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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

Simulation-Based Inference for Neutrino Interaction Model Tuning

This project demonstrates, for the first time, the application of simulation-based inference (SBI) techniques to tune neutrino–nucleus interaction models. Using a mock dataset based on the MicroBooNE tuning of the GENIE event generator, our approach employs a Neural Posterior Estimator (NPE) with Masked Autoregressive Flows (MAF) to infer the posterior distributions of key GENIE parameters directly from simulated histograms. The workflow provides a scalable and amortized framework for performing likelihood-free inference in high-dimensional parameter spaces, offering a pathway to more efficient and uncertainty-aware model tuning for next-generation neutrino experiments such as DUNE and SBND.

Tame-Narvaez, KarlaMaria [Fermi National Accelerat↗
DOE OSTI2021

Simulations of ENSO Phase-locking in CMIP5 and CMIP6

The characteristics of El-Niño-Southern Oscillation (ENSO) phase-locking in observations and CMIP5 and CMIP6 models are examined in this study. Two metrics based on the peaking month histogram for all El Niño and La Niña events are adopted to delineate the basic features of ENSO phase-locking in terms of the preferred calendar month and strength of this preference. It turns out that most models are poor at simulating the ENSO phase-locking, either showing little peak strengths or peaking at the wrong seasons. By deriving ENSO’s linear dynamics based on the conceptual recharge oscillator (RO) framework through the seasonal linear inverse model (sLIM) approach, various simulated phase-locking behaviors of CMIP models are systematically investigated in comparison with observations. In observations, phase-locking is mainly attributed to the seasonal modulation of ENSO’s SST growth rate. In contrast, in a significant portion of CMIP models, phase-locking is co-determined by the seasonal modulations of both SST growth and phase-transition rates. Further study of the joint effects of SST growth and phase-transition rates suggests that for simulating realistic winter peak ENSO phase-locking with the right dynamics, climate models need to have four key factors in the right combination: (1) correct phase of SST growth rate modulation peaking at the fall; (2) large enough amplitude for the annual cycle in growth rate; (3) amplitude of semi-annual cycle in growth rate needs to be small; and (4) amplitude of seasonal modulation in SST phase-transition rate needs to be small.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Two Large-Scale Meteorological Patterns are Associated with Short-Duration Dry Spells in the Northeastern United States

Large-scale meteorological pattern (LSMP)–based analysis is used in a novel way to understand meteorological conditions before and during short-duration dry spells over the northeastern United States. These LSMPs are useful to assess models and select better-performing models for future projections. Dry-spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting 12 days or longer, which correspond to ~10% of dry-spell events, are examined. The 500-hPa stream-function anomaly fields for the first 12 days of each event are time averaged, and k -means clustering is applied to isolate the dry-spell-related LSMPs. The first cluster has a strong low pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong high pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold-air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm tracks are generally shifted southward of the region during the dry spells. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but different remote, properties. Although dry spells occur with greater frequency during drought months, most dry spells occur during nondrought months. Significance Statement: This study examines the large-scale weather patterns and meteorological conditions associated with dry-spell events lasting at least 2 weeks while affecting the northeastern United States. A statistical approach groups events together on the basis of similar atmospheric features. We find two distinct sets of patterns that we call large-scale meteorological patterns. These patterns reduce moisture, foster localized sinking, and shift the storm track southward along the Atlantic seaboard, all of which reduce precipitation. Besides greater understanding, knowing the meteorological patterns during short-term dryness in the region provides an important tool to assess how well atmospheric models reproduce these specific patterns. More dry spells occur in non-drought months than in drought months, which means that dry spells can occur without preexisting drought conditions.

58 GEOSCIENCES↗
DOE OSTI2023

DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), index models needed to be developed to map values in geoscientific exploration datasets to favorability index values. This GDR submission includes those index models. Index models were created by binning values in exploration datasets into chunks based on their favorability, and then applying a number between 0 and 5 to each chunk, where 0 represents very unfavorable data values and 5 represents very favorable data values. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were created using histograms of the distributions of each exploration dataset in combination with literature and input from experts about what combinations of geophysical, geological, and geochemical signatures are considered favorable at Newberry. This is in attempt to create similar sized bins based on the current understanding of how different anomalies map to favorable areas for the different types of geothermal plays (i.e., conventional hydrothermal, superhot EGS, and supercritical). For example, an area of partial melt would likely appear as an area of low density, high conductivity, low vp, and high vp/vs. This means that these target anomalies would be given high (4 or 5) index values for the purpose of imaging the heat source. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were produced for the following datasets: - Geologic model - Alteration model - vp/vs - vp - vs - Temperature model - Seismicity (density*magnitude) - Density - Resistivity - Fault distance - Earthquake cutoff depth model

