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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 OSTI2025

The DECADE cosmic shear project III: validation of analysis pipeline using spatially inhomogeneous data

We present the pipeline for the cosmic shear analysis of the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog consisting of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. The catalog derives from a large number of disparate observing programs and is therefore more inhomogeneous across the sky compared to existing lensing surveys. First, we use simulated data-vectors to show the sensitivity of our constraints to different analysis choices in our inference pipeline, including sensitivity to residual systematics. Next we use simulations to validate our covariance modeling for inhomogeneous datasets. Finally, we show that our choices in the end-to-end cosmic shear pipeline are robust against inhomogeneities in the survey, by extracting relative shifts in the cosmology constraints across different subsets of the footprint/catalog and showing they are all consistent within 1σ to 2σ. This is done for forty-six subsets of the data and is carried out in a fully consistent manner: for each subset of the data, we re-derive the photometric redshift estimates, shear calibrations, survey transfer functions, the data vector, measurement covariance, and finally, the cosmological constraints. Our results show that existing analysis methods for weak lensing cosmology can be fairly resilient towards inhomogeneous datasets. This also motivates exploring a wider range of image data for pursuing such cosmological constraints.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗
DOE OSTI2020

Effects of cluster correlations on fragment emission in 12 C+ 12 C at 50 MeV/nucleon

The effects of cluster correlations have been studied in the 12 C + 12 C reaction at 50 MeV/nucleon, using three AMD models, the AMD (AMD/D) without any additional cluster correlations, AMD/D-COALS with nucleon correlations based on a coalescence prescription for light cluster formations with A ≤ 4 and AMD-Cluster with an extended cluster correlation in two-nucleon collision processes and a special treatment for intermediate fragment formation with A ≤ 9. The angular distributions and energy spectra of fragments have been simulated and compared with the available experimental data. It is found that the cluster correlations take a crucial role to describe the productions of light charged particles (LCPs) and intermediate mass fragments (IMFs), and the AMD-cluster studied here provides a consistent overall reproduction of the experimental data. It is also shown that the significant effects of the secondary decay processes are involved for the fragment production besides the dynamical productions in the AMD stage. Detailed LCP and IMF production mechanisms involved in the intermediate energy heavy ion collisions are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Cosmology From CMB Lensing and Delensed EE Power Spectra Using 2019-2020 SPT-3G Polarization Data

From CMB polarization data alone we reconstruct the CMB lensing power spectrum, comparable in overall constraining power to previous temperature-based reconstructions, and an unlensed E -mode power spectrum, with clear detections of the third through tenth acoustic peaks. The observations, taken in 2019 and 2020 with the South Pole Telescope (SPT) and the SPT-3G camera, cover 1500 deg 2 at 95, 150, and 220 GHz with arcminute resolution and roughly 4.9 µ K-arcmin coadded noise in polarization. The power spectrum estimates, together with systematic parameter estimates and a joint covariance matrix, follow from a Bayesian analysis using the Marginal Unbiased Score Expansion (MUSE) method. The E -mode spectrum at ℓ > 2000 and lensing spectrum at L > 350 are the most precise to date. Assuming the ΛCDM model, and using only these SPT data and priors on τ and absolute calibration from Planck, we find H 0 = 66.81 ± 0.81 km/s/Mpc, comparable in precision to the Planck determination and in 5.4 σ tension with the most precise H 0 inference derived via the distance ladder. We also find S 8 ≡ σ 8 (Ω m /0.3) 0.5 = 0.850 ± 0.017, providing further independent evidence of a slight tension with low-redshift structure probes. The ΛCDM model provides a good simultaneous fit to the combined Planck, ACT, and SPT data, and thus passes a powerful test. Combining these CMB datasets with BAO observations, we explore extensions to the ΛCDM model. We find that the effective number of neutrino species, spatial curvature, and primordial helium fraction are consistent with standard model values, and that the 95% confidence upper limit on the neutrino mass sum is 0.075 eV, close to the minimum sum expected from observations of solar and atmospheric neutrino oscillations. The SPT data are consistent with the somewhat weak (< 3 σ ) preference for excess lensing power seen in Planck and ACT data relative to predictions of the ΛCDM model given the combined Planck, ACT, and BAO data sets. Finally, we also detect at greater than 3 σ the influence of non-linear evolution in the CMB lensing power spectrum and discuss it in the context of the S 8 tension. Forthcoming SPT-3G analyses will feature deeper and wider observations in temperature and polarization, providing even tighter constraints and more powerful tests of the ΛCDM model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

