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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enriching the physics program of the CMS experiment via data scouting and data parking

Specialized data-taking and data-processing techniques were introduced by the CMS experiment in Run 1 of the CERN LHC to enhance the sensitivity of searches for new physics and the precision of standard model measurements. These techniques, termed data scouting and data parking, extend the data-taking capabilities of CMS beyond the original design specifications. The novel data-scouting strategy trades complete event information for higher event rates, while keeping the data bandwidth within limits. Data parking involves storing a large amount of raw detector data collected by algorithms with low trigger thresholds to be processed when sufficient computational power is available to handle such data. The research program of the CMS Collaboration is greatly expanded with these techniques. The implementation, performance, and physics results obtained with data scouting and data parking in CMS over the last decade are discussed in this Report, along with new developments aimed at further improving low-mass physics sensitivity over the next years of data taking.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Perturbation of soil organic carbon induced by land-use change from primary forest

Abstract The impact of land-use change (LUC) on soil organic carbon (SOC) has been a wide concern of land management policymakers because CO 2 emissions induced by LUC have been the second largest carbon source worldwide. However, due to insufficient data quality and limited biome coverage, a global big picture of the impact of LUC on SOC is still not clear. This study conducted a meta-analysis on 288 independent observations sourced from 62 peer-reviewed papers to provide a global summary of the change in SOC after the conversion of primary forests into other land-use types. The conversion of primary forest to cropland resulted in the most severe SOC loss (−33.2%), followed by conversion into plantation forests (−22.3%) and secondary forests (−19.1%). Nonetheless, SOC increased by 9.1% after a conversion from primary forests into pasture. More SOC loss was found at sites with lower precipitation for primary forests converted to cropland and plantation forests. The SOC loss decreased consistently with increasing mean annual temperature (MAT) for all four types of LUC. Moreover, the loss of SOC tended to worsen over time when primary forests are converted to cropland or plantation forests. In contrast, SOC loss recovered over time following conversion to secondary forests. The gain of SOC gradually increased over time after conversion to pastures. To conclude, the changes in SOC are related not only to the land-use type but also to precipitation, temperature and turn years after LUC. Due to limited data, this study focuses on soil profiles within 30 cm depth, and future research should explore SOC dynamics induced by LUC at greater depths. Overall, cases of SOC loss of approximately 30% following deforestation were very common (except for conversion to pasture), and the results of this study show that the loss of SOC following LUC should be carefully considered and monitored in land management.

Zhang, Zhiyuan (ORCID:0000000223407001)↗
DOE OSTI2025

Summary of Pilot Project State Technical Assistance on Multi-Sector Analysis for Electric and Petroleum Fuels

The Oregon Energy Security Plan, (ODOE 2024) published in September 2024, builds a strong case for the state to give acute attention to the fuel supply chain. In December 2024, Pacific Northwest National Laboratory (PNNL) in partnership with Oregon Department of Energy (ODOE), announced a pilot project to conduct an analysis that synthesizes current and projected transportation fuel dynamics, supply chain risks, and risk comparators with relevant sectors, such as transportation electrification, sponsored by the Department of Energy’s (DOE) Office of Cybersecurity, Energy Security, and Emergency Response (CESER). The study, is intended to leverage existing modeling and frameworks from a recent 2024 sector coupling analysis supported by the DOEs Office of Electricity (OE) (B. Mitra, S. Pal, et al., Coupling of the Electricity and Transportation Sectors - Part I: Sector Overviews 2024) (B. Mitra, S. Pal and J. Reeve, et al. 2024). While the PNNL team set out to conduct a quantitative risk analysis driven by detailed data that synthesizes current and projected transportation fuel dynamics, supply chain risks, and risk comparators with relevant sectors. The intention was to provide an approach that could be extendable to other parts of the country. They encountered data limitations and adjusted their approach accordingly. This report summarizes PNNL's original plan for executing the study, including limitations for obtaining data requirements for fuel flows and interim products, as well as a risk matrix that can be used to identify supply chain risks.

02 PETROLEUM↗
NASA NTRS1972

An improved learning decoder

Learning decoder was developed which operates at system data rate without limiting data rate. Decoder is much simpler than those in existence, operates near Shannon's channel capacity, and automatically recovers operation after loss of signal.

