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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 OSTI2022

Transitioning from Stage-III to Stage-IV: cosmology from galaxy×CMB lensing and shear×CMB lensing

ABSTRACT We examine the cosmological constraining power from two cross-correlation probes between galaxy and cosmic microwave background (CMB) surveys: the cross-correlation of lens galaxy density with CMB lensing convergence 〈δgκCMB〉, and source galaxy weak lensing shear with CMB lensing convergence 〈γκCMB〉. These two cross-correlation probes provide an independent cross-check of other large-scale structure constraints and are insensitive to galaxy-only or CMB-only systematic effects. In addition, when combined with other large-scale structure probes, the cross-correlations can break degeneracies in cosmological and nuisance parameters, improving both the precision and robustness of the analysis. In this work, we study how the constraining power of 〈δgκCMB〉 + 〈γκCMB〉 changes from Stage-III (ongoing) to Stage-IV (future) surveys. Given the flexibility in selecting the lens galaxy sample, we also explore systematically the impact on cosmological constraints when we vary the redshift range and magnitude limit of the lens galaxies using mock galaxy catalogs. We find that in our setup, the contribution to cosmological constraints from 〈δgκCMB〉 and 〈γκCMB〉 are comparable in the Stage-III data sets; but in Stage-IV surveys, the noise in 〈δgκCMB〉 becomes subdominant to cosmic variance, preventing 〈δgκCMB〉 to further improve the constraints. This implies that to maximize the cosmological constraints from future 〈δgκCMB〉 + 〈γκCMB〉 analyses, we should focus more on the requirements on 〈γκCMB〉 instead of 〈δgκCMB〉. Furthermore, the selection of the lens sample should be optimized in terms of our ability to characterize its redshift or galaxy bias instead of its number density.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Statistical Uncertainty in Paleoclimate Proxy Reconstructions

A quantitative analysis of any environment older than the instrumental record relies on proxies. Uncertainties associated with proxy reconstructions are often underestimated, which can lead to artificial conflict between different proxies, and between data and models. In this paper, using ordinary least squares linear regression as a common example, we describe a simple, robust and generalizable method for quantifying uncertainty in proxy reconstructions. We highlight the primary controls on the magnitude of uncertainty, and compare this simple estimate to equivalent estimates from Bayesian, nonparametric and fiducial statistical frameworks. We discuss when it may be possible to reduce uncertainties, and conclude that the unexplained variance in the calibration must always feature in the uncertainty in the reconstruction. This directs future research toward explaining as much of the variance in the calibration data as possible. We also advocate for a “data-forward” approach, that clearly decouples the presentation of proxy data from plausible environmental inferences.

58 GEOSCIENCES↗
DOE OSTI2026

SPT-3G D1: Compton-$y$ maps using data from the SPT-3G and Planck surveys

We present thermal Sunyaev-Zel'dovich (tSZ) Compton-$y$ parameter maps constructed from two years (2019-2020) of observations with the South Pole Telescope (SPT) third-generation camera, SPT-3G, combined with data from the Planck satellite. Using a linear combination (LC) pipeline, we obtain a suite of reconstructions that explore different trade-offs between statistical sensitivity and suppression of astrophysical contaminants, including minimum-variance, CMB-deprojected, and CIB-deprojected $y$-maps. We validate these maps through different statistical techniques such as auto- and cross-power spectra with large-scale structure tracers as well as stacking on cluster locations. These tests are used to understand the balance between noise and astrophysical foreground residuals (such as the CIB) in combination with the recovery of the tSZ signal for different maps. For example, results from stacking at the location of clusters confirm the robustness of the recovered tSZ signal over the $\sim 1500\: {\rm deg}^2$ SPT-3G survey field used in this analysis. The high-resolution and low-noise maps produced here provide an important cosmological tool for future studies, including measurements of the Compton-$y$ map power spectrum, cross-correlations with other tracers of the large-scale structure, detailed modeling of cluster pressure profiles, and study of the thermodynamic state of the baryons in the Universe.