A call to standardize metrics for monitoring baleen whales near marine construction activities

Effective monitoring is necessary to protect marine mammal species during the construction of offshore infrastructure. The tools for detecting or monitoring marine mammals span traditional (e.g., visual observers, optical cameras), to newer (e.g., passive acoustic monitoring, infrared cameras, tags), and emerging (e.g., satellite imagery, environmental DNA, dimethyl sulfide concentration) technologies. Some are better suited for use during offshore development; however, peer-reviewed literature does not typically evaluate and report on the performance of these various technologies. We define a minimum set of metrics related to efficacy (i.e., confusion matrix, precision and recall, probability of missed mitigation), detection range (i.e., maximum and reliable detection range, spatial resolution), and data delivery (i.e., detection latency, system reliability, temporal resolution) that we recommend are needed to assess the utility of monitoring technologies for this purpose. Following a literature review of relevant studies, we highlight which publications reported these metrics and used multiple technologies to compare relative performance. We also emphasize the benefits of multi-modal approaches and recommend performance assessments through modeling or large-scale collaborative field testing. These metrics will standardize data collection, reporting, and analysis; promote consistent and comparable results; and foster collaboration among developers, regulatory agencies, and scientists. This may lead to the co-development of technology that achieves multiple goals, has greater application, and can answer research questions while collecting data to fulfill permitting requirements. These metrics may also inform decisions on what systems regulatory agencies might consider using and reduce monitoring costs, which is critical to support the marine sector's rapid growth alongside marine mammal conservation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Single-size and cluster dynamics modeling of intra-granular fission gas bubbles in UO 2

For this work, we perform simulations of intra-granular fission gas bubble evolution in UO 2 using both a relatively simple, computationally inexpensive single-size model and a detailed cluster dynamics model. Simulations encompass 36 experimental cases from 4 different databases, covering various temperature and burnup levels. We systematically compare results from the two models to each other and to post-irradiation experimental data of bubble average size and number density. Overall, the model-to-model comparisons reveal an excellent agreement across the set of simulations. This outcome indicates that, in spite of the underlying assumptions, the single-size model provides a good approximation of the complex physical behavior that is more rigorously described by the cluster dynamics model. Qualitatively, both models reproduce the trends of the experimental data with temperature and burnup correctly. Quantitatively, calculated results are either in good agreement with the data or within errors that appear consistent with the inherent uncertainties. Moreover, for the single-size model, we demonstrate and assess a multiscale approach whereby values for the fission gas atom diffusion coefficient from separate atomistic calculations are used. Systematic comparisons to experimental data point out a credible accuracy of the multiscale model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Spatial Study 2022: Water Column, Sediment, and Total Ecosystem Respiration Rates across the Yakima River Basin, Washington, USA (v2)