Doland, G. D.↗
NASA NTRS2021

The NANOGrav 11yr Data Set: Limits on Supermassive Black Hole Binaries in Galaxies within 500 Mpc

Supermassive black hole binaries (SMBHBs) should form frequently in galactic nuclei as a result of galaxy mergers. At subparsec separations, binaries become strong sources of low-frequency gravitational waves (GWs), targeted by Pulsar Timing Arrays. We used recent upper limits on continuous GWs from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) 11 yr data set to place constraints on putative SMBHBs in nearby massive galaxies. We compiled a comprehensive catalog of ∼44,000 galaxies in the local universe (up to redshift ∼0.05) and populated them with hypothetical binaries, assuming that the total mass of the binary is equal to the SMBH mass derived from global scaling relations. Assuming circular equal-mass binaries emitting at NANOGrav's most sensitive frequency of 8 nHz, we found that 216 galaxies are within NANOGrav's sensitivity volume. We ranked the potential SMBHBs based on GW detectability by calculating the total signal-to-noise ratio such binaries would induce within the NANOGrav array. We placed constraints on the chirp mass and mass ratio of the 216 hypothetical binaries. For 19 galaxies, only very unequal-mass binaries are allowed, with the mass of the secondary less than 10% that of the primary, roughly comparable to constraints on an SMBHB in the Milky Way. However, we demonstrated that the (typically large) uncertainties in the mass measurements can weaken the upper limits on the chirp mass. Additionally, we were able to exclude binaries delivered by major mergers (mass ratio of at least 1/4) for several of these galaxies. We also derived the first limit on the density of binaries delivered by major mergers purely based on GW data.

Zaven Arzoumanian↗
NASA NTRS2016

The NANOGrav Nine-Year Data Set: Limits on the Isotropic Stochastic Gravitational Wave Background

We compute upper limits on the nanohertz-frequency isotropic stochastic gravitational wave background (GWB) using the 9 year data set from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) collaboration. Well-tested Bayesian techniques are used to set upper limits on the dimensionless strain amplitude (at a frequency of 1 yr(exp -1) for a GWB from supermassive black hole binaries of A(sub gw) less than 1.5 x 10(exp -15). We also parameterize the GWB spectrum with a broken power-law model by placing priors on the strain amplitude derived from simulations of Sesana and McWilliams et al. Using Bayesian model selection we find that the data favor a broken power law to a pure power law with odds ratios of 2.2 and 22 to one for the Sesana and McWilliams prior models, respectively. Using the broken power-law analysis we construct posterior distributions on environmental factors that drive the binary to the GW-driven regime including the stellar mass density for stellar-scattering, mass accretion rate for circumbinary disk interaction, and orbital eccentricity for eccentric binaries, marking the first time that the shape of the GWB spectrum has been used to make astrophysical inferences. Returning to a power-law model, we place stringent limits on the energy density of relic GWs, OMEGA(sub gw) (f) h squared less than 4.2 x 10(exp -10). Our limit on the cosmic string GWB, OMEGA(sub gw) (f) h squared less than 2.2 x 10(exp -10), translates to a conservative limit on the cosmic string tension with G mu less than 3.3 x 10(exp -8), a factor of four better than the joint Planck and high-l‚ cosmic microwave background data from other experiments.

Arzoumanian, Z.↗
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↗
DOE OSTI2025

Reference Correlations for the Density and Viscosity of Molten Alkali and Alkaline Earth Fluoride Salts