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

NOvA 2020 official data release (13.6E20 neutrino + 12.5E20 antineutrino)

NOvA results of the 2020 joint analysis of nue appearance and numu disappearance data presented in Phys. Rev. D 106, 032004:https://doi.org/10.1103/PhysRevD.106.032004Note: these contained plots are slightly different from the ones shown at NEUTRINO 2020.Exposure: 13.6E20 protons on target, neutrino-enhanced beam12.5E20 protons on target, antineutrino-enhanced beamParameters not constrained by NOvA:- th13 is assumed to be Gaussian, ss2th13 = 0.085 +/- 0.003 (PDG 2019, Phys. Rev. D98, 030001 (2018) and 2019 update, https://pdg.lbl.gov/2019/listings/rpp2019-list-neutrino-mixing.pdf)- ss2th12 = 0.851, dm21=7.53e-5 are fixed at 2019 PDG valuesSignificances are obtained with the Feldman-Cousins procedure.Files contain TGraphs for the following:- ssth23 vs deltaCP 1,2,3 sigma and 90% CL contours, for NO or IO- dmsq32 vs ssth23 1,2,3 sigma and 90% CL contours, for NO or IO- deltaCP profile for NO UO, NO LO, IO LO, IO UO- ssth23 profile for NO, IO- dmsq32 profile for NO UO, NO LO, IO LO, IO UOContour files also include a TMarker with the best-fit parameters (in the NO)An additional file containing the predictions for all channels’ signal and background components computed at the NOvA best-fit oscillation parameters and systematic pull terms. Numu samples include the no-oscillation case as well.Please note that any fits performed with these histograms are not expected to exactly reproduce the official NOvA results as parameterizations of the numerous systematic uncertainties considered in the official fits are not included in this release.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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 OSTI2024

CaloFlow for CaloChallenge dataset 1

CALOFLOW is a new and promising approach to fast calorimeter simulation based on normalizing flows. Applying CALOFLOW to the photon and charged pion ≥ant showers of Dataset 1 of the Fast Calorimeter Simulation Challenge 2022, we show how it can produce high-fidelity samples with a sampling time that is several orders of magnitude faster than ≥ant. We demonstrate the fidelity of the samples using calorimeter shower images, histograms of high level features, and aggregate metrics such as a classifier trained to distinguish CALOFLOW from ≥ant samples.

Physics↗
DOE OSTI2025

Sparks in the dark

This study presents a novel method for the definition of signal regions in searches for new physics at collider experiments. By leveraging multi-dimensional histograms with precise arithmetic and utilizing the SparkDensityTree library, it is possible to identify high-density regions within the available phase space, potentially improving sensitivity to very small signals. Inspired by a search for dark mesons at the ATLAS experiment, CMS open data is used for this proof-of-concept intentionally targeting an already excluded signal. Signal regions are defined based on density estimates of signal and background. These preliminary regions align well with the physical properties of the signal while effectively rejecting background events.

Gudnadottir, Olga Sunneborn [Uppsala Univ. (Sweden↗
DOE OSTI2020

Detectors and Data [Slides]

Summary: A gamma-ray spectrum is a histogram of energy deposited in a detector and a ‘fingerprint’ for a particular radionuclide. Shielding can reduce the intensity of peaks in a spectrum, especially at lower energies. HPGe detectors provide the sharpest picture in terms of gamma ray data. Regarding moderation and ³He detectors–too little and neutrons are too fast to be detected; too much and neutrons may get absorbed before reaching the ³He tube.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Brief Introduction to Gamma Spectroscopy [Slides]

Radiation energy is measured in units of electron volts (eV). Photon energies are like a fingerprint that allow us to identify specific radioisotopes. Radiation interacts with our detector & deposits energy. That deposited energy produces a proportional signal. Signals are sorted and analyzed. Signals are placed in a histogram, making a gamma spectrum. Analyze gamma spectrum and identify the radioactive material.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

