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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Threshold $\pi ^-$ photoproduction on the neutron

Recent data from the PIONS@MAX-lab Collaboration, measuring the total cross section of the incoherent pion photoproduction reaction, γd → π – pp, near threshold, have been used to extract the E 0+ multipole and total cross section of the reaction γn → π – p, also near threshold. These are the first measurements of the reaction γd → π – pp in the threshold region. The value of E 0+ is extracted through a fit to the deuteron data in a photoproduction model accounting for final-state interactions. The model takes an S-wave approximation for the elementary reaction γn → π – p with E 0+ = const in the threshold region. The fit over all the 6 deuteron data points gives the value E 0+ = –31.86 ± 0.8 (in units 10 –3 /mπ). We explore the dependence of our results on the choice of data subsets included in the fit. Here, the obtained values of E 0+ , for different subsets, have overlapping errors and agree with previous determinations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC

The performance is presented of the reconstruction and identification algorithms for electrons and photons with the CMS experiment at the LHC. The reported results are based on proton-proton collision data collected at a center-of-mass energy of 13 TeV and recorded in 2016–2018, corresponding to an integrated luminosity of 136 fb^-1. Results obtained from lead-lead collision data collected at √(sNN)=5.02 TeV are also presented. Innovative techniques are used to reconstruct the electron and photon signals in the detector and to optimize the energy resolution. Events with electrons and photons in the final state are used to measure the energy resolution and energy scale uncertainty in the recorded events. The measured energy resolution for electrons produced in Z boson decays in proton-proton collision data ranges from 2 to 5%, depending on electron pseudorapidity and energy loss through bremsstrahlung in the detector material. The energy scale in the same range of energies is measured with an uncertainty smaller than 0.1 (0.3)% in the barrel (endcap) region in proton-proton collisions and better than 1 (3)% in the barrel (endcap) region in heavy ion collisions. The timing resolution for electrons from Z boson decays with the full 2016–2018 proton-proton collision data set is measured to be 200 ps.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Characterizing model uncertainties in simulated coast-to-offshore wind over the northeast U.S. using multi-platform measurements from the TCAP field campaign

Numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, are widely used to provide estimates of the offshore wind energy resource owing to their large spatial coverage compared to available observations. Nevertheless, spatiotemporal distribution of model biases is highly dependent on factors including model configuration, location, and the interplay of multi-scale physical processes. Here, in this study, we focus on the characterization of model uncertainties in simulated coast-to-offshore winds over the northeast U.S., by varying sea surface temperature (SST) forcings, surface layer (SL) and planetary boundary layer (PBL) parameterizations, as well as identifying biases that may be directly passed from initial and boundary conditions. Multiple measurements, including aircraft data collected during the U.S. Department of Energy's Two-Column Aerosol Project (TCAP) experiment, are used to constrain the model results and facilitate quantitative comparisons. Our analysis indicates while SST forcing has notable impacts on simulated air temperature and moisture within PBL, the modeled winds are in general more sensitive to the choices of SL and PBL physics than to SST. The model’s forcing data not only controls the vertical dependence of wind speed errors, but also alters regional variability in wind speed’s spatial correlation. Bias comparisons between ERA5 reanalysis and ensemble simulations revealed significant similarity, particularly in wind speed biases during winter, underscoring their dependency on initial and boundary conditions. Coastal and offshore near-surface wind speed biases tend to exhibit much higher similarity in winter than in summer due to the presence of much stronger and more persistent synoptic wind conditions. This study highlights the importance of accurate atmospheric forcing and parameterization choices in improving wind forecasts and suggests the potential for extrapolating coastal wind biases to offshore locations, aiding wind energy forecasting and informing the Wind Forecast Improvement Project-3 (WFIP3).