This dataset supports a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin and is associated with the manuscript “Sediment-associated processes account for most of the spatial variation in ecosystem respiration in the Yakima River basin” submitted to Nature Communications Earth & Environment (Garayburu-Caruso et al., in review). The dataset provides ecosystem metabolism estimates generated from streamMetabolizer (Appling et al.; 2018) using data collected during the same five-week period at 48 sites within multiple rivers throughout the Yakima River Basin in Washington, USA. Additionally, it includes the scripts used for the analysis and producing the figures in the manuscript. The contents include streamMetabolizer inputs and outputs and additional relevant data needed to generate the main manuscript results. The data included are: total ecosystem respiration, water respiration, calculated sediment-associated respiration, gross primary production outputs from the river corridor model for the Yakima River Basin, median grain size (d50), depth, dissolved oxygen, water temperature, pressure, and annual oxygen consumption. The associated GitHub repository can be found at https://github.com/river-corridors-sfa/SSS_metabolism. Samples collected during this study were labeled as “Second Spatial Study” or “SSS.” Raw time series sensor data, total suspended solids, and depth data from SSS were published at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1969566. A subset of data from the SSS samples were published in the contiguous United States (CONUS)-Scale Model-Sample (CM) study data package available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689 that presents data from across the CONUS. They include dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC), total nitrogen (TN), grain size, aerobic sediment respiration, dissolved oxygen (DO), and temperature. Parent IDs and Site IDs are consistent between the SSS and CM data packages, and they can be mapped directly so data across packages can be used together. Field metadata for the samples in this da This dataset is comprised of one main data folder with four subfolders. The main data folder contains of (1) file-level metadata; (2) data dictionary; (3) total/water column/sediment respiration; (4) gross primary production (GPP); (5) median grain size (d50); and (6) annual oxygen consumption. The “Figures” subfolder contains the figures used in the paper and all intermediate files (including geospatial files). The “Published_Data” contains a readme directing the user to download the public data to reproduce analyses and figures. The “Scripts” folder contains all scripts used in the analyses that were not part of running StreamMetabolizer. Lastly, the “Stream_Metabolizer” folder contains all files associated with running StreamMetabolizer including (1) model input files, (2) model output files, (3) processing scripts, (4) histogram plots of the outputs, and (5) an R project. All files are .csv, .pdf, .R, .Rmd, .Rproj, .html, .png, .txt, .qgz, .cpg, .dbf, .prj, .shp, .shp.ea.iso.xml, .shp.iso.xml, .shx, .sbn. ta package can be found at either link. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

BEPAM Model Code and CABBI Simulation Results for "The Economic and Environmental Costs and Benefits of the Renewable Fuel Standard"

This dataset contains BEPAM model code and input data to replicate the outcomes for "The Economic and Environmental Costs and Benefits of the Renewable Fuel Standard". The dataset consists of: (1) The replication codes and data for the BEPAM model. The code file is named as output.gms. (BEPAM-Social cost model-ERL.zip) (2) Simulation results from the BEPAM model (BEPAM_Simulation_Results.csv) * Item (1) is in GAMS format. Item (2) is in text format.

Cost-Benefit Analysis↗
DOE OSTI2023

FrESCO

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information and summary statistics about cancer prevalence to facilitate population level monitoring of cancer incidence. Extracting cancer phenotype from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating this information extraction process from cancer pathology reports has the potential to improve not only the quality of the data by extracting information in a consistent manner across registries, but to improve the quality of patient outcomes by reducing the time to assimilate new data and enabling time-sensitive applications such as precision medicine. Here we present FrESCO: Framework for Exploring Scalable Computational Oncology, a modular deep-learning natural language processing (NLP) library for extracting pathology information from clinical text documents.