FY21 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g., height, color and grayscale values; all as functions of a location in a plane projection). A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data, flag significant features, execute Machine Learning (ML) algorithms, output parameters for trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Features can be called out by user-specified thresholds, manual labeling or machine learning algorithms when they have been completed. The ability to rapidly label data is important because of the volume of data required for training machine learning algorithms. The GUI has the flexibility to allow addition of improved ML algorithms, methods for data visualization, and statistical computations. Statistical analyses via the GUI include areas of pits within a defined range of pit depths, correlations between Red-Green-Blue (RGB) or grayscale intensity and relative surface height, covariances between values associated with features, and feature histograms. The development of supervised machine learning algorithms, however, has been hindered by a lack of training data. The machine learning algorithms for crack identification are being refined but require improvements to the true positive rate for crack detection. This shortcoming is an artifact of the limited training data currently available, perhaps more so than the structure of the neural networks. At present, the best results are had from a consensus over an ensemble of randomly generated Deep Neural Network (DNN) or Convolutional Neural Network (CNN) algorithms. Although the consensus accuracy method has yielded optimum true positive and true negative rates in excess of 80%, additional validation testing is necessary. In addition to the suite of LCM data that was initially used, and which represents the majority of the work presented in this report, WAMS image data was also reviewed at a preliminary level. The review included a comparison between image resolution and dynamic range for each method. WAMS (ZON file) image data was found to have a pixel pitch of 3.69μm compared to 1 μm for the LCM (vk4 file) data, which implies a lower resolution for the WAMS images. Conversely, the ratio of dynamic range of the WAMS data to the LCM data was approximately 41:20 for height data, suggesting that information from WAMS should more accurately determine the depth of pits. At present, the significance of the greater dynamic range of the WAMS data relative to the LCM data has not yet been evaluated.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Outflow from Post-Black Hole-Neutron Star Merger Disks [Poster]

Histograms of mass of a given ratio of electron to baryon number in winds from a post-merger disk for four disks of different masses. This ratio determines how many elements heavier than iron, if any, are produced by a black hole-neutron star merger event. Analysis performed by PhD student Sanjana Curtis (NCSU, now graduated) on Capulin in collaboration with her advisor, Carla Frohlich, and LANL staff Jonah Miller.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Reducing Uncertainty of Fielded Photovoltaic Performance (Final Technical Report)

Improved analysis and reporting of photovoltaic (PV) field performance increases the certainty of owners and financiers that systems will perform as expected. Advanced module technologies (e.g., PERC, HJT, and bifacial) introduce new degradation mechanisms and performance characteristics. The FY19-21 Reducing Uncertainty project leveraged data from the ever-increasing PV fleet to develop models and understanding of the field performance of existing and new technologies. Specifically, we accomplished: report on field performance and degradation rates for high-efficiency silicon (HJT, PERC, IBC) and more conventional technologies; developed automated analysis techniques to quantify system performance (performance ratio, energy yield) and production shortfalls (soiling, degradation, availability); refined the RdTools software toolkit to bring standard, validated analysis techniques to bear on third-party data; analyzed and reported on large datasets including Treasury data and Lawrence Berkeley National Laboratory's Utility-Scale dataset to expand the high-quality degradation-rate histogram published previously; worked with industry partners and the DuraMAT data hub to enable private parties to share and aggregate PV production data anonymously, leveraging cloud-based data analysis infrastructure and publishing on US fleet-scale performance comprising over 7GW of operating systems. (https://www.nrel.gov/pv/fleet-performance-data-initiative.html). Through our industry collaborations we have engaged in NDA-covered data transfer with twelve PV fleet owners as of January 2022, with more agreements in negotiation. Our scalable cloud-based time series database contains over 30 billion rows (20TB) of PV time series data, representing over 1700 commercial and utility-scale systems, and over 7.2 GW of DC capacity (Fig 1). Initial field performance results have been distributed in several public reports. Because our fleet composition and data quality methods are continually improving, annual updates to these results are published to our PV Fleet webpage [ https://www.nrel.gov/pv/fleet-performance-data-initiative.html ] and DuraMAT data hub [DOI: 10.21948/1842958]. Another existing dissemination channel used for observed soiling losses is a map we maintain for soiling losses. Additional products developed include a report detailing fleet-wide performance index, availability, startup loss and snow loss factors, a detailed report on the 1603 grant dataset comprising over 100,000 PV systems with failure and performance details and a utility-scale report coauthored with LBNL on 31 GW of system performance.

14 SOLAR ENERGY↗
DOE OSTI2020

GNDS Expert Group Report [Slides]

The presentation discusses the GNDS-1.9 specifications that were published during the Summer 2020 and a webinar that occurred on 8 July 2020. It also discusses the goals of the GNDS-2.0, including the intention to satisfy the remaining SG-38 goals, to ensure “forwards compatibility” with ENDF-6, and to respond to user needs. It discusses the 2020 status and a poll that was implemented to find out more about how GNDS is being implemented. The presentation states that some projects are far along in their GNDS support and that most are open source with 20% closed source. It also states that GNDS's purpose is to inspire younger scientists with a histogram displaying what fraction of the developers are student, post-docs, and junior staff. The presentation concludes by discussing what is next including major format changes post-GNDS-2.0 and an ENDF to GNDS transition.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Blueprints for Training Information Bottlenecks for Collider Analyses

