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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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NASA NTRS1994

Einstein x ray observations of the core of the Shapley Supercluster in northern Centaurus

We present Einstein x ray observations of the core of the Shapley Supercluster, one of the richest and densest known mass concentrations in the local (z less than 0.1) universe. We used Imaging Proportional Counter (IPC) observations supplemented with data from the Einstein Slew Survey to determine the locations and structure of mass concentrations in the region. An x ray map composed of IPC observations of the central (10 deg x 10 deg) region of the Shapley Supercluster is presented. We present evidence that the X-ray clusters observed within 5 deg of the core of the supercluster are on average brighter than those of corresponding richness class distributed throughout the sky. However, we measure no significant difference in the galaxy formation efficiency of these cluster of galaxies compared to other, more isolated clusters. We also find one previously uncataloged cluster-sized mass concentration in the core of the Shapley Supercluster. This new cluster, 'SC 1327-312', is relatively x ray bright (F(sub x) = 1.1 + or - 0.2 x 10(exp -11) erg sec(exp -1) cm(exp -2)) and L(sub x) = 1.1 + or - 0.2 x 10(exp 44) erg sec(exp -1) within 10 minutes, assuming z = 0.0477, H(sub 0) = 50, q(sub 0) = 0). As SC 1327-312 lies well within an Abell radius of the richness R = 4 cluster Shapley 8 (A3558), we suggest it may contribute to an artificially high galaxy count and richness classification for shapley 8. From slew data, we estimate an x ray luminosity for Shapley 8 which is just half the mean luminosity of the four other R = 4 clusters observed by the IPC, further suggesting the richness classification to be an overestimate.

Breen, Jeffrey↗

NASA NTRS1984

IRAS observations of Shapley-Ames galaxies

A preliminary discussion of the infrared properties of a representative subsample of galaxies in the Revised Shapley-Ames Catalog (B less than about 13 mag) is presented. Of the 165 galaxies in the sample, 108 predominantly spiral galaxies, are detected in the infrared by IRAS. None of the elliptical galaxies and only about 25 percent of the lenticular galaxies scanned were detected. The range of infrared-to-blue luminosity ratios, a measure of the infrared excess of galaxies, is large, varying from roughly 0.1 to roughly 5. The data suggest that weakly infrared emitting galaxies are cool (100-60 micron color temperatures of about 25 K), while the more infrared luminous ones tend to be warmer (about 50 K). The rate of star formation in barred spiral galaxies is apparently higher than in normal spirals. About 1 solar mass/year of interstellar matter is converted into massive stars in the typical spiral galaxy.

De Jong, T.↗

NASA NTRS2012

Search for X-Ray Emission Associated with the Shapley Supercluster with Suzaku

Suzaku performed observations of 3 regions in and around the Shapley supercluster: a region located between A3558 and A3556, at approx 0.9 times the virial radii of both clusters, and two other regions at 1 deg and 4 away from the first pointing. The 4 deg -otfset observation was used to evaluate the Galactic foreground emission. We did not detect significant redshifted Oxygen emission lines (O VII and O VIII) in the spectra of all three pointings, after subtracting the contribution of foreground and background emission. An upper limit for the redshifted O VIII Ka line intensity of the warm-hot intergalactic medium (WHIM) is 1.5 x 10(exp -7) photons / s / sq cm / sq arcmin, which corresponds to an overdensity of approx 380 (Z/0.1 Solar Z)(exp -1/2)(L/3 Mpc)(exp -1/2), assuming T = 3 x 10(exp 6) K. We found excess continuum emission in the 1 deg-offset and on-filament regions, represented by thermal models with kT approximates 1 keV and approximates 2 keV, respectively. The redshifts of both 0 and that of the supercluster (0.048) are consistent with the observed spectra. The approx 1 keV emission can be also fitted with Ne-rich Galactic (zero redshift) thin thermal emission. Radial intensity profile of 2 keV component suggests contribution from A3558 and A3556, but with significant steepening of the intensity slope in the outer region of A3558. Finally, we summarized the previous Suzaku search for the WHIM and discussed the feasibility of constraining the WHIM. An overdensity of <400 can be detectable using O VII and O VIII emission lines in a range of 1.4 x 10(exp 6) K < T < 5 x 10(exp 6) K or a continuum emission in a relatively high temperature range T > 5 x 10 (exp 6) K with the Suzaku XIS. The non detection with Suzaku suggests that typical line-of-sight average overdensity is < 400.