While there is a significant body of literature pertaining to thermophysical property measurements of molten salts, there is often a wide degree of variability among independent measurements of the same compounds. As such, the scientific community benefits greatly from an unbiased, independent assessment of duplicate datasets, so that reference correlations which describe these thermophysical properties as functions of temperature can be determined and then commonly used by researchers, scientists, and engineers. With regard to molten fluoride compounds, a significant time has elapsed since density and viscosity reference correlations have been determined; Janz conducted the most recent effort, in 1988, to provide reference correlations for the densities and viscosities of molten fluoride compounds via the National Standard Reference Data System coordinated by the National Bureau of Standards. Since then, new data have been published for molten fluoride compounds, and a new precedent has surfaced for putting forth reference correlations that involve fitting to multiple primary datasets. In this work, reference correlations are put forth for molten alkali and alkaline earth fluoride compounds in an effort to provide updated, improved correlations for general use. For molten alkali fluoride densities, estimated uncertainties with a 95% confidence interval are summarized as follows: LiF (0.63%), NaF (0.48%), KF (0.76%), RbF (0.93%), and CsF (0.75%). For molten alkaline earth fluoride densities, an estimated uncertainty was not able to be quantified for BeF 2 because of limited data; however, estimated uncertainties with a 95% confidence interval are summarized as follows for the remaining alkaline earth fluorides: MgF 2 (1.5%), CaF 2 (0.92%), SrF 2 (1.6%), and BaF 2 (0.23%). For molten alkali fluoride viscosities, uncertainty was not able to be quantified for RbF and CsF because of limited data; however, estimated uncertainties with a 95% confidence interval are summarized as follows for the remaining alkali fluorides: LiF (4.4%), NaF (3.0%), and KF (4.0%). For molten alkaline earth fluoride viscosities, limited consistent data resulted in the recommendation of single datasets (from literature) that are deemed to be the most trustworthy based on the quality of the underlying experimental studies.

Birri, A. [Oak Ridge National Laboratory (ORNL), O↗
NASA NTRS2019

The NANOGrav 11 Yr Data Set: Limits on Gravitational Waves from Individual Supermassive Black Hole Binaries

Observations indicate that nearly all galaxies contain supermassive black holes at their centers. When galaxies merge, their component black holes form SMBH binaries (SMBHBs), which emit low-frequency gravitational waves (GWs) that can be detected by pulsar timing arrays. We have searched the North American Nanohertz Observatory for Gravitational Waves 11 yr data set for GWs from individual SMBHBs in circular orbits. As we did not find strong evidence for GWs in our data, we placed 95% upper limits on the strength of GWs from such sources. At f(gw) = 8 nHz, we placed a sky-averaged upper limit of h(0) < 7.3(3) × 10(exp −15). We also developed a technique to determine the significance of a particular signal in each pulsar using "dropout" parameters as a way of identifying spurious signals. From these upper limits, we ruled out SMBHBs emitting GWs f(gw) = 8 nHz within 120 Mpc for M = 10(exp 9) Solar Mass, and within 5.5 Gpc for M= 10(exp 10) Solar Mass at our most sensitive sky location. We also determined that there are no SMBHBs with M > 1.6 x 10(exp 9) Solar Mass emitting GWs with f(gw) = 2.8–317.8 nHz in the Virgo Cluster. Finally, we compared our strain upper limits to simulated populations of SMBHBs, based on galaxies in the Two Micron All-Sky Survey and merger rates from the Illustris cosmological simulation project, and found that only 34 out of 75,000 realizations of the local universe contained a detectable source.

Aggarwal, K.↗
DOE OSTI2023

Machine learning models inaccurately predict current and future high-latitude C balances

The high-latitude carbon (C) cycle is a key feedback to the global climate system, yet because of system complexity and data limitations, there is currently disagreement over whether the region is a source or sink of C. Recent advances in big data analytics and computing power have popularized the use of machine learning (ML) algorithms to upscale site measurements of ecosystem processes, and in some cases forecast the response of these processes to climate change. Due to data limitations, however, ML model predictions of these processes are almost never validated with independent datasets. To better understand and characterize the limitations of these methods, we develop an approach to independently evaluate ML upscaling and forecasting. We mimic data-driven upscaling and forecasting efforts by applying ML algorithms to different subsets of regional process-model simulation gridcells, and then test ML performance using the remaining gridcells. In this study, we simulate C fluxes and environmental data across Alaska using ecosys, a process-rich terrestrial ecosystem model, and then apply boosted regression tree ML algorithms to training data configurations that mirror and expand upon existing AmeriFLUX eddy-covariance data availability. We first show that a ML model trained using ecosys outputs from currently-available Alaska AmeriFLUX sites incorrectly predicts that Alaska is presently a modeled net C source. Increased spatial coverage of the training dataset improves ML predictions, halving the bias when 240 modeled sites are used instead of 15. However, even this more accurate ML model incorrectly predicts Alaska C fluxes under 21st century climate change because of changes in atmospheric CO 2 , litter inputs, and vegetation composition that have impacts on C fluxes which cannot be inferred from the training data. Our results provide key insights to future C flux upscaling efforts and expose the potential for inaccurate ML upscaling and forecasting of high-latitude C cycle dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine learning enhanced predictions of ICRF heating: Overcoming numerical limitations via data curation