17 WIND ENERGY↗
DOE OSTI2023

Tomography of pions and protons via transverse momentum dependent distributions

We perform the first simultaneous extraction of parton collinear and transverse degrees of freedom from low-energy fixed-target Drell-Yan data in order to compare the transverse momentum dependent (TMD) parton distribution functions (PDFs) of the pion and proton. We demonstrate that the transverse separation of the quark field encoded in TMDs of the pion is more than 4⁢𝜎 smaller than that of the proton. Additionally, we find the transverse separation of the quark field decreases as its longitudinal momentum fraction decreases. In studying the nuclear modification of TMDs, we find clear evidence for a transverse EMC effect. We comment on possible explanations for these intriguing behaviors, which call for a deeper examination of tomography in a variety of strongly interacting quark-gluon systems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

The Sylvester Graphical Lasso (SyGlasso)

This paper introduces the Sylvester graphical lasso (SyGlasso) that captures multiway dependencies present in tensor-valued data. The model is based on the Sylvester equation that defines a generative model. The proposed model complements the tensor graphical lasso that imposes a Kronecker sum model for the inverse covariance matrix by providing an alternative Kronecker sum model that is generative and interpretable. A nodewise regression approach is adopted for estimating the conditional independence relationships among variables. The statistical convergence of the method is established, and empirical studies are provided to demonstrate the recovery of meaningful conditional dependency graphs. Here we apply the SyGlasso to an electroencephalography (EEG) study to compare the brain connectivity of alcoholic and nonalcoholic subjects. Here, we demonstrate that our model can simultaneously estimate both the brain connectivity and its temporal dependencies.

Wang, Yu↗
DOE OSTI2024

The Atacama Cosmology Telescope: A Measurement of the DR6 CMB Lensing Power Spectrum and Its Implications for Structure Growth

We present new measurements of cosmic microwave background (CMB) lensing over 9400 deg of the sky. These lensing measurements are derived from the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) CMB data set, which consists of five seasons of ACT CMB temperature and polarization observations. We determine the amplitude of the CMB lensing power spectrum at 2.3% precision (43σ significance) using a novel pipeline that minimizes sensitivity to foregrounds and to noise properties. To ensure that our results are robust, we analyze an extensive set of null tests, consistency tests, and systematic error estimates and employ a blinded analysis framework. Our CMB lensing power spectrum measurement provides constraints on the amplitude of cosmic structure that do not depend on Planck or galaxy survey data, thus giving independent information about large-scale structure growth and potential tensions in structure measurements. The baseline spectrum is well fit by a lensing amplitude of A lens = 1.013 ± 0.023 relative to the Planck 2018 CMB power spectra best-fit ΛCDM model and A lens = 1.005 ± 0.023 relative to the ACT DR4 + WMAP best-fit model. From our lensing power spectrum measurement, we derive constraints on the parameter combination $S^{CMBL}_{8}$ ≡ σ 8 (Ω m /0.3) 0.25 of $S^{CMBL}_{8}$ = 0.818 ± 0.022 from ACT DR6 CMB lensing alone and $S^{CMBL}_{8}$ = 0.813 ± 0.018 when combining ACT DR6 and Planck NPIPE CMB lensing power spectra. These results are in excellent agreement with ΛCDM model constraints from Planck or ACT DR4 + WMAP CMB power spectrum measurements. Our lensing measurements from redshifts z ~ 0.5–5 are thus fully consistent with ΛCDM structure growth predictions based on CMB anisotropies probing primarily z ~ 1100. We find no evidence for a suppression of the amplitude of cosmic structure at low redshifts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Estimating mass-absorption cross-section of ambient black carbon aerosols: theoretical, empirical, and machine learning models

The mass-absorption cross-section of black carbon (MAC BC ) is an essential parameter to link the atmospheric concentration of black carbon (BC) with its radiative forcing. When a direct calculation of MAC BC based on observations of aerosol light absorption and BC mass concentration is impossible, we rely on modeling and simulations to estimate MAC BC , but currently, there is no consensus model that can be relied on for accurate predictions across all atmospheric environments when BC particles have different coating thicknesses. Here, we applied five MAC BC prediction models (including three light scattering theories, an empirical model based on observations of particle mass concentrations, and a machine learning model developed in our previous work) to aerosols from three Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) field campaigns. While many studies have found that increasing the complexity of the models helps to constrain biases of the estimated MAC BC , our effort is to evaluate the models based on the criteria of simplicity and accuracy. We find that our machine learning model (support vector machine for regression, SVM) generally performs well across all DOE ARM field campaign data, while the accuracy of core-shell Mie theory depends on the bias correction algorithm applied to filter-based light absorption data. Generally, the empirical model for internally-mixed particles that we considered tends to over-predict MAC BC , while Mie theory for externally-mixed particles tends to under-predict MAC BC . An examination of the influence of coating material on BC cores suggests that the performance of our current SVM model is degraded when the BC is thickly-coated (e.g., it has undergone aging and mixing with other materials in the atmosphere).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Search for Multi-flare Neutrino Emissions in 10 yr of IceCube Data from a Catalog of Sources