Maniyar, A. S. [Harvard-Smithsonian Ctr. Astrophys↗
DOE OSTI2022

SDSS-IV MaNGA: Exploring the Local Scaling Relations for N/O

We present, for the first time, the relationship between local stellar mass surface density, Σ * , and N/O derived from SDSS-IV MaNGA data, using a sample of 792,765 high signal-to-noise ratio star-forming spaxels. Using a combination of phenomenological modeling and partial correlation analysis, we find that Σ * alone is insufficient to predict the N/O in MaNGA spaxels and that there is an additional dependence on the local star formation rate surface density, Σ SFR . This effect is a factor of 3 stronger than the dependence of 12+log(O/H) on Σ SFR . Surprisingly, we find that the local N/O scaling relations also depend on the total galaxy stellar mass at fixed Σ * and the galaxy size at fixed stellar mass. We find that more compact galaxies are more nitrogen rich, even when Σ * and Σ SFR are controlled for. We show that ~50% of the variance of N/O is explained by the total stellar mass and size. Thus, the evolution of nitrogen in galaxies is set by more than just local effects and does not simply track the buildup of oxygen in galaxies. The precise form of the N/O–O/H relation is therefore sensitive to the sample of galaxies from which it is derived. This result casts doubt on the universal applicability of nitrogen-based strong-line metallicity indicators derived in the local universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

BEYONDPLANCK IV. On end-to-end simulations in CMB analysis — Bayesian versus frequentist statistics

End-to-end simulations play a key role in the analysis of any high-sensitivity cosmic microwave background (CMB) experiment, providing high-fidelity systematic error propagation capabilities that are unmatched by any other means. In this paper, we address an important issue regarding such simulations, namely, how to define the inputs in terms of sky model and instrument parameters. These may either be taken as a constrained realization derived from the data or as a random realization independent from the data. We refer to these as posterior and prior simulations, respectively. We show that the two options lead to significantly different correlation structures, as prior simulations (contrary to posterior simulations) effectively include cosmic variance, but they exclude realization-specific correlations from non-linear degeneracies. Consequently, they quantify fundamentally different types of uncertainties. We argue that as a result, they also have different and complementary scientific uses, even if this dichotomy is not absolute. In particular, posterior simulations are in general more convenient for parameter estimation studies, while prior simulations are generally more convenient for model testing. Before BEYONDPLANCK, most pipelines used a mix of constrained and random inputs and applied the same hybrid simulations for all applications, even though the statistical justification for this is not always evident. BEYONDPLANCK represents the first end-to-end CMB simulation framework that is able to generate both types of simulations and these new capabilities have brought this topic to the forefront. The BEYONDPLANCK posterior simulations and their uses are described extensively in a suite of companion papers. In this work, we consider one important applications of the corresponding prior simulations, namely, code validation. Specifically, we generated a set of one-year LFI 30 GHz prior simulations with known inputs and we used these to validate the core low-level BEYONDPLANCK algorithms dealing with gain estimation, correlated noise estimation, and mapmaking.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Traffic safety analysis and model updating for freeways using Bayesian method

Freeway crash prediction models are the basic of traffic safety research, yet crash occurrence and the influencing factors change over time. In order to make sure the implemented safety models fit the current traffic environment, this study conducts a comparative analysis of 2017 and 2020 datasets collected from freeways in Suzhou, China. Herein, considering the spatial correlation among analysis units and the hierarchical data structure, a Bayesian conditional autoregressive negative binomial (CAR-NB) model and a Bayesian hierarchical CAR-NB (HCAR-NB) model were used to explore the safety influencing factors, and a traditional NB model was developed for further comparison. To update the HCAR-NB model from 2017 to 2020, Bayesian inference with informative priors was used to improve its goodness of fit and efficiency. Preliminary results showed that 1) the HCAR-NB model outperformed the NB model and CAR-NB model in prediction accuracy, and 2) the number of crashes was significantly correlated with average speed, speed variance, road segment length, number of lanes, and presence of ramps. The potential for safety improvement (PSI) method was applied to the modeling results to identify hotspots for the two years. The results confirmed that the hotspots spatiotemporally shifted among the freeways. The proposed crash prediction model and updating method are expected to assist implementation of informed countermeasures for freeway safety improvement.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations

