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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Characterization of the ORNL SiPM PSD ASIC's Basic Properties

ORNL has developed an ASIC intended for reading out arrays of Silicon Photomultipliers coupled to scintillator materials that exhibit neutron/gamma pulse shape discrimination. The ASIC's basic properties: power consumption, linearity, PSD FoM, and timing resolution have all been measured and the results are presented here.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Maximum Likelihood Estimation: Some Basics

The maximum likelihood estimation is a general estimation procedure. It is often compared to estimation procedures like the ordinary least squares regression or generalized method of moments, to name a few. We discuss some basics about the maximum likelihood estimation, its advantages and disadvantages, and provide an example application to a gamma distribution function.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Maximum Likelihood Estimation: Some Basics

The maximum likelihood estimation is a general estimation procedure. It is often compared to estimation procedures like the ordinary least squares regression or generalized method of moments, to name a few. We discuss some basics about the maximum likelihood estimation, its advantages and disadvantages, and provide an example application to a gamma distribution function.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

Inverse problems, which aim to infer unknown properties of a system using experimental and observational data, are central to addressing many of the U.S. Department of Energy’s (DOE) most critical scientific and engineering challenges. Accurate, computationally efficient, and data-efficient solutions to inverse problems are essential for advancing DOE mission-critical science drivers, including analyzing data from large-scale experimental facilities, optimizing fusion reactor performance, accelerating materials discovery, enhancing geophysical imaging, improving wildfire predictions, and enabling autonomous systems and digital twins. However, these problems are becoming increasingly complex, often involving nonlinear, highdimensional, and interconnected systems and models that span multiple physics and scales, while relying on data with varying quantity, quality, and information content. Compounding these challenges is the uncertainty inherent in DOE-relevant systems, where errors in inputs, noise in data, incompleteness of data, and discrepancies between models and reality constrain the accuracy and precision of solutions. At the same time, the convergence of recent scientific computing trends—scientific machine learning, artificial intelligence, and computing advances such as exascale computing—is creating unprecedented opportunities for tackling these challenges. The cross-cutting nature of inverse problems, combined with their growing complexity and rapidly evolving data and algorithmic demands, strongly motivates the formulation of a prioritized research agenda to maximize their capabilities and impact. In response to this need, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science convened the Workshop on Basic Research Needs for Inverse Problems for Complex Systems Under Uncertainty in June 2025. This workshop brought together experts across disciplines to identify grand challenges and major opportunities in the field. Through collaborative discussions, the workshop defined transformative research directions aimed at addressing the mathematical, statistical, and computational challenges posed by inverse problems under uncertainty. As a result of these efforts, four priority research directions (PRDs) were identified to guide future research and development in this area. These PRDs, summarized below, represent a roadmap for advancing the foundational science and mathematics of inverse problems, enabling robust, scalable, and uncertainty-aware solutions that are critical for DOE applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Characterization of Kariya ( Hildegardia barteri (Mast.) Kosterm) Seed Oil Fatty Acid Methyl Ester Prepared from Basic Catalytic Transesterification

The rising global energy demand, alongside concerns regarding environmental deterioration due to the use of fossil fuels, has spurred extensive investigation into renewable energy alternatives. Biomass-derived biodiesel, especially from lesser-known oil sources, emerges as a promising option. This research focuses on analyzing the fatty acid methyl esters (FAMEs) derived from Kariya (Hildegardia barteri (Mast.) Kosterm) seed oil through basic catalytic transesterification using gas chromatography–flame ionization detector (GC–FID) analysis, assessing its potential as a biodiesel feedstock. Oil extraction from Kariya seeds was carried out using three solvents (n-hexane, ethanol, and a 1:1 blend of hexane and ethanol), followed by transesterification with methanol. Gas chromatography–mass spectrometry (GC–MS) and GC–FID analyses were utilized to identify and quantify FAMEs in the resulting biodiesel. The results revealed various FAMEs, including methyl myristate, methyl palmitate, methyl stearate, methyl oleate, methyl linoleate, and methyl linolenate. Significant differences in FAME composition were observed among the samples, with hexane–ethanol Kariya oil biodiesel (HE-KOB) showing the highest FAME content (76.1%). This combination of solvents exhibited synergistic effects on the composition of HE-KOB, suggesting potential optimization strategies for biodiesel production. Fourier transform infrared spectroscopy (FTIR) provided additional insights into the molecular composition of the biodiesel samples, confirming their biodiesel nature through the identified functional groups such as methyl, methylene, hydrocarbon, ester, aldehyde, and alkene. Thermogravimetric analysis (TGA) for thermal decomposition also gave an insight into FAME composition and its contribution to the degree of conversion of biodiesel to energy. These findings highlight the feasibility of utilizing Kariya seed oil as a biodiesel feedstock, emphasizing the importance of solvent selection and transesterification conditions in optimizing FAME yield and composition. This research contributes to the exploration of underutilized oil sources for sustainable biodiesel production, aligning with the global shift towards cleaner and renewable energy sources.

