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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 NTRS1986

The six year Nimbus-7 Earth Radiation Budget data set

Correlative statistics gathered during the first 6 yr of operation of the Nimbus-7 ERB instrumentation are presented and discussed. The numerical model employed for calculating the net radiation from data collected by wide and narrow field of view scanners is reviewed, along with the scanning patterns and specific data collection scenarios encountered with the remote sensing system. Seasonal and regional discrepancies between the ERB and polar-orbiting NOAA satellite data for average differences between zonal temperatures are noted.

Kyle, H. L.↗
NASA NTRS1986

Magnetic field directional discontinuities - Characteristics between 0.46 and 1.0 AU

Based on Mariner 10 data, a statistical survey and an application of the Sonnerup-Cahill variance procedure to a visual identification with 1.2-s averages for time intervals corresponding to the equally spaced heliocentric distances of 1.0, 0.72 and 0.46 AU, are employed to study the characteristics of directional discontinuities (DDs) in the interplanetary magnetic field. Analysis using two methods demonstrated that the ratio of tangential discontinuities (TDs) to rotational discontinuities (RDs) decreased with decreasing radial distance. Decreases in average discontinuity thickness of 41 percent between 1.0 and 0.72 AU, and 56 percent between 1.0 and 0.46 AU, were found for both TDs and RDs, in agreement with Pioneer 10 data between 1 and 5 AU. Normalization of the individual DD thicknesses with respect to the estimated local proton gyroradius (R sub L) gave a nearly constant average thickness at the three locations, 36 + or - 5 R sub L, for both RDs and TDs.

Lepping, R. P.↗
NASA NTRS2023

Spacesuit Fit and Mobility Assessments by Digital Human Modeling

Spacesuit Fit and Mobility Assessments by Digital Human Modeling K. Han Kim (Leidos, Inc.) Elizabeth A. Benson (KBR, Inc.) Sudhakar L. Rajulu (NASA Johnson Space Center) Spacesuits are required to accommodate safe operations for astronauts across gender and a wide variety of body shapes and sizes. This goal has been of particular importance given the increasing diversity of NASA crewmembers for upcoming Missions. While testing with design prototypes is a critical step for spacesuit development, iterative mockup design, fabrication, and human subject tests can be extremely costly and time consuming. Moreover, testing with a limited subject pool has often raised questions for validity, as test subjects need to represent the entire astronaut population, not only of the past or current, but also the future. This study is aimed at demonstrating how digital human modeling (DHM) tools have been built and directly supported NASA spacesuit developments. With DHM, the computer aided design (CAD) model of a spacesuit was integrated with human body models. Two use scenarios are presented in this paper, namely fit and mobility. For fit assessments, the suit-to-body contact and compression patterns were estimated using 3D human body scan models virtually wearing a spacesuit model. A statistical fit classifier was made from the contact patterns and applied against a large database of body scans (N=2,500). With this technique, the NASA reference design spacesuit Exploration EVA Mobility Unit (xEMU)was verified to accommodate 90% of the astronaut-like population, with the critical dimensions covering 1stto 99thpercentiles of the target body measurements. A similar technique assessed XEMU mobility. The maximum reach envelopes were considered, within which the work objects and critical hardware interfaces should be located for safe and ergonomic operations. While the reach envelope geometry varies significantly with the suit wearer’s body size and strength, the existing test data did not include the subjects critically required to define suit mobility requirements, such as very small females or large males. Using the xEMU virtual model kinematically simulated and permuted for a hypothetical wearer, however, the existing data were statistically transformed and scaled. This method enabled for a parametric estimation of the reach envelopes from the 1stpercentile female or 99thpercentile male. The outcome was successfully incorporated for requirement developments. With the new DHM tools, human integration of the spacesuit was structurally simulated and predictively assessed, which would have been otherwise impossible. Also, the needs for iterative mockup and subject tests were significantly reduced, which resulted in time and cost saving. Additional work is in progress to integrate additional vehicles, tools, and hardware with DHM framework.

K Han Kim↗
NASA NTRS1991

Probability of brittle failure

A methodology was developed for collecting statistically representative data for crack initiation and arrest from small number of test specimens. An epoxy (based on bisphenol A diglycidyl ether and polyglycol extended diglycyl ether and cured with diethylene triamine) is selected as a model material. A compact tension specimen with displacement controlled loading is used to observe multiple crack initiation and arrests. The energy release rate at crack initiation is significantly higher than that at a crack arrest, as has been observed elsewhere. The difference between these energy release rates is found to depend on specimen size (scale effect), and is quantitatively related to the fracture surface morphology. The scale effect, similar to that in statistical strength theory, is usually attributed to the statistics of defects which control the fracture process. Triangular shaped ripples (deltoids) are formed on the fracture surface during the slow subcritical crack growth, prior to the smooth mirror-like surface characteristic of fast cracks. The deltoids are complementary on the two crack faces which excludes any inelastic deformation from consideration. Presence of defects is also suggested by the observed scale effect. However, there are no defects at the deltoid apexes detectable down to the 0.1 micron level.