In this work, we present the development of robust surrogate models for Ion Cyclotron Range of Frequencies (ICRF) and High-Harmonic Fast Wave (HHFW) heating predictions in fusion plasmas. Building upon our previous efforts to achieve real-time capable models, we identify the cause of the outliers found using TORIC in certain HHFW heating scenarios. The outliers are observed to be spurious ion Bernstein wave (IBW)-like modes caused by a wavelength control algorithm designed to address challenging scenarios with high perpendicular wavenumbers. The effect arises from the modulation in the perpendicular susceptibility, which can induce sign reversal and IBW-like propagation for scenarios featuring normalized ion Larmor radius λ i ≫ 1. We use TORIC with this algorithm disabled to generate a novel HHFW-NSTX database that is free of outliers. Surrogate models trained on this database, including Random Forest Regressor (RFR), Multi-Layer Perceptrons, and Gaussian Process Regressors (GPR), demonstrate the ability to accurately predict HHFW heating profiles, with regression scores of R 2 ∈[0.93−0.99]. Additionally we demonstrate that it is possible to generalize predictions beyond training data by the use of both RFR and GPR models, enabling the prediction of scenarios previously limited to the original model. GPR models also provide uncertainty quantification, offering insights into model confidence. This work introduces a comprehensive Verification, Validation, and Uncertainty Quantification methodology for surrogate modeling, applicable not only to ICRF heating but also to other RF heating challenges and fusion physics problems. Beyond accelerated inference, these models show effective extrapolation capabilities, providing an alternative for addressing numerical challenges.

Artificial neural networks↗
DOE OSTI2025

Bayesian And Human Reliability Analysis (hra)-aided Method For The Reliability Analysis Of Software (bahamas)

The purpose of the BAHAMAS code is to provide a simplified process for performing quantitative evaluations of software reliability. The Bayesian and Human Reliability Analysis (HRA)-Aided method for the Reliability Analysis of software (BAHAMAS) was developed specifically to perform quantification under limited data conditions, i.e., when limited testing or operational data are available, such as during early development stages. BAHAMAS essentially examines the quality of a software development life cycle to determine the probability of specific types of software failure. BAHAMAS will have modules to support user input for detailed and simplified analyses. The user interface will also support software common cause failure analysis.

Wang, Congjian (0000000207789927)↗
NASA NTRS2020

The NANOGrav 11 yr data set: Limits on Gravitational Wave Memory

The mergers of supermassive black hole binaries (SMBHBs) promise to be incredible sources of gravitational waves (GWs).While the oscillatory part of the merger gravitational waveform will be outside the frequency sensitivity range of pulsar timing arrays, the nonoscillatory GW memory effect is detectable. Further, any burst of GWs will produce GW memory, making memory a useful probe of unmodeled exotic sources and new physics. We searched the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) 11 yr data set for GW memory. This data set is sensitive to very low-frequency GWs of ∼3 to 400 nHz (periods of ∼11 yr–1 month). Finding no evidence for GWs, we placed limits on the strain amplitude of GW memory events during the observation period. We then used the strain upper limits to place limits on the rate of GW memory causing events. At a strain of 2.5 × 10−14, corresponding to the median upper limit as a function of source sky position, we set a limit on the rate of GW memory events at <0.4 yr−1. That strain corresponds to an SMBHB merger with reduced mass of ηM ~2 × 1010Mmoonand inclination of ι = π/3 at a distance of 1 Gpc. As a test of our analysis, we analyzed the NANOGrav 9 yr data set as well. This analysis found an anomolous signal, which does not appear in the 11 yr data set. This signal is not a GW, and its origin remains unknown.