Mitsuishi, Ikuyuki↗

NASA NTRS1985

Soft X-ray maps of the Large Magellanic Cloud (LMC)

Soft X-ray maps of the Large Magellanic Cloud (LMC) were obtained from scanning-observations with the HEAO-1 low energy detectors. Comparison of the 1/4 keV X-ray observations with the neutral hydrogen column densities in the LMC obtained from a 21 cm line survey, shows no evidence for absorption effects in the 1/4 keV X-ray flux from the LMC due to the neutral matter in the LMC. Instead, faint X-ray emission is detected from the LMC. The extent of this emission is smaller than the size of the halo or the disk of the LMC. Assuming this 1/4 keV emission to be diffuse, it is identified with a supergiant shell of optical nebulosity known as Shapley III, and the bar of the LMC. The X-ray luminosities of the regions are estimated to be 9 times 10 to the 38th power ergs/sec and 1.8 times 10 to the 39th power ergs/sec for the Shapley III region and the bar of the LMC respectively. Shapley III could be an X-ray superbubble.

Singh, K. P.↗

NASA NTRS2024

Generating Dominating Sets Using Locally Defined Centrality Measures

The dominating set problem has many practical applications but is well-known to be NP-hard. Therefore, there is a need for efficient heuristic algorithms, especially in applications such as ad hoc wireless networks. Most distributed algorithms proposed in the literature assume that each node has knowledge of the network structure. We propose a distributed heuristic algorithm that uses two rounds of communication, and where each node has only local information, both in terms of network structure and dominating set assignment. First, each node calculates a local centrality measure to determine whether it is part of the dominating set D. The second round guarantees D is a dominating set by adding any non-dominated nodes. We compare several centrality measures and show that the Shapley centrality, derived from the Shapley value in game theory, is theoretically motivated and performs well in practice on several synthetic and real-world networks.

Network↗

NASA NTRS1988

Atlas of Galaxies Useful for Measuring the Cosmological Distance Scale

A critical first step in determining distances to galaxies is to measure some property of primary objects such as stars of specific types, H II regions, and supernovae remnants that are resolved out of the general galactic star content. With the completion of the Mount Wilson/Palomar/Las Campanas survey of bright galaxies in 1985, excellent large-scale photographs of the complete Shapley-Ames sample were on hand. Most of the galaxies useful for distance scale calibration are in this collection. This atlas contains photographs of 322 galaxies including the majority of all Shapley-Ames bright galaxies, plus cluster members in the Virgo Cluster core that might be usefully resolved by the Hubble Space Telescope (HST). Because of crowding and high background-disk surface brightness, the choice of field position is crucial for programs involving resolution of particular galaxies into stars. The purpose of this atlas is to facilitate this choice. Enough information is given herein (coordinates of the galaxy centers and the scale of the photography) to allow optimum placement of the HST wide-field planetary camera format of approximately 150 arc-seconds on a side.

Sandage, Allan↗

NASA NTRS1990

Are bars essential for starbursts in non-interacting galaxies

Analyzed here are the 1.6 and 2.2 micron images of a sample of galaxies that are classified as unbarred by the Revised Shapley-Ames Catalog. These galaxies have characteristic properties of nuclear starbursts and are examined through near infrared imaging in a search for hidden bars. Researchers selected a sample of 36 galaxies from the Revised Shapley-Ames Catalog that have far infrared luminosities greater than 10(exp 10) solar luminosity and hot Infrared Astronomy Satellite (IRAS) colors between 60 and 100 microns, indicative of nuclear starbursts, but are not classified as Seyfert 1 or 2. Their determination of the presence of a bar relies primarily on an analysis of the 2 micron image using the Galaxy Surface Photometry (GASP) package (Cawson, 1983). The GASP analysis programs determine the galaxy surface brightness and ellipticity profiles as well as the position angle and the center coordinates of the ellipses. To test the way that GASP will characterize the surface brightness of barred galaxies, two galaxies with known bars, NGC 1068 and NGC 2523, were imaged with the 2 micron camera and analyzed with GASP. Fifteen of the sample that are not clearly barred from optical data and are isolated were imaged at 1.6 and 2.2 microns; 9 of these do not appear to have bars. Strong bars therefore do not appear to be an absolute requirement for high infrared luminosity in isolated galaxies.

