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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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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Trace Element Analyses of Micron-Size Particles and Statistical Determination of Minimum Detection Limits

Savannah River National Laboratory (SRNL) has developed expertise in producing homogeneous, ca. 1 m-diameter spherical particles of mixed-element components, wherein dopants can be varied from a trace constituent (ppm) to wt.% concentrations. The samples used for this work are nickel-doped cerium oxide microspheres produced by SRNL. They were initially selected as analogs for plutonium-doped uranium oxide particles and analyzed as part of a larger study to evaluate whether electron probe microanalyzers (EPMA) can be used to characterize nuclear materials as an alternative or complementary method to mass spectrometers. The five samples used in this study contained nominal compositions of 0, 0.004, 0.04, 0.4 and 4 wt.% Ni. They were analyzed by both an Agilent 7900 Q-ICP-MS at SRNL and the JEOL JXA8530F Plus EPMA at the University of Minnesota. In addition to EPMA results (calibrated with high-precision Q-ICP-MS analyses) suggesting that the EPMA could address outstanding nuclear material characterization needs, these samples 1) showcase the ability of the EPMA to quantify not just trace concentrations, but trace concentrations in microparticles (1 m diameter, Fig. 1), and 2) offer a unique opportunity to evaluate the methodology for assessing the minimum detection limits of EPMA analyses.

McSwiggen, Peter [JEOL USA, 11 Dearborn Road, Peab↗
DOE OSTI2025

Bayesian Statistical Analysis for Mass Spectrometric Data Processing

Thermal ionization mass spectrometry (TIMS) is a “gold standard’ technique for actinide isotope amount ratio and assay measurements. • Ubiquitously used in: • Nuclear Nonproliferation • Nuclear Safeguards • Nuclear Forensics • Basic Science • Savannah River National Laboratory (SRNL) installed a new Thermo Scientific TRITON Plus TIMS in early 2024.

McLarty, Ellis C. [Savannah River National Laborat↗
DOE OSTI2026

Statistical Properties of Rainfall in Tasmania (SPORT) Field Campaign Report

The objective of this project is to complement the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility second ARM Mobile Facility (AMF2) deployment at kennaook-Cape Grim (CAPE-k) with a micro-rain radar (MRR-PRO) mounted on top of one of the ARM containers. This was done to locate the MRR-PRO close to the ARM cloud radars (Ka-band ARM Zenith Radar [KAZR] and Marine W-band ARM Cloud Radar [MWACR]). This will allow for a continuous calibration check of the ARM cloud radars against the MRR-PRO and Parsivel-2 disdrometers. Scientifically, it will add one radar frequency to the analysis of vertical profiles of cloud and precipitation properties (K-band).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Material Control & Accounting Statistical Test Modeling Supporting the Low Enriched Fuel Fabrication Facility Using F3M and MAPIT

This report covers the application of the TRi-structural ISOtropic (TRISO) fuel fabrication facility material control & accounting (MC&A) modeling capability developed by Sandia National Laboratories (SNL) under the U.S. Department of Energy (DOE) Materials Protection, Accounting, and Control Technologies (MPACT) program to support the development of MC&A approaches for the Low Enriched Fuel Fabrication Facility (LEFFF) at Los Alamos National Laboratory (LANL).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Statistical and Machine Learning Approaches to Analyzing Pipeline Incidents in the United States (2010–2024)

This study applies machine learning methods to analyze natural gas pipeline incidents in the United States using the Pipeline and Hazardous Materials Safety Administration (PHMSA) Gas Distribution Incident Dataset (2010–2024). The dataset includes over 600 variables describing incident characteristics, infrastructure attributes, and contributing factors associated with unintentional gas releases. The objective is to assess whether these features can reliably predict the underlying cause of pipeline failures. Multinomial logistic regression and Random Forest models were developed to classify incident causes, including excavation damage, corrosion, equipment failure, and natural forces. Results show that excavation damage is both the most frequent and most predictable cause, with models achieving strong performance for this category. However, when excavation damage is excluded, model accuracy declines significantly, with some models performing near random levels. Across all approaches, severe class imbalance and limited variability in key predictors constrain predictive performance. Pipeline age and diameter emerge as the most influential variables, but they provide insufficient discriminatory power to distinguish among less frequent failure types. These findings indicate that non-excavation-related incidents are rare, heterogeneous, and weakly represented in the dataset, limiting the effectiveness of machine learning classification. Overall, this study highlights the structural limitations of the PHMSA dataset for predictive modeling and underscores the need for improved data balance and feature enrichment. The results reinforce excavation damage prevention as the most impactful strategy for reducing pipeline incidents.

03 NATURAL GAS↗