A recent time-integrated analysis of a catalog of 110 candidate neutrino sources revealed a cumulative neutrino excess in the data collected by IceCube between 2008 April 6 and 2018 July 10. This excess, inconsistent with the background hypothesis in the Northern Hemisphere at the 3.3σ level, is associated with four sources: NGC 1068, TXS 0506+056, PKS 1424+240, and GB6 J1542+6129. Here, this Letter presents two time-dependent neutrino emission searches on the same data sample and catalog: a point-source search that looks for the most significant time-dependent source of the catalog by combining space, energy, and time information of the events, and a population test based on binomial statistics that looks for a cumulative time-dependent neutrino excess from a subset of sources. Compared to previous time-dependent searches, these analyses enable a feature to possibly find multiple flares from a single direction with an unbinned maximum-likelihood method. M87 is found to be the most significant time-dependent source of this catalog at the level of 1.7σ post-trial, and TXS 0506+056 is the only source for which two flares are reconstructed. The binomial test reports a cumulative time-dependent neutrino excess in the Northern Hemisphere at the level of 3.0σ associated with four sources: M87, TXS 0506+056, GB6 J1542+6129, and NGC 1068.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Multiscale Flow for robust and optimal cosmological analysis

We propose Multiscale Flow, a generative Normalizing Flow that creates samples and models the field-level likelihood of two-dimensional cosmological data such as weak lensing. Multiscale Flow uses hierarchical decomposition of cosmological fields via a wavelet basis and then models different wavelet components separately as Normalizing Flows. The log-likelihood of the original cosmological field can be recovered by summing over the log-likelihood of each wavelet term. This decomposition allows us to separate the information from different scales and identify distribution shifts in the data such as unknown scale-dependent systematics. The resulting likelihood analysis can not only identify these types of systematics, but can also be made optimal, in the sense that the Multiscale Flow can learn the full likelihood at the field without any dimensionality reduction. We apply Multiscale Flow to weak lensing mock datasets for cosmological inference and show that it significantly outperforms traditional summary statistics such as power spectrum and peak counts, as well as machine learning–based summary statistics such as scattering transform and convolutional neural networks. We further show that Multiscale Flow is able to identify distribution shifts not in the training data such as baryonic effects. Finally, we demonstrate that Multiscale Flow can be used to generate realistic samples of weak lensing data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Automated Resonance Fitting for Nuclear Data Evaluation

Global and national efforts to deliver high-quality nuclear data to users have a wide-ranging impact, affecting applications in national security, reactor operations, basic science, medicine, and more. Cross section evaluation is a major part of this effort, combining theory and experimentation to produce recommended values and uncertainties for reaction probabilities. Resonance region evaluation is a specialized type of nuclear data evaluation that can require significant manual effort and months of time from expert scientists. In this article, non-convex non-linear optimization methods are combined with concepts of inferential statistics to infer a resonance model from experimental data in an automated manner that is not dependent on prior evaluation(s). This methodology aims to enhance the workflow of a resonance evaluator by minimizing time, effort, and the potential for bias from prior assumptions, while enhancing reproducibility and documentation, thereby addressing well-known challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

A Twenty-Year Analysis of Winds in California for Offshore Wind Energy Production Using WRF v4.1.2

Offshore wind resource characterization in the United States relies heavily on simulated winds from numerical weather prediction (NWP) models, given the lack of hub-height observations offshore. One such NWP data set used extensively by U.S. stakeholders is the Wind Integration National Dataset (WIND) Toolkit, a 7-year time-series data set produced in 2013 by the National Renewable Energy Laboratory. In this study, we present an update to that data set for offshore California that leverages recent advancements in NWP modeling capabilities and extends the period of record to a full 20 years. The data set predicts a significantly larger wind resource (0.25–1.75 m s-1 stronger), including in three Call Areas that the Bureau of Ocean Energy Management is considering for commercial activity. We conduct a set of yearlong simulations to study factors that contribute to this increase in the modeled wind resource. The largest impact arises from a change in the planetary boundary layer parameterization from the Yonsei University scheme to the Mellor-Yamada-Nakanishi-Niino scheme and their diverging wind profiles under stable stratification. Additionally, we conduct a refined wind resource assessment at the three Call Areas, characterizing distributions of wind speed, shear, veer, stability, frequency of wind droughts, and power production. We find that, depending on the attribute, the new data set can show substantial disagreement with the WIND Toolkit, thereby driving important changes in predicted power.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2025