Pompea, Stephen M.↗

NASA NTRS2001

Large Scale Diffuse X-ray Emission from Abell 3571

Observations of the Luman alpha forest suggest that there are many more baryons at high redshift than we can find in the Universe nearby. The largest known concentration of baryons in the nearby Universe is the Shapley supercluster. We scanned the Shapley supercluster to search for large scale diffuse emission with the Rossi X-ray Timing Explorer (RXTE), and found some evidence for such emission. Large scale diffuse emission may be associated to the supercluster, or the clusters of galaxies within the supercluster. In this paper we present results of scans near Abell 3571. We found that the sum of a cooling flow and an isothermal beta model adequately describes the X-ray emission from the cluster. Our results suggest that diffuse emission from A3571 extends out to about two virial radii. We briefly discuss the importance of the determination of the cut off radius of the beta model.

Molnar, Sandor M.↗

DOE OSTI2026

Explaining System-Level Prognostics with Established Machine Learning Methods

System-level prognostics is crucial for ensuring reliability and enabling predictive maintenance in complex systems with interconnected components. This study presents a framework that integrates data-driven methods to predict the remaining useful life (RUL) of a subsystem under multiple and concurrent faults within a nuclear power plant system with explainable artificial intelligence (XAI). A nuclear power plant (NPP) operation was simulated to model the degradation behavior of NPP components, and four machine learning models—Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory (LSTM)—were evaluated for prognostics with a novel system RUL parameter. The LSTM model demonstrated potential superior repeatability, while SHAP (SHapley Additive exPlanations) for explainability provided consistent and trustworthy global explanations. In contrast, LIME (Local Interpretable Model-agnostic Explanations) offered localized interpretability but showed reduced stability for sequential data. Key findings include the interplay between component-level degradation and system-wide performance, with LSTM effectively capturing these dynamics through sequence-level predictions. The XAI techniques enhanced transparency by identifying critical features influencing model predictions and aligning with domain knowledge. Furthermore, this framework has significant implications for improving trust and understanding in predictive maintenance, particularly in safety-critical industries like nuclear energy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Automatic Loss Factor Modeling and Attribution on Unlabeled PV Energy Data

We present a novel approach for modeling the loss factors of photovoltaic power generation systems (PV systems). This method is a white-box machine learning model built on convex optimization that is fast, interpretable, and auditable. It takes as an input the measured daily energy produced by the system, over a multi-year period, and returns a multiplicative decomposition model of the daily energy signal and full attribution of the total energy loss to each feature. The methods section of this paper has two major components: (1) the description of the signal decomposition (SD) model, expressed in the SD framework, and (2) the attribution of total energy losses via Shapley values. We validate the method on synthetic and open-source data sets and compare to similar methods from the literature.

artificial intelligence↗

DOE OSTI2026

Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning

Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site-years of high-frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem-scale carbon, water, and energy fluxes. Using an interpretable machine-learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short-term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem-scale carbon–water–energy coupling. By integrating long-term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data-driven basis for improving crop and Earth-system models and for guiding bioenergy landscape design under future climate scenarios.

