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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 OSTI2024

Multi-Tiered Estimation for Correlation Spectroscopy in 3D (MTECS3D) v0.1

This innovative software estimates rotational diffusion coefficients of particles from X-ray photon correlation spectroscopy (XPCS) data of monodispersed particle systems. It is the first method capable of extracting rotational diffusion information from three-dimensional particle systems using XPCS. Using the angular-temporal cross-correlation of the XPCS images, this software is able to estimate the rotational diffusion coefficients with only a few percent relative errors while requiring minimal prior knowledge of particle structures. This software enhances XPCS analysis capabilities, allowing researchers to study translational and rotational Brownian dynamics of particles in suspension across various temporal and spatial scales.

Hu, Zixi↗
DOE OSTI2026

Comparative Performance of Gaussian Plume and Backward Lagrangian Stochastic Models for Near-Field Methane Emission Estimation Using a Single Controlled Release Experiment

Methane (CH 4 ) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH 4 emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH 4 emissions at the facility-level. Near-field quantification of methane (CH 4 ) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH 4 emissions to controlled single-point emissions between 0.4 and 5.2 kg CH 4 h −1 . Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain.

gaussian plume↗
DOE OSTI2024

HPC Network Simulation Tuning via Automatic Extraction of Hardware Parameters

Popular HPC network interconnection simulators such as SST/macro provide a variety of configurable parameters to explore the design space of hardware components such as network interface cards (NIC), switches, and links among them. While such knobs provide flexibility to explore design trade-offs for novel hardware, manually configuring simulations for matching configurations of the existing hardware to focus on topology exploration can be cumbersome and error-prone, leading to widely inaccurate simulations. This challenge is compounded when specifications of various (proprietary) technologies are not readily available or intentionally omitted. In this work, we propose a framework to autotune the multiple network models’ simulation configurations within SST/macro using Tree-structured Parzen Estimator-based Bayesian optimization to observe the effect on simulation accuracy across different message regimes. These regimes consist of small to large message sizes and latency to bandwidth-bound messages. We provide a detailed analysis of the simulation error for four representative HPC systems. Our Bayesian optimization based autotuning framework for network models achieves a maximum of 5x improvement in accuracy over best-effort manual configurations based on available hardware specifications.

Simulation, autotuning↗
DOE OSTI2025

CFDverify

Estimating the discretization error of computational fluid dynamics (CFD) or other scientific codes as part of solution verification is often a non-trivial part of the analysis process. Methods can be complicated and may include assumptions/qualifications that need to be checked during analysis. CFD analysts, therefore, are likely to make errors when trying to conduct this necessary analysis on their own in not knowing about the best method for their problem, not correctly implementing a method, or in not have diagnostic tools to determine if the method was correctly applied.

Weinmeister, Justin [Oak Ridge National Laboratory↗
DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗
DOE OSTI2025

Hierarchical Bayesian Inverse Problems: A High-Dimensional Statistics Viewpoint

This paper analyzes hierarchical Bayesian inverse problems using techniques from highdimensional statistics. Furthermore, our analysis leverages a property of hierarchical Bayesian regularizers that we call approximate decomposability to obtain non-asymptotic bounds on the reconstruction error attained by maximum a posteriori estimators. The new theory explains how hierarchical Bayesian models that exploit sparsity, group sparsity, and sparse representations of the unknown parameter can achieve accurate reconstructions in high-dimensional settings.

MAP estimation↗
DOE OSTI2026

Tracing U.S. fuel life-cycle greenhouse gas emissions in a multi-sector dynamics model using LC-GCAM

Model-based analysis of fuel pathways is essential for informing energy and environmental policy. Two major model types are typically used: multi-sector dynamics models, which capture the broader energy-economy, such as GCAM (Global Change Analysis Model), and life cycle assessment models, such as GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation). Each has distinct strengths and limitations, and recent studies increasingly adopt hybrid approaches to harness the advantages of both. However, such integration is often time-consuming and complicated by inconsistencies in system boundaries and technology definitions. We present LC-GCAM, a new tool that enables estimation of life-cycle greenhouse gas emissions and primary energy use for any fuel pathway represented in GCAM. We apply LC-GCAM to 300 scenarios designed to explore key uncertainties affecting the life-cycle performance of future fuel options in the U.S. freight sector. To evaluate LC-GCAM, we compare its results with those from GREET for nine fuel types in a 2030 reference scenario. When input assumptions are modestly aligned, LC-GCAM and GREET estimates typically agree within 10% (absolute sum-based mean absolute percentage error). LC-GCAM offers a flexible and efficient approach to generating life-cycle metrics within an integrated modeling framework, supporting robust policy analysis across a wide range of interacting energy system uncertainties.