09 BIOMASS FUELS↗
DOE OSTI2023

The Bimodal Absorption System Imaging Campaign (BASIC). I. A Dual Population of Low-metallicity Absorbers at z < 1

The bimodal absorption system imaging campaign (BASIC) aims to characterize the galaxy environments of a sample of 36 H I-selected partial Lyman limit systems (pLLSs) and Lyman limit systems (LLSs) in 23 QSO fields at z ≲ 1. These pLLSs/LLSs provide a unique sample of absorbers with unbiased and well-constrained metallicities, allowing us to explore the origins of metal-rich and low-metallicity circumgalactic medium (CGM) at z < 1. Here we present Keck/KCWI and Very Large Telescope/MUSE observations of 11 of these QSO fields (19 pLLSs) that we combine with Hubble Space Telescope/Advanced Camera for Surveys imaging to identify and characterize the absorber-associated galaxies at 0.16 ≲ z ≲ 0.84. We find 23 unique absorber-associated galaxies, with an average of one associated galaxy per absorber. For seven absorbers, all with <10% solar metallicities, we find no associated galaxies with logM * ≳ 9.0 within ρ/R vir and |Δv|/v esc ≤ 1.5 with respect to the absorber. We do not find any strong correlations between the metallicities or H I column densities of the gas and most of the galaxy properties, except for the stellar mass of the galaxies: the low-metallicity ([X/H] ≤ –1.4) systems have a probability of $0.39_{–0.15}^{+0.16}$ for having a host galaxy with logM * ≥ 9.0 within ρ/R vir ≤ 1.5, while the higher metallicity absorbers have a probability of $0.78_{–0.13}^{+0.10}$. This implies metal-enriched pLLSs/LLSs at z < 1 are typically associated with the CGM of galaxies with logM * > 9.0, whereas low-metallicity pLLSs/LLSs are found in more diverse locations, with one population arising in the CGM of galaxies and another more broadly distributed in overdense regions of the universe. Using absorbers not associated with galaxies, we estimate the unweighted geometric mean metallicity of the intergalactic medium to be [X/H] ≲ –2.1 at z < 1, which is lower than previously estimated.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Community Solar Project Models: An Introduction to Basic Financial and Administrative Relationships [Slides]

This resource is a collection of visual aids and descriptions to help explain the basic financial and administrative relationships underpinning common community solar project models. The graphics in this resource depict the flow of electricity from generation to consumption and the financial exchanges between the community solar project owner, subscribers, and distribution utility. Community solar models vary based on ownership (e.g., third party, utility), customer type (e.g., multifamily, single-household), and solar array location (e.g., on-site, off-site).

14 SOLAR ENERGY↗
NASA NTRS1964

Stellar evolution. i. a program for the solution of the time-dependent basic equations <sternentwicklung. i. ein program zur losung der zeitabhingigen aufbaugleichungen<

In this paper a program is described for stellar evolution computations, based on the Henyey method. The program shall be used for very different phases of stellar evolution. Therefore, the main program for the solution of the time dependent basic equations is kept as general as possible, while all assumptions about special physical conditions in the star occur only in subprograms. Hence the range of applicability of the program can easily be extended. The system of difference equations and details of the integration method used in the program are given in two appendices. For a first test the program has been applied to the post-main sequence evolution of a star of 7 M; the results will appear in part II and III. Some technical details of these calculations are given and discussed here. The program turned out to be applicable to calculations of the evolution near the main sequence, of phases of core contraction and of nuclear burning in shell sources.

STELLAR EVOLUTION↗