Accumulated Local Effects↗

DOE OSTI2026

Data, model inputs, and analysis scripts associated with a manuscript on stream intermittency controls across spatial scales in Pacific Northwest watersheds

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript "Hydroclimatic Memory and Watershed Template Shape Stream Intermittency: Multi-scale Attribution Using Process-based Simulation and Explainable ML" by Niroula et al. (2026), submitted to Water Resources Research (WRR). The study investigates the dominant controls on stream intermittency across local, reach, and watershed scales using a coupled process-based simulation and explainable machine-learning framework. Long-term daily simulations from the Advanced Terrestrial Simulator (ATS) were used to generate wetness states and ponded-depth responses over river-corridor cells. These ATS outputs were then aggregated across scales and used to train XGBoost (eXtreme Gradient Boosting) models. SHAP (SHapley Additive exPlanations) was applied to quantify the relative importance of hydroclimatic forcings, watershed template attributes, and antecedent-memory effects in shaping intermittency behavior. The analysis is carried out for three contrasting Pacific Northwest watersheds: Oak Creek (OCW), American River Watershed (ARW), and H.J. Andrews (HJA). Across these testbeds, the package contains ATS-ready watershed inputs, ATS run configuration and selected output files, model-evaluation data products, intermittency-analysis datasets, machine-learning target-feature tables, SHAP outputs, and notebooks used to organize, analyze, and visualize results. At a high level, the package documents a workflow in which ATS provides the physically based simulation backbone and explainable machine learning is used as a post-processing attribution tool. The contents are intended to support interpretation of the manuscript figures and results, provide context for how intermittency metrics were generated at multiple scales, and preserve the key artifacts needed to understand and reuse the analysis workflow. The package contains a high-level directory summary file (`summary.txt`) and four main content folders (1) `evaluation_plots` contains evaluation figures and supporting evaluation datasets; (2) `intermittency_plots` contains intermittency-focused analysis notebook and prepared datasets; (3) `ml-training-and-shap_values_plots` contains ML training inputs, SHAP outputs, and figure-generation notebooks; and (4) `watershed_mesh_and_ats_input` contains ATS model setup materials, forcing inputs, geometry, and selected run files. More specifically, the `evaluation_plots` folder contains the notebook used for ATS evaluation plotting and site-specific evaluation datasets. These include evapotranspiration and water-balance products for three watersheds, as well as an Oak Creek field-measurement discharge file. The `intermittency_plots` folder contains the notebook used for intermittency analysis and the prepared datasets used to analyze intermittent and non-intermittent wetness behavior across the study watersheds. The `ml-training-and-shap_values_plots` folder contains notebooks and outputs for the machine-learning and explainability workflow. This includes the main XGBoost and SHAP notebook(s), a beeswarm plotting notebook, target-feature tables for machine-learning training, SHAP summary tables, and per-sample SHAP value archives. The `watershed_mesh_and_ats_input` folder contains ATS-related watershed inputs and supporting materials. This includes mesh and shape products, ATS-readable LAI and meteorological forcing inputs, selected ATS spinup and transient-run files, and a watershed workflow example notebook. Subdirectories are organized by watershed where applicable.All files are .cpg (codepage files), .csv (comma-separated values), .dbf (database files), .exo (Exodus mesh format), .h5 (HDF5 format), .ipynb (Jupyter notebooks), .pkl (Python pickle), .prj (projection files), .sh (shell scripts), .shp (shapefile geometry), .shx (shapefile index), .txt (text files), or .xml (markup data).

Advanced Terrestrial Simulator↗

DOE OSTI2026

Data and scripts associated with a manuscript analyzing ELM-FATES parameter sensitivity under pre-fire and postfire scenarios using machine learning

Aboveground biomass↗

DOE OSTI2024

Teaching Freight Mode Choice Models New Tricks Using Interpretable Machine Learning Methods

Understanding and forecasting the intricate freight mode choice behavior under various industry, policy, and technology contexts is essential in freight planning and policymaking. Numerous models have been developed in prior studies to provide insights into freight mode selection, the majority of which use discrete choice models such as multinomial logit (MNL) models. However, logit models often rely on linear specifications of independent variables, despite potential nonlinear relationships in the data. Moreover, there often lacks a heuristic and efficient approach to identify such complex relationships to define the logit model specifications. To fill this gap, we developed an MNL model for freight mode choice using the insights from state-of-the- art machine learning (ML) models. ML models can capture the nonlinear nature of the complex decision-making process, and recent advances in 'explainable AI' have greatly improved their interpretability. The interpretable ML methods help enhance the performance of MNL models and advance knowledge of freight mode choice. Specifically, the influential factors and their relationship with individual modes are identified using SHapley Additive exPlanations (SHAP) to improve the MNL's performance. The workflow is demonstrated in a case study of Austin, Texas, and the SHAP results reveal multiple nonlinear relationships predicted by ML models. Incorporating those relationships into MNL model specifications improves the interpretability and accuracy of the MNL model compared to a conventional MNL model. Findings from this study can be used to guide freight planning and inform policymakers and practitioners on how key factors affect freight decision-making.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