Spannaus, Adam [Oak Ridge National Lab. (ORNL), Oa↗
DOE OSTI2025

Moltensaltpropnet

MoltenSaltPropnet is a physics-informed machine learning framework that aims to predict the thermophysical properties of molten fluoride and chloride salt mixtures, which are crucial for the design and safety of Generation IV molten salt reactors. The code processes data from the Molten-Salt Thermal Properties Database (MSTDB-TP) and the Janz compendium, converting critically evaluated correlations into fast, differentiable surrogate models for density, viscosity, thermal conductivity, and heat capacity across 448 distinct salt systems. The implementation consists of several key components: 1. Data Curation: The code parses and cleans the raw data, normalizing elemental mole fractions and extracting relevant regression coefficients for various thermophysical properties. 2. Feature Engineering: It generates fixed-length numerical descriptors that encapsulate the composition and temperature, incorporating polynomial interaction terms and dimensionality-reduction techniques to optimize model performance. 3. Coefficient Learning: Four different machine learning architectures are employed: a deep residual network (ResNet), a Kolmogorov–Arnold network (KAN), a sparsity-inducing neural network (SNN), and classical regression models. Each model learns to predict coefficients that define the temperature-dependent correlations for the thermophysical properties. 4. Property Reconstruction: The predicted coefficients are used to compute temperature-dependent property values, ensuring positivity and monotonic trends through a composite loss function that enforces physical constraints. 5. User Interface: An open-source web application enables users to filter the database, train task-specific models, and visualize the results, allowing for rapid exploration of candidate salt mixtures. MoltenSaltPropnet bridges the gap between limited experimental data and high-fidelity reactor simulations, providing a powerful tool for researchers in the field of molten salt reactors and advanced nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
DOE OSTI2024

A framework for inferring and analyzing pharmacotherapy treatment patterns

Background: To discover pharmacotherapy prescription patterns and their statistical associations with outcomes through a clinical pathway inference framework applied to real-world data. Methods: We apply machine learning steps in our framework using a 2006 to 2020 cohort of veterans with major depressive disorder (MDD). Outpatient antidepressant pharmacy fills, dispensed inpatient antidepressant medications, emergency department visits, self-harm, and all-cause mortality data were extracted from the Department of Veterans Affairs Corporate Data Warehouse. Results: Our MDD cohort consisted of 252,179 individuals. During the study period there were 98,417 emergency department visits, 1,016 cases of self-harm, and 1,507 deaths from all causes. The top ten prescription patterns accounted for 69.3% of the data for individuals starting antidepressants at the fluoxetine equivalent of 20-39 mg. Additionally, we found associations between outcomes and dosage change. Conclusions: For 252,179 Veterans who served in Iraq and Afghanistan with subsequent MDD noted in their electronic medical records, we documented and described the major pharmacotherapy prescription patterns implemented by Veterans Health Administration providers. Ten patterns accounted for almost 70% of the data. Associations between antidepressant usage and outcomes in observational data may be confounded. The low numbers of adverse events, especially those associated with all-cause mortality, make our calculations imprecise. Furthermore, our outcomes are also indications for both disease and treatment. Despite these limitations, we demonstrate the usefulness of our framework in providing operational insight into clinical practice, and our results underscore the need for increased monitoring during critical points of treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Classifying and analyzing small-angle scattering data using weighted k nearest neighbors machine learning techniques

A consistent challenge for both new and expert practitioners of small-angle scattering (SAS) lies in determining how to analyze the data, given the limited information content of said data and the large number of models that can be employed. Machine learning (ML) methods are powerful tools for classifying data that have found diverse applications in many fields of science. Here, ML methods are applied to the problem of classifying SAS data for the most appropriate model to use for data analysis. The approach employed is built around the method of weighted k nearest neighbors (wKNN), and utilizes a subset of the models implemented in the SasView package (https://www.sasview.org/) for generating a well defined set of training and testing data. The prediction rate of the wKNN method implemented here using a subset of SasView models is reasonably good for many of the models, but has difficulty with others, notably those based on spherical structures. A novel expansion of the wKNN method was also developed, which uses Gaussian processes to produce local surrogate models for the classification, and this significantly improves the classification accuracy. Further, by integrating a stochastic gradient descent method during post-processing, it is possible to leverage the local surrogate model both to classify the SAS data with high accuracy and to predict the structural parameters that best describe the data. The linking of data classification and model fitting has the potential to facilitate the translation of measured data into results for both novice and expert practitioners of SAS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Two-particle azimuthal correlations in γp interactions using pPb collisions at s NN = 8.16 TeV