K Aggarwal↗
NASA NTRSDate not supplied

Economic Impact Assessments (EIA) of application of GEOGLOWS in Ecuador: Data Gaps, Limitations and Recommendations

In 2022, the United Nations launched the Early Warnings for All (EW4ALL) Program to establish global early warning systems by 2027. To assess the impact of the substantial $3.1 billion annual investment over five years, EW4ALL will consider factors that will require national coordination for the data needed for these assessments. In 2023, Ecuador was identified as one of the world's most climate-vulnerable countries, emphasizing the need to enhance its early warning systems. In 2020, the SERVIR Amazonia hub implemented the GEOGLOWS streamflow forecast service in collaboration with Ecuador's national meteorological agency (INAMHI). GEOGloWS provides 15-day ensemble forecasts and 80 years of historical streamflow data for every river worldwide through a free web service. The World Meteorological Organization has recognized this initiative as essential in contributing to the UN's call to ensure an 'Early Warning for All' by 2027. In 2023, as part of NASA's continuous efforts to fund research for Policy-Relevant Implementations, an economic impact assessment (EIA) was performed to understand the potential socioeconomic benefits of Early streamflow predictions in Ecuador using the GEOGLOWS service. Preliminary findings highlighted that gaps remain in effectively integrating socioeconomic and Earth observation (EO) data to capture the total value of these predictions. Implementing GEOGLOWS has led to valuable hydrological forecasts; however, the total economic benefits have yet to be documented. This study addresses the gaps and makes recommendations for future work that should focus on capturing the socio-economic benefits and costs associated with these forecasts, including their impact on decision-making at national and local levels. Despite the daily use of GEOGLOWS by key figures, including the President of Ecuador, the need for comprehensive recommendations and assessments is urgent.

Reetwika Basu↗
DOE OSTI2023

Pragmatic Uncertainty Quantification and Propagation in Inverse Estimation of Structural Dynamics Parameters given Material Property Uncertainties and Limited Sensor Data

In this report we demonstrate some relatively simple and inexpensive methods to effectively account for various sources of epistemic lack-of-knowledge type uncertainty in inverse problems. The demonstration problem involves inverse estimation of six parameters of a bolted joint that attaches a kettlebell shaped object to a thick plate. The parameters are efficiently inverted in a modal-based model calibration using gradient-based optimization. Two material properties of the kettlebell are treated as uncertain to within given epistemic uncertainty bounds. We apply and test interval and sparse-sample probabilistic approaches to account for uncertainty in the estimated parameters (and various scalar functionals of the parameters as generic quantities of interest, QOIs) due to uncertainties in the material properties. We also investigate the error effects of limited numbers of vibration sensors (accelerometers) on the kettlebell and plate, and therefore abbreviated excitation/response information in the parameter inversions. We propose and demonstrate a Leave-K-Sensors-Out “cross-prediction” UQ approach to estimate related uncertainties on the parameters and QOI functionals. We indicate how uncertainties from material properties and limited sensors are treated in a combined manner. The economical combined UQ approach involves just three to five samples (i.e. three to five inverse simulations), with no added complication or error/uncertainty from use of surrogate models for affordability. Finally, we describe a related economical UQ approach for handling potential parameter solution non-uniqueness and numerical optimization related precision uncertainties in the estimated parameter values. Indicated further research is identified.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data

Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1972

Discussion of the reliability of electron densities and energies interpreted from data and limits on the proton energy and density

Analysis of radio observations of Jupiter were changed to take into account the antenna resolution. A dipole magnetic field with a surface equatorial value of 7 gauss is assumed. The electron temperature is found to increase for r 2.5 Jupiter radii with decreasing r as 1/r cubed, reaching a peak of about 100 MeV at r = 2.5 Jupiter radii. For r 2.5 Jupiter radii, the electron temperature goes as r to the 6th power because of energy lost to radiation. The consequences of making an upper estimate on the proton flux by assuming the magnetic field is loaded with all the energetic protons it can hold are described. The upper limits of proton energy, density, flux, and energy flux are calculated for 1, 2, 2.5, 3, and 6 Jupiter radii. The proton energy and velocity estimates are considered to be fairly reliable; the upper limit to the number density is probably much higher than actuality.

Beard, D. B.↗