People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has compared human visual inference to that of an optimal Bayesian agent, with deviations from rational analysis viewed as problematic. However, human reliance on non-normative heuristics may prove advantageous in certain circumstances. We investigate scenarios where human intuition might surpass idealized statistical rationality. In two experiments, we examine individuals’ accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings indicate that, although participants generally exhibited lower accuracy compared to statistical models, they frequently outperformed Bayesian agents, particularly when faced with extreme samples. Participants appeared to rely on their internal models to filter out noisy visualizations, thus improving their resilience against spurious data. However, participants displayed overconfidence and struggled with uncertainty estimation. They also exhibited higher variance than statistical machines. Our findings suggest that analyst gut reactions to visualizations may provide an advantage, even when departing from rationality. These results carry implications for designing visual analytics tools, offering new perspectives on how to integrate statistical models and analyst intuition for improved inference and decision-making. The data and materials for this paper are available at https://osf.io/qmfv6

human-machine collaboration↗
DOE OSTI2026

A Bayesian inferencing framework for ultrasound wave speed measurements in metal additive manufacturing

Process-related changes during metal additive manufacturing introduce microstructural variability in the material properties of printed parts, directly affecting component reliability. Accurate estimation of these property variations with part performance are essential for quality assurance. Ultrasound testing offers a non-destructive means to estimate mechanical properties and detect defects; however, conventional analysis methods often neglect the influence of microstructural variability, limiting their effectiveness. Here, this research presents a Bayesian inference technique for quantifying wave speed uncertainty from ultrasound measurements of metal additive manufactured parts. By integrating prior ultrasound data with a Bayesian model, the proposed approach generates posterior density estimates of wave speed that systematically account for manufacturing-induced variability and uncertainty. The novelty of this research lies in applying a Bayesian framework to analyze experimental ultrasound measurements within the context of metal additive manufacturing variability. The method enhances the accuracy of wave speed estimation by 64%, defect position by 50% and increases confidence associated with wave speed variance by 30% across different porosity levels, thereby providing a robust foundation for improved decision-making and increased reliability in additively manufactured components.

Additive manufacturing↗
DOE OSTI2024

Autoregressive distributed lag-based dynamic uniformity modeling and monitoring approaches for superconductor manufacturing

High-temperature superconductors (HTS), known for their high efficiency and low energy loss, have found profound applications across various fields, driving the demand for long, uniformly performing tapes. However, ensuring uniform performance over extended lengths of HTS tapes, often characterized by the consistency of critical current, remains challenging due to fluctuations in growth conditions during manufacturing. Here, to elucidate the mechanisms underlying variations in tape uniformity and enable real-time monitoring of associated parameters, we propose an Autoregressive Distributed Lag (ADL)-based Dynamic Uniformity Modeling and Monitoring (ADUM2) approach. This method integrates uniformity measurement, the identification of critical process parameters and real-time monitoring within the manufacturing process. The ADUM2 approach is applied to the advanced metal organic chemical vapor deposition (A-MOCVD) process, a pilot-scale method for superconductor manufacturing. Our model demonstrates superior performance compared to benchmark methods, accounting for over 80% of the total variance in the data and identifying 13 key process parameters influencing the uniformity of HTS tapes. This study offers significant insights into the high-temperature superconductor manufacturing process and holds the potential to facilitate the production of cost-effective, uniformly performing long superconducting tapes in the future.

autoregressive distributed lag analysis↗
DOE OSTI2025

Analytical Model for the Higher Order Moments of Midlatitude Atmospheric Temperature Distributions