Wolfram, Paul↗
DOE OSTI2025

Dark Energy Survey Year 6 Results: Point-Spread Function Modeling

We present the point-spread function (PSF) modeling for weak lensing shear measurement using the full six years of the Dark Energy Survey (DES Y6) data. We review the PSF estimation procedure using the PIFF (PSFs In the Full FOV) software package and describe the key improvements made to PIFF and modeling diagnostics since the DES year three (Y3) analysis: (i) use of external Gaia and infrared photometry catalogs to ensure higher purity of the stellar sample used for model fitting, (ii) addition of color-dependent PSF modeling, the first for any weak lensing analysis, and (iii) inclusion of model diagnostics inspecting fourth-order moments, which can bias weak lensing measurements to a similar degree as second-order modeling errors. Through a comprehensive set of diagnostic tests, we demonstrate the improved accuracy of the Y6 models evident in significantly smaller systematic errors than those of the Y3 analysis, in which all g band data were excluded due to insufficiently accurate PSF models. For the Y6 weak lensing analysis, we include g band photometry data in addition to the riz bands, providing a fourth band for photometric redshift estimation. Looking forward to the next generation of wide-field surveys, we describe several ongoing improvements to PIFF, which will be the default PSF modeling software for weak lensing analyses for the Vera C. Rubin Observatory’s Legacy Survey of Space and Time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Elliptically-Contoured Tensor-variate Distributions with Application to Image Learning

Statistical analysis of tensor-valued data has largely used the tensor-variate normal (TVN) distribution that may be inadequate for data arising from distributions with heavier or lighter tails. We study a general family of elliptically contoured (EC) TV distributions and derive its characterizations, moments, marginal, and conditional distributions. We describe procedures for maximum likelihood estimation from data that are (1) uncorrelated draws from an EC distribution, (2) from a scale mixture of the TVN distribution, and (3) from an underlying but unknown EC distribution, for which we extend Tyler’s robust estimator. A detailed simulation study highlights the benefits of choosing an EC distribution over the TVN for heavier-tailed data. We develop TV classification rules using discriminant analysis and EC errors and show that they better predict cats and dogs from images in the Animal Faces-HQ dataset than the TVN-based rules. A novel tensor-on-tensor regression and TV analysis of variance (TANOVA) framework under EC errors is also demonstrated to better characterize gender, age, and ethnic origin than the usual TVN-based TANOVA in the celebrated labeled faces of the wild dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

AGR-5/6/7 CAPSULE 1 THERMAL MODEL WITH OFFSET GAS GAPS

Fuel compact temperatures are a crucial factor in assessing the irradiation performance of tristructural isotropic (TRISO) fuel particles. In the absence of direct measurement, fuel compact temperatures were calculated using a 3 D finite-element thermal model that was subject to simulation uncertainty. The most dominant factor in the uncertainty of calculated fuel temperatures is the gas gap uncertainty caused by the graphite holder nub-to-capsule clearance. This thermal model was revised to examine the most probable graphite offset position for Capsule 1 for four different days during the irradiation. In the analysis, the offset distance and azimuthal direction at both the top and bottom of the holder were varied. The best-fit offset was estimated based on the minimum root mean square error (RMSE) of the residuals (measured minus calculated thermocouple [TC] temperatures) for the operational TCs in Capsule 1. Near the end of the irradiation, when the graphite had shrunk and the gas gaps had grown, a peak temperature was found with maximum offset along the TC lines, with nickel introducing a potential mechanism for fuel particle failures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Discrete Superconvergence Analysis for Quantum Magnus Algorithms of Unbounded Hamiltonian Simulation