NASA NTRS2022

Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence

Computational models of the Earth System are critical tools for modern scientific inquiry. Effortstoward evaluating and improving errors in representations of physical and chemical processes inthese large computational systems are commonly stymied by highly nonlinear and complexerror behavior. Recent work has shown that these errors can be effectively predicted usingmodern Artificial Intelligence (A.I.) techniques. In this work, we go beyond these previousstudies to apply an interpretable A.I. technique to not only predict model errors but also movetoward understanding the underlying reasons for successful error prediction. We use XGBoostclassification trees and SHapley Additive exPlanations (SHAP) analysis to explore the errors inthe prediction of lightning occurrence in the NASA GEOS model, a widely used Earth SystemModel. This explainable error prediction system can effectively predict the model error andindicates that the errors are strongly related to convective processes and the characteristics ofthe land surface.

Artificial intelligence↗

DOE OSTI2025

Evapotranspiration Partitioning Using Flux Tower Data in a Semi-Arid Ecosystem

Information about evapotranspiration (ET) and its components, that is, evaporation and transpiration, is crucial for a wide range of water and ecosystem management applications. However, partitioning ET into its two components is often challenging because of their spatiotemporal variabilities and lack of process understanding. This study developed a machine learning (ML) framework to shed light on ET processes and assess the relative importance of different drivers by incorporating hydrometeorology and biomass productivity variables. The Shapley Additive Explanations (SHAP) approach was applied to enhance explainability and rank the importance of ET drivers and their components. A total of 62 variables covering hydrometeorological and biomass productivity dimensions were considered from the Reynolds Creek Critical Zone Observatory (CZO) station in Idaho. The variable importance assessment identified the leading drivers individually for evaporation, transpiration and ET (soil water content for evaporation, vapour pressure deficit for transpiration and soil water content for ET). The results further highlighted the value of combining hydrometeorological and biomass productivity variables to achieve better predictability of ET processes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Identifying Nuclear Data Correlated Through Predicting Bias in Integral Experiments via Applying Principal Component Analysis to Random Forest

ABSTRACT Nuclear data (ND) are the input data for neutron‐transport simulations to answer questions related to nuclear technologies. Subsets of ND, here > 20,000 data points, are validated with respect to thousands of criticality experiments that represent various applications on a small scale. The aim of validation with these experiments is to find errors in ND or methods. The key challenge here is that several hundreds of ND are used to simulate one integral value. Hence, one cannot clearly identify what ND are leading to bias in criticality measurements. In fact, a mistake in one nuclear‐data observable can be compensated with an error in another, and the predicted criticality value would still be predicted in agreement with experimental data. Random forest (RF) was previously employed to predict bias in criticality measurements using sensitivities of simulated criticality experiments to ND. The SHapley Additive exPlanations (SHAP) metric was then applied to attribute the importance of each ND experiment and observable to bias prediction. This, however, did not highlight what ND were jointly related to predicting bias. This is important as it could inform us about where compensating errors in ND could hide. We tackle this shortcoming here by first decomposing the ND sensitivities to integral‐experiment simulations into principal components. Then we use principal component projections to predict bias via the RF and SHAP. The SHAP values and principal components are employed to reconstruct detailed SHAP values for each ND observable. We demonstrate that these extended SHAP bias predictions are more robust, less noisy, and more efficient. In addition, we show that this approach accounts for covariance in ND sensitivities and automates the identification of where compensating errors could hide in ND.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Jin, Ling↗