Dimensionality reduction is a crucial aspect of data analysis in high energy physics, even if accompanied by information loss. Several methods, including histogram- and kernel-based analyses, are only computationally feasible for low-dimensional data. Furthermore, simulation models used in HEP can often only be validated for low-dimensional data. We provide several blueprints for using machine learning to create low-dimensional data representations (continuous event variables and discrete classification labels) for use in signal discovery and parameter estimation tasks. We also describe how to design the learned representation to facilitate a) searches with unknown model parameters and b) validation of simulation models in data control regions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

PV-Finder: ML Based Algorithm for Primary Vertex Identification

he CMS detector at the High-Luminosity Large Hadron Collider (HL-LHC) will operate in challenging conditions with expected pile-up of up to 200 collisions per bunch crossing, necessitating the development of a more resilient primary vertex (PV) reconstruction method to ensure the integrity of data analysis and the efficiency of the CMS triggering system. This contribution describes preliminary studies on a new ML based PV-Finder method for PV identification. The method is based on a model trained using Kernel Density Estimations (KDEs) derived from the positions of reconstructed tracks at the beamline, incorporating uncertainties from track parameters. It also utilizes target histograms, modeled as Gaussian distributions centered on the actual ground truth values of specific primary vertices.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A Python Script to Read MCNP6.3 Surface-Source Files

This report provides a Python script to read an MCNP ® surface source file created with the SSW card with SYM = 0 (i.e., the default symmetry treatment). For background: the general format of an MCNP surface-source file is described in; however, that document did not provide coding and/or a tool to interrogate such files. The current format will not be given in this document other than through the record-read statements necessary for the script to function. The reader capability in this report is augmented with the ability to directly write a couple demonstrative outputs: 1. A comma-separated value (CSV) file containing particle phase-space state information and 2. A Matplotlib histogram of the energy distribution of the particles. This report also describes accompanying verification work that shows the script performing as required with MCNP6.2, MCNP6.3, and (expected) MCNP6.4 surface-source files. However, users of the enclosed script must still verify that the script is behaving correctly for their own work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Mechanical Loads on Spent Nuclear Fuel in the General 30 cm Package Drop Scenario

The U.S. Department of Energy Office of Nuclear Energy (DOE-NE) Spent Fuel and Waste Science & Technology (SFWST) research program is guided by the high-level goal of closing prioritized knowledge gaps related to spent nuclear fuel (SNF) storage and transportation, which are summarized by Saltzstein et al. (2020). One of the high-priority knowledge gaps is the identification and quantification of mechanical loads that are expected to affect SNF during normal conditions of transportation and storage to inform the range of physical SNF test programs. This report uses modeling and analysis methods to estimate the mechanical loads on spent nuclear fuel (SNF) in the general 30 cm package drop scenario. The drop scenario assumes impact limiters are in place in the transportation configuration and the impact surface is perfectly rigid. The goal of this analysis is to consider the universe of potential mechanical loading conditions that can happen to SNF and present the results in a manner that is useful for materials testing, decision making, and regulatory rule making purposes. This study uses validated finite element models and methods to perform a broad parametric study of key variables that can affect the mechanical loads on SNF during a hypothetical package free drop scenario. Physical drop test data from a cask and fuel assembly drop test campaign is the basis for model validation. Additionally, the results of the parametric study are used to inform a damage model, which uses multiple nonlinear regression to estimate the relationships between input variables and output response. The parametric finite element analyses consider thousands of input variable combinations, while the damage model estimates millions of combinations. The breadth of this study provides confidence that the potential range of mechanical loads that SNF might experience during the general 30 cm package drop scenario are characterized well enough to consider this knowledge gap closed. While this report documents the overall peak values calculated in this study, the 95 th percentile values, the histograms, and the observed trends are equally important. This study covered a large range of SNF temperatures, room temperature to 300°C, and burnups, 10 GWd/MTU to 62 GWd/MTU. Each temperature and burnup combination has a different cladding yield strain, so it is more meaningful to summarize the calculated cladding strain response as its factor of safety, which is defined relative to the yield strain. The factor of safety is calculated as the yield strain divided by the peak cladding strain. A factor of safety greater than unity indicates that the cladding remains below yield, whereas a value less than unity is indicative of plastic deformation. In all cases of this study a safety factor over 1.0 was calculated, although in the most limiting case at 300°C the safety factor was only 1.01, which suggests that yielding could occur when additional loads like rod internal pressure are included. When the temperature is restricted to 200°C the limiting safety factor increases to 1.28, which has significant margin to accommodate internal pressure and potential local cladding defects that could cause a local stress concentration. An important trend in the calculated fuel rod mechanical loads is that the 2 nd highest loaded fuel rod in an assembly tends to be significantly lower than the highest loaded rod. The implication is that even if one rod in an assembly experiences a failure the loads would have to be significantly higher to cause two or more rods to fail.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