Motivated by various applications, unbounded Hamiltonian simulation has recently garnered great attention. Quantum Magnus algorithms, designed to achieve commutator scaling for time-dependent Hamiltonian simulation, have been found to be particularly efficient for such applications. When applied to unbounded Hamiltonian simulation in the interaction picture, they exhibit an unexpected superconvergence phenomenon. However, existing proofs are limited to the spatially continuous setting and do not extend to discrete spatial discretizations. Here, in this work, we provide the first superconvergence estimate in the fully discrete setting with a finite number of spatial discretization points N, and show that it holds with an error constant uniform in N. The proof is based on the two-parameter symbol class, which, to our knowledge, is applied for the first time in algorithm analysis. The key idea is to establish a semiclassical framework by identifying two parameters through the discretization number and the time step size rescaled by the operator norm, such that the semiclassical uniformity guarantees the uniformity of both. This approach may have broader applications in numerical analysis beyond the specific context of this work.

Borns-Weil, Yonah [University of California, Berke↗
DOE OSTI2025

Lanczos algorithm for lattice QCD matrix elements

Recent work [M. L. Wagman, Lanczos, the transfer matrix, and the signal-to-noise problem, .] found that an analysis formalism based on the Lanczos algorithm allows energy levels to be extracted from Euclidean correlation functions with faster ground-state convergence than effective masses, convergent estimators for multiple states from a single correlator, and two-sided error bounds. After filtering out spurious eigenvalues and using outlier-robust estimators within a nested bootstrap framework, Lanczos estimators behave more like multistate fit results than effective masses—but without involving statistical fitting. We extend this formalism to the determination of matrix elements from three-point correlation functions and provide a physical picture of “spurious-state filtering” involving restriction to a Hermitian subspace. We demonstrate similar advantages for matrix elements as for spectroscopy through example applications to noiseless mock-data and (bare) forward matrix elements of the strange scalar current between both ground and excited states with the quantum numbers of the nucleon.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Optimizing the Heisenberg Vortex Tube for Hydrogen Cooling (Final Technical Report)

Hydrogen utilization at Plug Power sites ranges between 70-93% with the remainder vented and lost to the atmosphere. The goal of this project was to improve hydrogen utilization at Plug Power fulfillment centers via the patented Heisenberg vortex tube (HVT). The HVT combines conventional counterflow vortex tubes with para-orthohydrogen conversion catalyst to allow the cooling of hydrogen flows with no moving parts or external energy inputs. In year 1 of this project the HVT was evaluated to improve hydrogen utilization via the following concepts: 1) increasing liquid hydrogen pump volumetric efficiency by 20% through vapor separation and subcooling of the liquid, 2) reducing liquid hydrogen storage tank boil-off losses by 20% through thermal vapor shielding (TVS), and 3) increase isentropic efficiency of supercritical hydrogen expansion at 40-50 K, from 31% to over 40%. The TVS concept was selected as the most favorable application for further development. While the conceptual analysis was underway, the cryocatalysis hydrogen experiment facility (CHEF) was retrofitted with a new cryocooler, higher pressure condenser tanks, and in-situ fiber-optic Raman probes for ortho-parahydrogen composition analysis at the inlets and outlets of the HVT. To our knowledge this is the first in-situ implementation of cryogenic Raman probes for ortho-parahydrogen analysis. Subsequent testing of catalyzed and non-catalyzed HVT identified a low flow rate, high-conversion efficacy regime well suited for TVS development. Computational fluid dynamics (CFD), Reduced order modeling (ROM), and quantum Monte Carlo atomistic simulations were applied to optimize the design of the HVT implemented for TVS. The simulations and analysis identified a ruthenium-based catalyst as the most optimal for high conversion with little pressure losses and was matched to experimental measurements. These results indicated the design could achieve the 20% boil-off reduction target. An HVT field-trial was designed and constructed by Plug Power and implemented at a customer site. However, budget limitations reduced the amount of catalyst that could be applied to the HVT. With the reduced amount of catalyst, CFD analysis estimated about 3% reduction in boil-off. A similar amount of boil-off reduction was observed during preliminary measurements during tank commissioning. However, the results have a large margin for error and were operated at relatively low tank temperatures when the HVT has the lowest potential for improvement. This project demonstrated the use of para-orthohydrogen catalysis for reducing liquid hydrogen tank boil-off losses. The field trial system is anticipated to provide long-term experimental measurements on tank operational performance that will allow Plug Power to instrument additional tanks in the future. Recommendations for future work include the development of higher activity, lower cost para-orthohydrogen conversion catalyst for use in the HVT. Tank process optimizations could maximize the efficacy of the HVT and further improve hydrogen utilization.