Observed distributions of atmospheric temperature are non-Gaussian. Therefore, moments beyond variance are necessary in determining the frequency of extreme temperature events. Here we propose a simple kinematic model for atmospheric mid-latitude temperature variability based on symmetric advection from a non-symmetric background temperature profile. We then use this model to derive analytical expressions for the higher order moments of temperature distributions. Our results show that nonzero skewness and kurtosis arise due to the nonlinearity of the time-mean meridional temperature profile. The analytical model matches an idealized Held-Suarez atmospheric model, indicating nonlinearity of time-mean temperature in latitude is the dominant contribution to nonzero skewness and kurtosis in synoptic temperature variations. Model analysis further shows decrease in higher order moments due to climate change come roughly equally from changes in mixing length and changes in the background temperature profiles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Advancing Development of Emissions Detection (Final Report)

This document is the final report to the U.S. Department of Energy (DOE for contract DE-FE0031873) awarded to Colorado State University (CSU). CSU and partners at Harrisburg University of Science and Technology, University of Texas Arlington, and University of Texas at Austin organized several testing rounds to provide knowledge focused on advancing the detection capabilities of emissions monitoring devices. With this funding opportunity the research group began by establishing the Advancing the Development of Emission Detection (ADED) program, with the goal focused on enhancing the accuracy, reliability, and field applicability of methane detection technologies. This effort aimed to address the critical challenges of identifying and quantifying methane emissions while enabling industry stakeholders to meet regulatory compliance and environmental sustainability goals. The program engaged with industry, government, and technology stakeholders to promote adoption and consensus on testing techniques for methane detection solutions. Methane, a potent greenhouse gas, contributes significantly to global warming, and the oil and natural gas (O&G) sector is a primary source of methane emissions. Regulatory measures such as leak detection and repair (LDAR) programs have been implemented to address emissions. However, traditional LDAR approaches, reliant on handheld and component-level measurements, are resource-intensive. To address these limitations and move with evolving regulations, advanced methane technologies are emerging. These solutions include ground-based sensors, mobile systems (e.g., drones, vehicles, and aircraft), and satellite-based platforms. They offer innovative capabilities for autonomous monitoring, larger spatial coverage, and emission quantification using methods such as tracer gas techniques and inverse modeling with Gaussian plume analysis. The ADED program began with creating protocols for methane controlled release (CR) testing these continuous monitoring (CM) and survey technologies that detect and monitor methane emissions at O&G facilities. The protocols were then implemented throughout testing of CM and survey devices at CSU’s Methane Emissions Technology Evaluation Center (METEC) facility from 2021 through 2024. As apart of the protocol, solutions that tested under the ADED program installed their solutions at METEC, documented their system under test, and provided detection reports to the METEC team for analysis. The METEC team would provide the solutions with analyzed reports of their emissions and ground truth data of the releases conducted during their testing session. Under the ADED program, CMs were also tested at O&G facilities for a six week test run of challenge release (ChR) releases. The findings from the ADED program underscore the critical role of collaborative research and innovation in tackling methane emissions, offering a pathway for the oil and gas sector to achieve significant environmental and economic benefits. Results from METEC testing saw improvement of performance and accuracy across all solutions over the extent of the ADED experiments. The results also showed a variance in CM solution performance between CRs and ChRs. That variance pushed the team to further analyze the differences between CR testing environments and field conditions. With the drive from regulations and that variance in field conditions, the ADED team began designing a new CR testing protocol and additions to the METEC testing facility. The METEC team is furthering the progress made through the ADED program with awarded funding from DE-FE0032276. This funding pushes the development of METEC’s addition with new equipment, allowing for an updated facility layout. METEC still facilitates for traditional facilities, with a legacy pad, while expanding an new design based on how O&G infrastructure has Final Report - Contract Number: DE-FE0031873 been changed over the last decade. Throughout the ADED program the team has also been working with international partners to ensure staying in the trend globally. International partners have been essential in moving the new protocol forward to implement into CR testing at the METEC facility in Spring 2025.