Predicting the von Neumann entanglement entropy using a graph neural network

Calculating the von Neumann entanglement entropy from experimental data is challenging due to its dependence on the complete wavefunction, forcing reliance on approximations such as classical mutual information (MI). We propose a machine learning approach using a graph neural network to predict the von Neumann entropy directly from experimentally accessible bitstrings. We test this approach on a Rydberg ladder system and achieve a mean absolute error of $3.6\,\times 10^{-3}$ when evaluating within the training range on a dataset with entropy values ranging from 0 to 1.9. The model achieves a mean absolute percentage error of 1.44% and outperforms MI-based bounds. When tested beyond the training range, the model maintains reasonable accuracy. Furthermore, we demonstrate that fine-tuning the model with small datasets significantly improves performance on data outside the original training range.

graph neural networks↗
DOE OSTI2024

Reactor antineutrino flux and anomaly

Reactor antineutrinos have played a significant role in establishing the standard model of particle physics and the theory of neutrino oscillations. In this article, we review the reactor antineutrino flux and in particular the reactor antineutrino anomaly (RAA) coined over a decade ago. RAA refers to a deficit of the measured antineutrino inverse beta decay rates at very short-baseline reactor experiments compared to the theoretically improved predictions (i.e. the Huber–Mueller model). Since the resolution of several previous experimental anomalies have led to the discovery of non-zero neutrino mass and mixing, many efforts have been invested to study the origin of RAA both experimentally and theoretically. The progress includes the observation of discrepancies in antineutrino energy spectrum between data and the Huber–Mueller model, the re-evaluation of the Huber–Mueller model uncertainties, the potential isotope-dependent rate deficits, and the better agreement between data and new model predictions using the improved summation method. Importantly, these developments disfavor the hypothesis of a light sterile neutrino as the explanation of RAA and supports the deficiencies of Huber–Mueller model as the origin. Looking forward, more effort from both the theoretical and experimental sides is needed to fully understand the root of RAA and to make accurate predictions of reactor antineutrino flux and energy spectrum for future discoveries.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Physical Interpretation of Early Battery Life Prediction Models

Early battery life prediction models are most useful for R&D if they help us understand the early changes in battery electrochemical response that correspond with long-term degradation and failure. Linear regression models such as Fused lasso and Partial Least Squares can fit coefficients directly to high-dimensional electrochemical data like capacity-voltage and ΔV–state-of-charge, i.e., Q(V) and ΔV(SOC) curves, learning coefficients that can be physically interpreted. We leverage the ISU-ILCC battery aging data set to learn high-dimensional coefficients for early battery life prediction from traditional slow-rate capacity check data, demonstrating learning on Q(V), d Q· d V −1 , and ΔV(SOC) curves. A thorough study on the dependence of coefficient values on train/test size and data preprocessing methods is made, demonstrating the reliability of high-dimensional regression approaches unless very small amounts of data are used for model training. For this data set, coefficients from Q(V) and d Q· d V −1 models highlight changes in electrode stoichiometry due to lithium loss, while ΔV(SOC) coefficients highlight changes in positive electrode diffusivity due to particle cracking as well as electrode stoichiometry shifts. By directly interpreting the coefficients of a regression model, we make physical insights into battery degradation mechanisms without requiring the assumptions of traditional battery data analysis methods.

25 ENERGY STORAGE↗
DOE OSTI2024

Leveraging BERT and Network-Based Attention Analysis for Identifying Treatment Milestones in EHRs

This study introduces a sophisticated data-driven framework for analyzing Electronic Health Records (EHRs) using transformer-based models to identify and disentangle overlapping treatment contexts. The framework leverages a preprocessing pipeline that transforms structured procedural codes into semantically enriched descriptive text, enabling the use of attention mechanisms to cluster medical events into treatment milestones—cohesive and distinct components of care processes. The methodology is rigorously validated using synthetic datasets derived from the MIMIC-III database, designed to simulate the heterogeneity and overlapping procedural contexts characteristic of real-world EHR scenarios. Quantitative evaluation highlights the framework’s robustness in disentangling concurrent care pathways, with attention metrics and unsupervised clustering approaches demonstrating the ability to preserve intra-context relationships while distinguishing inter-context dependencies. By addressing challenges inherent in data heterogeneity, this approach provides a foundation for uncovering complex treatment patterns, advancing clinical decision-making, and optimizing resource allocation in diverse healthcare environments.