08 HYDROGEN↗
DOE OSTI2024

Improving cosmological analyses of HI clustering by reducing stochastic noise

High-number-density tracers of large-scale structure, such as the HI-rich galaxies measured by 21 cm intensity mapping, have low sampling noise, making them particularly promising as cosmological probes. At large scales, this sampling noise can be subdominant to other scale-independent contributions to the power spectrum; such contributions arise from nonlinear bias, and exceed the sampling noise if at least one of the associated bias coefficients is sufficiently large. This has important consequences for cosmological constraints obtained from such tracers, since it indicates that using the power spectrum does not lead to optimal constraints even in the linear regime. In this paper, we provide a conservative estimate of the possible improvement in constraining power of a 21 cm survey if one were to use an optimal analysis strategy (such as field-level analysis), where only the true sampling noise enters the error budget. We find that improvements in uncertainties on some cosmological parameters can be as large as 50%, depending on redshift, foreground cleaning efficiency, scales used in the analysis, and instrumental noise. One byproduct of our work is measurements of bias parameters and stochasticity for neutral hydrogen in the IllustrisTNG simulation over a wide range of redshifts; we provide simple fitting formulas for these measurements. Furthermore, our results motivate further exploration of new optimal analysis techniques and provide important insights into the constraining power of current and future 21 cm surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

The DESI-Lensing Mock Challenge: large-scale cosmological analysis of 3x2-pt statistics

The current generation of large galaxy surveys will test the cosmological model by combining multiple types of observational probes. Realising the statistical promise of these new datasets requires rigorous attention to all aspects of analysis including cosmological measurements, modelling, covariance and parameter likelihood. In this paper we present the results of an end-to-end simulation study designed to test the analysis pipeline for the combination of the Dark Energy Spectroscopic Instrument (DESI) Year 1 galaxy redshift dataset and separate weak gravitational lensing information from the Kilo-Degree Survey, Dark Energy Survey and Hyper-Suprime-Cam Survey. Our analysis employs the 3x2-pt correlation functions including cosmic shear and galaxy-galaxy lensing, together with the projected correlation function of the spectroscopic DESI lenses. We build realistic simulations of these datasets including galaxy halo occupation distributions, photometric redshift errors, weights, multiplicative shear calibration biases and magnification. We calculate the analytical covariance of these correlation functions including the Gaussian, noise and super-sample contributions, and show that our covariance determination agrees with estimates based on the ensemble of simulations. We use a Bayesian inference platform to demonstrate that we can recover the fiducial cosmological parameters of the simulation within the statistical error margin of the experiment, investigating the sensitivity to scale cuts. This study is the first in a sequence of papers in which we present and validate the large-scale 3x2-pt cosmological analysis of DESI-Y1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

DESI DR2 Baryon Acoustic Oscillations from the Lyman Alpha Forest Multipoles

We present an alternative measurement of the Baryon Acoustic Oscillation (BAO) using the Legendre multipole representation of the Ly$α$ forest correlation functions from the second data release (DR2) of the Dark Energy Spectroscopic Instrument survey. Compressing the auto- and cross-correlation functions into Legendre multipoles yields a positive-definite covariance matrix without any smoothing -- unlike the baseline DR2 analysis -- thanks to a significantly reduced data vector size. We introduce the statistical corrections required to debias the finite-sample covariance matrix estimate and demonstrate that monopole and quadrupole terms for both auto- and cross-correlations can be used even when the correlation functions are distorted by continuum errors and contaminated by metals. This formalism has slightly diminished the constraining power of the BAO scale, while considerably weakening constraints on nuisance parameters. We measure the isotropic BAO scale with $0.93\%$ precision at $z_\mathrm{eff}=2.35$, the Hubble parameter $H(z_\mathrm{eff})=(239.5\pm3.4)~(147.09~\mathrm{Mpc}/r_d) ~\mathrm{km~s}^{-1}~\text{Mpc}^{-1}$, and the transverse comoving distance $D_M(z_\mathrm{eff})=(5.80 \pm 0.10)~(r_d/147.09~\mathrm{Mpc})$~Gpc for a given value of the sound horizon ($r_d$). Our BAO results are entirely consistent with the baseline DR2 analysis.

