computational complexity of these advanced models often limits their practicality for experimental analyses. To address this challenge, we introduce a new Monte Carlo method utilizing normalizing flows to generate surrogate cross sections that closely approximate those of the original model while significantly reducing computational overhead. As a case study, we built a Monte Carlo event generator for the neutrino-nucleus cross section model developed by the Ghent group. This model employs a Hartree-Fock procedure to establish a quantum mechanical framework in which both the bound and scattering nucleon states are solutions to the mean-field nuclear potential. The surrogate cross sections generated by our method demonstrate excellent accuracy with a relative effective sample size of more than 98.4%, providing a computationally efficient alternative to traditional Monte Carlo sampling methods for differential cross sections.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics

Molecular dynamics simulations offer detailed insights into atomic motions but face timescale limitations. Enhanced sampling methods have addressed these challenges but even with machine learning, they often rely on pre-selected expert-based features. Here, in this work, we present a Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) framework, which combines graph neural networks and the state predictive information bottleneck to automatically learn low-dimensional representations directly from atomic coordinates. Tested on three benchmark systems, our approach predicts essential structural, thermodynamic and kinetic information for slow processes, demonstrating robustness across diverse systems. The method shows promise for complex systems, enabling effective enhanced sampling without requiring pre-defined reaction coordinates or input features.

Zou, Ziyue↗
DOE OSTI2024

Calendar Year 2023 Underground Test Area Annual Sampling Letter Report Nevada National Security Site

The calendar year 2023 annual sampling letter report presents Plan implementation and a summary of field activities, sampling methods, well completion diagrams, and analytical laboratory results from sampling of wells associated with the Pahute Mesa CAUs. The CY 2023 letter report also presents data from samples collected from Well ER-EC-11_p3 as part of a sampling investigation. This well was resampled because of anomalously low standard tritium (3H) results from bailer sampling that occurred in July 2021.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Renovating Monte Carlo Methods and Codebases with Generative Models

The code will implement a standardized interface for Monte Carlo sampling methods, including conventional techniques, and going beyond current available packages to also incorporate generative model-enabled Monte Carlo sampling to provide a unified framewor

Garcia-Cardona, Cristina↗
DOE OSTI2026

Hierarchical Gaussian Random Field Sampling for Multilevel Markov Chain Monte Carlo: Coupling Stochastic Partial Differential Equation and the Karhunen–Loève Decomposition

This work introduces structure preserving hierarchical decompositions for sampling Gaussian random fields (GRFs) within the context of multilevel Bayesian inference in high-dimensional space. Existing scalable hierarchical sampling methods, such as those based on stochastic partial differential equations (SPDEs), often reduce the dimensionality of the sample space at the cost of accuracy of inference. Other approaches, such that those based on Karhunen-Loève (KL) expansions, offer sample space dimensionality reduction but sacrifice GRF representation accuracy and ergodicity of the Markov chain Monte Carlo (MCMC) sampler and are computationally expensive for high-dimensional problems. The proposed method integrates the dimensionality reduction capabilities of KL expansions with the scalability of SPDE-based sampling, thereby providing a robust, unified framework for high-dimensional uncertainty quantification (UQ) that is scalable and accurate, preserves ergodicity, and offers dimensionality reduction of the sample space. The hierarchy in our multilevel algorithm is derived from the geometric multigrid hierarchy. By constructing a hierarchical decomposition that maintains the covariance structure across the levels in the hierarchy, the approach enables efficient coarse-to-fine sampling while ensuring that all samples are drawn from the desired distribution. The effectiveness of the proposed method is demonstrated on a benchmark subsurface flow problem, demonstrating its effectiveness in improving computational efficiency and statistical accuracy. Furthermore, our proposed technique is more efficient and accurate and displays better convergence properties than existing methods for high-dimensional Bayesian inference problems.

Gaussian random fields↗
DOE OSTI2026

Navigating the Path to Autonomy: Real-World Lessons from an Air-Free Self-Driving Laboratory

While autonomous experimentation has promise to accelerate discovery in physcial sciences, the real-world integration of predictive models and experimentation is non-trivial. Here we describe the genesis of a self-driving laboratory (SDL) for air-sensitive chemistry at Argonne National Laboratory and demonstrate the experimental design considerations needed for high-throughput experiments before predictive models can lead to scientific discovery. Our SDL was designed to explore battery electrolyte stability. Our final SDL utilized plate readers in a glovebox with a nitrogen atmosphere to perform kinetic assays and screen hundreds of battery-relevant solvents. However, the roadmap to autonomy and airfree-friendly experimentation required the complex evaluation of several spectroscopic and chromatographic methods. The greatest experimental challenges were (a) developing long-term sampling methods that remained air-free; (b) accelerating kinetics to advance reactivity projections; and (c) ensuring labware compatibility with nonaqueous solvents used in battery chemistry. Our experiences highlight the practical gap between closed-loop aspirations and the realities of chemical discovery, offering lessons on the challenges of transferring every day laboratory workflows to autonomy. These results suggest a more realistic blueprint for autonomy in chemistry—one that balances thoughtful and realistic experimental formulation.

Robertson, Lily A.↗
DOE OSTI2026

Adaptive Sampling Trust Region Method for Bi-fidelity Simulation Optimization [SWR-25-166]

Adaptive Sampling Trust Region Method for Bi-fidelity Simulation Optimization aims to demonstrate the effect of adaptive sampling-based bi-fidelity stochastic trust region method (ASTRO-BFDF). ASTRO-BFDF, derived from a derivative-free adaptive sampling trust-region optimization (ASTRO-DF) (Shashaani et al. 2018, Ha and Shashaani 2023), intended to efficiently solve the bi-fidelity simulation optimization.

Mueller, Juliane [National Laboratory of the Rocki↗