42 ENGINEERING↗
DOE OSTI2022

Understanding Air-Sea Feedbacks to the MJO through Process Evaluation of Observations and Models (Final Report)

The goal of our project was to enhance our understanding of the role of coupling in the simulation of the Madden Julian Oscillation (MJO) in coupled climate models. We developed diagnostics of precipitation and Sea Surface Temperature variability in climate models and investigated how they are related to metrics of MJO simulation performance. We found a relationship between models with a high spatial coherence of precipitation and a good propagation of the MJO through the Maritime Continent region, but no relationship between metrics of precipitation organisation and intensity. Analysis of coupled and uncoupled model simulations from the Sixth Coupled Model Intercomparison Project (CMIP6) shows that coupling systematically improved the MJO simulation in both metrics that we investigated. We analysed the role of background moisture gradients in the MJO simulation performance following Lim et al. (2018) and found that although the model to model variation in moisture gradients to the west of Indonesia was related to model MJO performance, in general no single moisture gradient was as good at explaining a large fraction of MJO performance across the models as compared to a linear combination of those metrics, which was able to explain a significant fraction of the variance in the MJO performance. Those relationships were much stronger for coupled simulations than uncoupled simulations and stronger for those metrics that measured propagation of the MJO across the Maritime Continent than for overall MJO activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Analysis of Second Target Station Target Removal Dose Rates

The Second Target Station (STS) project at Oak Ridge National Laboratory’s spallation neutron source is a crucial initiative for maintaining U.S. leadership in neutron sciences. The STS aims to create the world’s brightest pulsed cold neutron source, enabling cutting-edge research across various scientific disciplines. To ensure safe and efficient maintenance operations, understanding the effects of shutdown dose rates from activated components within the STS target systems is essential. This study establishes a computational framework for calculating decay gamma sources and subsequent shutdown dose rates utilizing advanced methods to account for all activation channels, including high-energy interactions down to thermal neutron capture. This study describes a novel integration of multiple tools and provides an effective means of analyzing activation and shutdown dose rates at spallation neutron facilities. A custom-developed script automates the decay gamma source generation process, ensuring proper sampling during the variance reduction phase, which is critical for accurate predictions of shutdown dose rates.

Transmutation↗
DOE OSTI2020

Improved Parametric Models for Explosion Pressure Signals Derived From Large Datasets

Accurate recording and characterization of explosion-induced pressure signals are key components of the forensic analysis of explosion events in the atmosphere. Parametric overpressure models based on several key waveform features (peak overpressure, positive pulse duration, and impulse) are widely used to estimate explosion energy in terms of trinitrotoluene equivalent yield. However, those models are often developed by a limited dataset, including only a few events or recordings at relatively short propagation distances. Here, we develop empirical waveform-parameter models based on a regression analysis of a large set of data curated from four chemical explosion experiments including 16 detonations. We measured peak overpressure and impulse for positive and negative phases from 1000 pressure signals recorded at local ranges (<20 km) with scaled distance up to 8000 m/kg 1/3 . Additionally, the measured waveform parameters showed large variation with respect to observing distances indicating the effects of atmospheric propagation. In this study, a second-order polynomial model was used in a least-squares regression to account for those propagation effects and to improve data fitting. In addition to model parameters for waveform features, we also determined range-dependent model uncertainties based on data variance. The model uncertainty represents the prediction error of our models and can be critical to evaluating the uncertainty of yield estimate.