Kim, A.↗
DOE OSTI2026

ComStock Measure Documentation: High-Efficiency Rooftop Unit

Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy's ResStock and ComStock models, this work produces national data sets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The "baseline" model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass adoption impact on the baseline building stock. "Measures" refers to various "what-if" scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public data sets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario - high-efficiency rooftop unit (RTU) - and briefly introduces key results. The full public data set can be accessed on the Comstock data lake or via the Data Viewer at comstock.nlr.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ComStock Measure Documentation: Variable-Speed Pumps

Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy's ResStock and ComStock models, this work produces national data sets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The "baseline" model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass adoption impact on the baseline building stock. "Measures" refers to various "what-if" scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public data sets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario - variable speed pumps - and briefly introduces key results. The full public data set can be accessed on the ComStock data lake or via the Data Viewer at comstock.nlr.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2002

U.S. Aerospace and Aviation Industry: A State-By-State Analysis

President George W. Bush and the Congress created the Commission on the Future of the United States Aerospace Industry to evaluate the current and future health of the industry as well as the challenges that lie ahead for the U.S. workforce and the economy. To accomplish our mission, we commissioned a study on the economic impact of the aerospace industry nationally and on a state-by-state basis, using the best available U.S. government data. This study sought to define the core of the aerospace industry. The resulting data represents that very core those jobs specifically tied to commercial and civilian aerospace. U.S. Aerospace and Aviation: A State-by-State Analysis examines the civilian and commercial aerospace and aviation industry by employment, wages, payroll, and establishments. The report found that the U.S. civilian and commercial aerospace and aviation industry employed over 2 million workers in 2001.

STATES (UNITED STATES)↗
NASA NTRS2014

Relationship Between Column-Density and Surface Mixing Ratio: Statistical Analysis of O3 and NO2 Data from the July 2011 Maryland DISCOVER-AQ Mission

To investigate the ability of column (or partial column) information to represent surface air quality, results of linear regression analyses between surface mixing ratio data and column abundances for O3 and NO2 are presented for the July 2011 Maryland deployment of the DISCOVER-AQ mission. Data collected by the P-3B aircraft, ground-based Pandora spectrometers, Aura/OMI satellite instrument, and simulations for July 2011 from the CMAQ air quality model during this deployment provide a large and varied data set, allowing this problem to be approached from multiple perspectives. O3 columns typically exhibited a statistically significant and high degree of correlation with surface data (R(sup 2) > 0.64) in the P- 3B data set, a moderate degree of correlation (0.16 < R(sup 2) < 0.64) in the CMAQ data set, and a low degree of correlation (R(sup 2) < 0.16) in the Pandora and OMI data sets. NO2 columns typically exhibited a low to moderate degree of correlation with surface data in each data set. The results of linear regression analyses for O3 exhibited smaller errors relative to the observations than NO2 regressions. These results suggest that O3 partial column observations from future satellite instruments with sufficient sensitivity to the lower troposphere can be meaningful for surface air quality analysis.

nitrogen dioxide↗
NASA NTRS2021

In Situ Mg/Ca Measurements on Foraminifera: Comparison Between Laser Ablation Inductively Coupled Plasma Mass Spectrometry and Wavelength-Dispersive X-Ray Spectroscopy by Electron Probe Microanalyzer

We present a comparison of two different techniques: Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) and wavelength-dispersive X-Ray Spectroscopy by electron probe microanalyzer (EPMA) for obtaining Mg/Ca ratios in individual foraminifera shells. The goal is to assess the use of EPMA as an alternative technique for Mg/Ca analyses of single foraminiferal calcite shells. Foraminifera obtained from sediments (benthic, Uvigerina spp.) and from plankton tows (planktonic, Orbulina universa) were analyzed. All specimens were prepared in epoxy mounts and exposed in cross-section such that multiple high-resolution analyses could be completed on the shells using both techniques. We examined our data using statistical methods designed for the assessment and comparison of measurement techniques. In the case of Uvigerina, the mean difference for ratios obtained using EPMA and LA-ICP-MS is very small (−0.046 mmol mol−1) and scale independent. The Limits of Agreement (LoA, the standard deviation of the bias plus the mean bias) is [−0.315, 0.223] mmol mol−1. For samples with ratios lower than 13 mmol mol−1, we found a mean EPMA–LA-ICP-MS bias of −2.44 mmol mol−1 and a corresponding LoA of [−3.85, −1.04] mmol mol−1. For ratios higher than 13 mmol mol−1, there appears to be a scale dependent bias, meaning that the EPMA measured ratios become progressively larger than those of LA-ICP-MS as the Mg/Ca ratio increases, so the mean bias and LoA metrics are not meaningful. Results indicate that it is possible to use EPMA to collect Mg/Ca data, if the ratios are lower than ∼13 mmol mol−1.