Kim, Minsu [ORNL] (ORCID:0000000224185535)↗
DOE OSTI2024

Energy Dependent Fission Product Yields (2nd year Milestone and Deliverables)

Introduction to the project: One of the Laboratory missions is to provide consistent, high-precision fission product; yield data critical for testing fission models and maintaining the safety and security of the nation’s nuclear weapons stockpile. However, high-quality, energy-differential fission product yield data is missing for certain actinides and neutron energies important to constrain the new U.S. Nuclear Data Program evaluation effort; It has been shown that the reactor antineutrino anomaly may be at least partially caused by roughly 20 fission products. The fission product yield data is missing or incomplete for many of these isotopes, thus it is necessary to accurately determine these values to better constrain the anomaly; Aitor Bracho is measuring very short-lived (seconds to minutes) fission product yields of 235 U and 239 Pu using monoenergetic neutron beams at E n = 60 and 560 keV; Aitor Bracho is using a direct approach utilizing a state-of-the-art rabbit transfer system, superior HPGe detector, and digital acquisition systems for fission decay measurements. The goals of this project: lop experimental capabilities and data analysis techniques to carry out the gamma-ray spectra analysis necessary for fission product yield calculation; Provide high-precision and energy-dependent fission product data supporting fission theory, neutrino physics, and applied physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Comparing Capacity Credit Calculations for Wind: A Case Study in Texas

The degree to which wind energy can contribute to the capacity needed to meet resource adequacy requirements, also known as capacity credit (CC), varies regionally with wind resource and correlation to net load. CC is an important metric widely used for resource planning and resource adequacy assessments. However, there are multiple methods for computing and estimating CC, depending on specific needs, access to data, and computational burden. It is unclear the extent to which the CC computation method may influence the result. To address this, we use a probabilistic resource adequacy tool and multiple approximation methods to systematically assess the CC of wind for near-term wind deployment under a case study in Texas. We find that proper consideration of transmission constraints is important; some approximation methods may overestimate the CC of wind due to a lack of consideration of transmission constraints, while other approximation methods may underestimate the CC by not capturing the ability of wind to be shipped to neighboring regions. In this case study, we find that several approximation methods do come close to the CC calculated by more robust probabilistic methods. However, the best approximation method may vary on a case-by-case basis, depending on system-specific considerations.

17 WIND ENERGY↗
DOE OSTI2026

Evaluating the limitations of Bayesian metabolic control analysis

Bayesian Metabolic Control Analysis (BMCA) is a promising framework for inferring metabolic control coefficients in data-limited scenarios, combining Bayesian inference with linear-logarithmic (lin-log) rate laws. These metabolic control coefficients quantify how changes in enzyme activities affect steady-state fluxes and metabolite concentrations across a metabolic network. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCA’s ability to infer elasticity values, flux control coefficients (FCC), and concentration control coefficients (CCC) under varying data availability conditions using three synthetic metabolic network models. We demonstrate that BMCA predictions are highly dependent on the inclusion of flux and enzyme concentration data, with the omission of these datasets leading to severe inaccuracies. In our synthetic, enzyme-perturbation datasets, external metabolite concentrations had minimal impact and, in some cases, their exclusion improved predictions; when external-nutrient perturbations were introduced and those concentrations were observed, gains were at most modest. Additionally, we find that posterior estimation with both ADVI and HMC can underestimate large-magnitude elasticities in our synthetic settings, with ADVI showing somewhat higher variance under strong up-regulation; thus, recovering |elasticity| ≳ 1.5 remains challenging regardless of the inference engine. ADVI also fails to accurately infer allosteric interactions, even when regulatory effects are strong. While BMCA maintains reasonable accuracy in partially recovering the rankings of the highest FCC values, its estimates of absolute values remain constrained by prior assumptions and data limitations. Our findings reveal the BMCA algorithm’s strengths and weaknesses, providing guidance on its application in metabolic engineering, and highlighting the need for methodological refinements to enhance its predictive capabilities.

59 BASIC BIOLOGICAL SCIENCES↗