Karaçaylı, Naim Göksel [Chicago U., KICP; Ohio Sta↗
DOE OSTI2025

Precision measurements of EFT parameters and BAO peak shifts for the Lyman- α forest

We present precision measurements of the bias parameters of the one-loop power spectrum model of the Lyman- α (Ly- α ) forest, derived within the effective field theory (EFT) of large-scale structure. We fit our model to the three-dimensional flux power spectrum measured from the ACCEL 2 hydrodynamic simulations. The EFT model fits the data with an accuracy of below 2% up to k = 2 h Mpc − 1 . Further, we analytically derive how nonlinearities in the three-dimensional clustering of the Ly- α forest introduce biases in measurements of the baryon acoustic oscillations (BAOs) scaling parameters in radial and transverse directions. From our EFT parameter measurements, we obtain a theoretical error budget of Δ α ∥ = − 0.2 % ( Δ α ⊥ = − 0.3 % ) for the radial (transverse) parameters at redshift z = 2.0 . This corresponds to a shift of − 0.3 % (0.1%) for the isotropic (anisotropic) distance measurements. We provide an estimate for the shift of the BAO peak for Ly- α -quasar cross-correlation measurements assuming analytical and simulation-based scaling relations for the nonlinear quasar bias parameters resulting in a shift of − 0.2 % ( − 0.1 % ) for the radial (transverse) dilation parameters, respectively. This analysis emphasizes the robustness of Ly- α forest BAO measurements to the theory modeling. We provide informative priors and an error budget for measuring the BAO feature—a key science driver of the currently observing Dark Energy Spectroscopic Instrument (DESI). Our work paves the way for full-shape cosmological analyses of Ly- α forest data from DESI and upcoming surveys such as the Prime Focus Spectrograph, WEAVE-QSO, and 4MOST. Published by the American Physical Society 2025

de Belsunce, Roger (ORCID:0000000336604028)↗
DOE OSTI2024

Misclassification of causes of death among a small all-autopsied group of former nuclear workers: Death certificates vs. autopsy reports

The U.S. Transuranium and Uranium Registries performs autopsies on each of its deceased Registrants as a part of its mission to follow up occupationally-exposed individuals. This provides a unique opportunity to explore death certificate misclassification errors, and the factors that influence them, among this small population of former nuclear workers. Underlying causes of death from death certificates and autopsy reports were coded using the 10 th revision of the International Classification of Diseases (ICD-10). These codes were then used to quantify misclassification rates among 268 individuals for whom both full autopsy reports and death certificates with legible underlying causes of death were available. When underlying causes of death were compared between death certificates and autopsy reports, death certificates correctly identified the underlying cause of death’s ICD-10 disease chapter in 74.6% of cases. The remaining 25.4% of misclassified cases resulted in over-classification rates that ranged from 1.2% for external causes of mortality to 12.2% for circulatory disease, and under-classification rates that ranged from 7.7% for external causes of mortality to 47.4% for respiratory disease. Neoplasms had generally lower misclassification rates with 4.3% over-classification and 13.3% under-classification. A logistic regression revealed that the odds of a match were 2.8 times higher when clinical history was mentioned on the autopsy report than when it was not. Similarly, the odds of a match were 3.4 times higher when death certificates were completed using autopsy findings than when autopsy findings were not used. This analysis excluded cases where it could not be determined if autopsy findings were used to complete death certificates. The findings of this study are useful to investigate the impact of death certificate misclassification errors on radiation risk estimates and, therefore, improve the reliability of epidemiological studies.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