EPMA↗
DOE OSTI2026

ComStock Measure Documentation: Thermostat Setbacks During Unoccupied Periods

This report assesses the potential for nationwide adoption of thermostat setbacks in appropriate applications. Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models, this work produces national datasets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. The “baseline” model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass-adoption impact on the baseline building stock. “Measures” refers to various “what-if” scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public datasets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario— Thermostat Setbacks During Unoccupied Periods—and briefly introduces key results. The full public dataset can be accessed on the ComStock data lake or via the Data Viewer at comstock.nrel.gov. The public dataset enables users to create custom aggregations of results for their use cases (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ComStock Measure Documentation: Fan Static Pressure Reset for Multizone Variable Air Volume Systems

This report assesses the potential for nationwide adoption of a duct static pressure reset in MZ VAV systems in appropriate applications. Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models, this work produces national datasets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. The “baseline” model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass-adoption impact on the baseline building stock. “Measures” refers to various “what-if” scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public datasets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario—Fan Static Pressure Reset for Multizone Variable Air Volume (VAV) Systems—and briefly introduces key results. The full public dataset can be accessed on the ComStock data lake or via the Data Viewer at comstock.nrel.gov. The public dataset enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Uncertainty Quantification Enabled by Automatic Differentiation for Hydrodynamic Simulation of Shock‐to‐Detonation Transition in High Explosives

Quantifying the effects of uncertainty in a reactive burn model on the run-to-detonation time in high explosives (HEs) provides a robust methodology for assessing the probability of an HE failing the IHE qualification standard. Moreover, uncertainty quantification helps evaluate whether the model calibration accurately represents data outside the calibration set. This study uses a specialized hydrodynamic simulation code for modeling detonation to determine the run-to-detonation time of the HE PBX 9502 for various impact velocities. To quickly approximate uncertainties in the model, a surrogate was constructed using a Taylor series expansion centered at the mean of the input parameters. To obtain the sensitivities required for constructing the Taylor series, HYP-percomplex Automatic Differentiation (HYPAD) was implemented. HYPAD is a methodology for infusing existing codes with automatic differentiation capabilities by augmenting variables with one or more imaginary units to compute step-size independent partial derivatives. These derivatives are accurate to machine precision with respect to the implemented numerical algorithm, meaning their accuracy reflects that of the underlying method (e.g., integration or discretization schemes). Using reduced order modeling techniques, the mean and standard deviation of the run-to-detonation time of a shock within PBX 9502 were computed for a number of initial impact velocities. A weighted least squares regression was then performed to obtain a best fit curve and prediction interval for the computed statistics. Historical data points from explosively driven wedge tests were utilized to validate the prediction interval, ensuring its reliability in predicting future outcomes. With this prediction interval and a known safety constraint curve, the most probable point of failure and the probability of failure for the HE PBX 9502 were determined.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ComStock Measure Scenario Documentation: Chiller Replacement

Building on a 3-year effort to calibrate and validate the U.S. Department of Energy's ResStock (TM) and ComStock (TM) models, this work produces national datasets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual energy consumption (at a subhourly resolution) of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. The goal of this work is to develop energy efficiency and demand flexibility end-use load shapes that cover high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to various "what-if" scenarios that can be applied to buildings. An end-use savings shape is the difference in energy consumption between a baseline building (or collection of buildings) and a building with an energy efficiency or demand flexibility measure applied. It results in a time-series profile broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step, as well as annual aggregations. This report describes the modeling methodology for a single end-use savings shape measure - chiller replacement - and briefly introduces key results. The full public dataset can be accessed on the ComStock (TM) data lake or via the Data Viewer at comstock.nrel.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Bayesian chain graph models to characterize microbe-environment dynamics

Microbiome data require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes. While a multiresponse linear regression model seems like a straight-forward solution, we argue that treating it as a graphical model is problematic given that the regression coefficient matrix does not encode the conditional dependence structure between response and predictor nodes. This observation is especially important in biological settings when we have prior knowledge on the edges from specific experimental interventions that can only be properly encoded under a conditional dependence model. Here, we propose a chain graph model with two sets of nodes (predictors and responses) whose solution yields a graph with edges that indeed represent conditional dependence, thus agreeing with the experimenter's intuition on the average behavior of nodes under treatment. The solution to our model is sparse via the Bayesian linear regression (LASSO). In addition, we propose an adaptive extension so that different shrinkages can be applied to different edges to incorporate edge-specific prior knowledge. Our model is computationally inexpensive through an efficient Gibbs sampling algorithm and can account for binary, counting, and compositional responses via an appropriate hierarchical structure. We test the performance of our model in a variety of simulated datasets, thereby showing superior performance to state-of-the-art approaches. We further apply our model to human gut and soil microbial compositional datasets, and we highlight that CG-LASSO can estimate biologically meaningful network structures in the data.