The first measurements of the Fourier coefficients ( V n Δ ) of the azimuthal distributions of charged hadrons emitted from photon-proton (γp) interactions are presented. The data are extracted from 68.8 nb −1 of ultra-peripheral proton-lead (pPb) collisions at s NN = 8.16 TeV using the CMS detector. The high energy lead ions produce a flux of photons that can interact with the oncoming proton. This γp system provides a set of unique initial conditions with multiplicity lower than in photon-lead collisions but comparable to recent electron-positron and electron-proton data. The V n Δ coefficients are presented in ranges of event multiplicity and transverse momentum ( p T ) and are compared to corresponding hadronic minimum bias pPb results. For a given multiplicity range, the mean p T of charged particles is smaller in γp than in pPb collisions. For both the γp and pPb samples, V 1 Δ is negative, V 2 Δ is positive, and V 3 Δ consistent with 0. For each multiplicity and p T range, V 2 Δ is larger for γp events. The γp data are consistent with model predictions that have no collective effects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Predicting oxidation damage in ultra high-temperature borides: A machine learning approach

Ultra-high temperature (UHT) borides are ceramics materials with melting points above 3000 °C for structural applications in extreme environments. However, at temperatures exceeding 1600 °C and under oxidizing conditions, the material suffers from detrimental degradation. Optimized design and performance of diboride materials under such extreme conditions requires filling the missing composition-microstructure-oxidation gap. This study proposes a computational data-driven framework to connect the processing and microstructure of Ultra-high temperature borides with the oxidation damage. Random Forest Regressor (RFR) model is adopted to forecast the oxide scale thickness developed after oxidation testing based on processing variables and microstructural features. The model trained on a dataset consisting of 107 samples of experimental data extracted from the literature aims to predict oxidation damage. With proper data manipulation and fine model tuning, the predictor could forecast the oxide scale thickness of UHT diborides with a Mean Absolute Error of 37.45 μm and an R-square of 0.83. This model could be used as a high-throughput scheme to design and test new UHT diborides materials computationally. Furthermore, a model with larger composition capabilities could also be developed in the future as more experimental data become available.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Seismic savanna: machine learning for classifying wildlife and behaviours using ground‐based vibration field recordings

Abstract We develop a machine learning approach to detect and discriminate elephants from other species, and to recognise important behaviours such as running and rumbling, based only on seismic data generated by the animals. We demonstrate our approach using data acquired in the Kenyan savanna, consisting of 8000 h seismic recordings and 250 k camera trap pictures. Our classifiers, different convolutional neural networks trained on seismograms and spectrograms, achieved 80%–90% balanced accuracy in detecting elephants up to 100 m away, and over 90% balanced accuracy in recognising running and rumbling behaviours from the seismic data. We release the dataset used in this study: SeisSavanna represents a unique collection of seismic signals with the associated wildlife species and behaviour. Our results suggest that seismic data offer substantial benefits for monitoring wildlife, and we propose to further develop our methods using dense arrays that could result in a seismic shift for wildlife monitoring.

Szenicer, Alexandre↗
DOE OSTI2021

Quality Assurance and Quality Control (QA/QC) of Meteorological Time Series Data for Billy Barr, East River, Colorado USA

A comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework consists of three major phases: Phase 1—Preliminary raw data sets exploration, including time formatting and combining datasets of different lengths and different time intervals; Phase 2—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme values; and Phase 3—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado) were analyzed. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.The files that are in this data package include one excel file, converted to CSV format (Billy_Barr_raw_qaqc.csv) that contains the raw meteorological data, i.e., input data used for the QA/QC analysis. The second CSV file (Billy_Barr_1hr.csv) is the QA/QC and flagged meteorological data, i.e., output data from the QA/QC analysis. The last file (QAQC_Billy_Barr_2021-03-22.R) is a script written in R that implements the QA/QC and flagging process. The purpose of the CSV data files included in this package is to provide input and output files implemented in the R script.

54 ENVIRONMENTAL SCIENCES↗