58 GEOSCIENCES↗
DOE OSTI2021

Multimodal analysis for human ex vivo studies shows extensive molecular changes from delays in blood processing

Multi-omic profiling of human peripheral blood is increasingly utilized to identify biomarkers and pathophysiologic mechanisms of disease. The importance of these platforms in clinical and translational studies led us to investigate the impact of delayed blood processing on the numbers and state of peripheral blood mononuclear cells (PBMC) and on the plasma proteome. Similar to previous studies, we show minimal effects of delayed processing on the numbers and general phenotype of PBMCs up to 18 hours. In contrast, profound changes in the single-cell transcriptome and composition of the plasma proteome become evident as early as 4 hours after blood draw. These reflect patterns of cellular activation across diverse cell types that lead to progressive distancing of the gene expression state and plasma proteome from native in situ biology. Differences accumulating during an overnight rest (18 hours) would confound relevant biologic variance related to many underlying disease states.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Optimization of Thermal Conductance at Interfaces Using Machine Learning Algorithms

We report optimization of thermal transport across the interface of two different materials is critical to micro-/nanoscale electronic, photonic, and phononic devices. Although several examples of compositional intermixing at the interfaces having a positive effect on interfacial thermal conductance (ITC) have been reported, an optimum arrangement has not yet been determined because of the large number of potential atomic configurations and the significant computational cost of evaluation. On the other hand, computation-driven materials design efforts are rising in popularity and importance. Yet, the scalability and transferability of machine learning models remain as challenges in creating a complete pipeline for the simulation and analysis of large molecular systems. In this work we present a scalable Bayesian optimization framework, which leverages dynamic spawning of jobs through the Message Passing Interface (MPI) to run multiple parallel molecular dynamics simulations within a parent MPI job to optimize heat transfer at the silicon and aluminum (Si/Al) interface. We found a maximum of 50% increase in the ITC when introducing a two-layer intermixed region that consists of a higher percentage of Si. Because of the random nature of the intermixing, the magnitude of increase in the ITC varies. We observed that both homogeneity/heterogeneity of the intermixing and the intrinsic stochastic nature of molecular dynamics simulations account for the variance in ITC.

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

Affinity of LDR Organics to Cementitious Materials: Sorption and Leaching Tests

SRNL is working to identify and test the physiochemical interactions of organic contaminants of potential concern with minerals in grout/cementitious materials. Organic species may interact with cementitious minerals including slag, fly ash, cement, and other components/dopants like carbon in fly ash. The identification of such interactions will support the solidification process design, performance assessments for the chemicals of concern, and a proposed Resource Conservation and Recovery Act (RCRA) treatment variance the technology-based treatment standard specified for the Land Disposal Restriction (LDR) organics associated with Hanford tank waste. In FY24, a list of the 132 reasonably expected to be present LDR organics associated with Hanford tank waste (RPP-RPT-63493, Rev 1a) was screened by functional groups, octanol-water partitioning coefficients, and detection frequency in Hanford tank waste samples. A subset of 10 compounds spanning the identified properties were tested using sorption and leachate tests. These compounds were subject to traditional batch sorption tests and leaching tests on/from Cast Stone cementitious material with and without activated carbon (a potential organic adsorption additive). The tests have been completed and analysis is underway but as yet currently unavailable. The analytical results (and conclusions) will be included in a forthcoming revision to this report as soon as they are available

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Jet classification using high-level features from anatomy of top jets

Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the decision-making process and to identify the high-level features (HLFs) crucial for explaining jet classification. To address this, we consider the top jet tagging problems and introduce an analysis model (AM) that analyzes selected HLFs designed to capture important features of top jets. Our AM mainly consists of the following three modules: a relation network analyzing two-point energy correlations, mathematical morphology and Minkowski functionals for generalizing jet constituent multiplicities, and a recursive neural network analyzing subjet constituent multiplicity to enhance sensitivity to subjet color charges. We demonstrate that our AM achieves performance comparable to the Particle Transformer (ParT) while requiring fewer computational resources in a comparison of top jet tagging using jets simulated at the hadronic calorimeter angular resolution scale. Furthermore, as a more constrained architecture than ParT, the AM exhibits smaller training uncertainties because of the bias-variance tradeoff. We also compare the information content of AM and ParT by decorrelating the features already learned by AM. Lastly, we briefly comment on the results of AM with finer angular resolution inputs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