compositional data↗
DOE OSTI2026

Model-based economic analysis under uncertainty for PFAS treatment by granular activated carbon and ion exchange technologies

Recent drinking water regulations have imposed the need for per- and polyfluoroalkyl substances (PFAS) remediation. In response, treatment facilities may be required to retrofit existing treatment schemes to treat PFAS below maximum contaminant levels (MCLs). Adsorption technologies such as granular activated carbon (GAC) and ion exchange (IX) have been demonstrated to be effective; however, there are limited techno-economic metrics available which provide guidance on technology selection and design for diverse PFAS-containing source water conditions. Process systems engineering (PSE) tools which can traditionally perform these analyses are hindered by the data availability, model validity, and understanding of treatment phenomena for emerging contaminants. This work employs published data regressions, statistical models, process models, techno-economic analyses, and other process systems tools in a model-based uncertainty framework to consider the limitations of emerging contaminant research. Through this analysis framework, economic results are provided as probabilistic distributions based on the uncertainty of the models and diverse conditions that treatment facilities experience.

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

Molecular Dynamics Simulation of Complex Reactivity with the Rapid Approach for Proton Transport and Other Reactions (RAPTOR) Software Package

Simulating chemically reactive phenomena such as proton transport on nanosecond to microsecond and beyond time scales is a challenging task. Ab initio methods are unable to currently access these time scales routinely, and traditional molecular dynamics methods feature fixed bonding arrangements that cannot account for changes in the system’s bonding topology. The Multiscale Reactive Molecular Dynamics (MS-RMD) method, as implemented in the Rapid Approach for Proton Transport and Other Reactions (RAPTOR) software package for the LAMMPS molecular dynamics code, offers a method to routinely sample longer time scale reactive simulation data with statistical precision. RAPTOR may also be interfaced with enhanced sampling methods to drive simulations toward the analysis of reactive rare events, and a number of collective variables (CVs) have been developed to facilitate this. Key advances to this methodology, including GPU acceleration efforts and novel CVs to model water wire formation are reviewed, along with recent applications of the method which demonstrate its versatility and robustness.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Upstreamness and downstreamness in input–output analysis from local and aggregate information

Abstract Ranking sectors and countries within global value chains is of paramount importance to estimate risks and forecast growth in large economies. However, this task is often non-trivial due to the lack of complete and accurate information on the flows of money and goods between sectors and countries, which are encoded in input–output (I–O) tables. In this work, we show that an accurate estimation of the role played by sectors and countries in supply chain networks can be achieved without full knowledge of the I–O tables, but only relying on local and aggregate information, e.g., the total intermediate demand per sector. Our method, based on a rank-1 approximation to the I–O table, shows consistently good performance in reconstructing rankings (i.e., upstreamness and downstreamness measures for countries and sectors) when tested on empirical data from the world input–output database. Moreover, we connect the accuracy of our approximate framework with the spectral properties of the I–O tables, which ordinarily exhibit relatively large spectral gaps. Our approach provides a fast and analytically tractable framework to rank constituents of a complex economy without the need of matrix inversions and the knowledge of finer intersectorial details.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Measurements of jet quenching using semi-inclusive hadron+jet distributions in 𝑝⁢𝑝 and central Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

The ALICE Collaboration reports measurements of the semi-inclusive distribution of charged-particle jets recoiling from a high transverse momentum (high pT) charged hadron, in pp and central Pb-Pb collisions at center-of-mass energy per nucleon–nucleon collision $\sqrt{s_{NN}}$ = 5.02 TeV. The large uncorrelated background in central Pb-Pb collisions is corrected using a data-driven statistical approach which enables precise measurement of recoil jet distributions over a broad range in p T,ch jet and jet resolution parameter R. Recoil jet yields are reported for R = 0.2, 0.4, and 0.5 in the range 7 < p T,ch jet < 140 GeV/c and π/2 < φ < π, where φ is the azimuthal angular separation between hadron trigger and recoil jet. The low-p T,ch jet reach of the measurement explores unique phase space for studying jet quenching, the interaction of jets with the quark–gluon plasma generated in high-energy nuclear collisions. Comparison of p T,ch jet distributions from pp and central Pb-Pb collisions probes medium-induced jet energy loss and intra-jet broadening, while comparison of their acopla narity distributions explores in-medium jet scattering and medium response. The measurements are compared to theoretical calculations incorporating jet quenching.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