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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Chemical investigation of three plutonium–beryllium neutron sources

Thorough physical and chemical characterization of plutonium–beryllium (PuBe) neutron sources is an important capability with applications ranging from material accountancy to nuclear forensics. Furthermore, characterization of PuBe sources is not trivial owing to range of existing source designs and the need for adequate infrastructure to deal with radiation and protect the analyst. Our study demonstrates a method for characterization of three PuBe sources that includes physical inspection and imaging followed by controlled disassembly and destructive analysis.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2018

Flexible classification with spatial quantile comparison and novel statistical features applied to spent nuclear fuel analysis

Multivariate classification algorithms are a common tool in contemporary data science that have begun to be applied to nuclear forensic analyses. Using a multivariate generalization of quantile–quantile plots for comparing unknown statistical distributions, a new model-free multivariate classifier called the Quantile–Quantile Comparator has been developed and tested on the analysis of simulated irradiated nuclear fuel. A method to estimate the sample-specific probability of having made an incorrect classification decision by analyzing the distribution of quantile comparisons has been developed, and methods have been developed to analyze the partition of the known data library used to analyze unknown data. By analyzing this partition, the analyst may identify classes that are likely to be confused, identify test data that are inconsistent with the data library and return a “None-of-the-Above” classification decision, and optimize the class boundaries. In this paper, the development of these capabilities is described, and a case study using real experimental assay data collected from nuclear fuel extracted from the newly-released In conclusion, Spent Fuel Isotopic Composition Database to demonstrate the efficacy of these capabilities is presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

An introduction to the significance of sample size in particle analyses for nuclear forensics and radiological investigations

Particulate isotopic analysis in nuclear forensics has developed rapidly during the past two decades due to technical advances in determining the isotopic composition of individual particles. This paper introduces basic statistical concepts that can be applied by analysts to understand the importance of statistical adequacy when interpretating particle data. While these basic statistical methods provide a useful point-of-entry to particle data analysis, more sophisticated statistical and modeling approaches are needed to extract maximal information from such datasets in the future.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2026

Mixed-precision numerics in scientific applications: survey and perspectives

The explosive demand for artificial intelligence (AI) workloads has led to a significant increase in silicon area dedicated to lower-precision computations on recent high-performance computing hardware designs. However, mixed-precision capabilities, which can achieve performance improvements of up to 8x compared to double-precision in extreme compute-intensive workloads, remain largely untapped in most scientific applications. A growing number of efforts have shown that mixed-precision algorithmic innovations can deliver superior performance without sacrificing accuracy. These developments should prompt computational scientists to seriously consider whether their scientific modeling and simulation applications could benefit from the acceleration offered by new hardware and mixed-precision algorithms. In this survey, we (1) review progress across diverse scientific domains—fluid dynamics, weather and climate, quantum chemistry, and computational genomics—that have begun adopting mixed-precision strategies; (2) examine state-of-the-art algorithmic techniques such as iterative refinement, splitting and emulation schemes, and adaptive precision solvers; (3) assess their implications for accuracy, performance, and resource utilization; and (4) survey the emerging software ecosystem that enables mixed-precision methods at scale. We conclude with perspectives and recommendations on cross-cutting opportunities, domain-specific challenges, and the role of co-design between application scientists, numerical analysts, and computer scientists. Collectively, this survey underscores that mixed-precision numerics can reshape computational science by aligning algorithms with the evolving landscape of hardware capabilities.

Graphics processing units↗
DOE OSTI2014

Video game console usage and US national energy consumption: Results from a field-metering study

There has been an increased in attention placed on the energy consumption of miscellaneous electronic loads in buildings by energy analysts and policymakers in recent years. The share of electricity consumed by consumer electronics in US households has increased in the last decade. Many devices, however, lack robust energy use data, making energy consumption estimates difficult and uncertain. Video game consoles are high-performance machines present in approximately half of all households and can consume a considerable amount of power. The precise usage of game consoles has significant uncertainty, however, leading to a wide range of recent national energy consumption estimates. We present here an analysis based on field-metered usage data, collected as part of a larger field metering study in the USA. This larger study collected data from 880 households in 2012 on a variety of devices, including 113 game consoles (the majority of which are Generation 7 consoles). From our metering, we find that although some consoles are left on nearly 24 h/day, the overall average usage is lower than many other studies have assumed, leading to a US national energy consumption estimate of 7.1 TWh in 2012. Nevertheless, there is an opportunity to reduce energy use with proper game console power management, as a substantial amount of game console usage occurs with the television turned off. The emergence of Generation 8 consoles may increase national energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Proposed Confidence Scale and ID Score in the Identification of Known-Unknown Compounds Using High Resolution MS Data

High-resolution (HR) MS instruments recording HR-full scan allow analysts to go further beyond pre-acquisition choices. Untargeted acquisition can reveal unexpected compounds or concentrations and can be performed for preliminary diagnosis attempt. Then, revealed compounds will have to be identified for interpretations. Whereas the need of reference standards is mandatory to confirm identification, the diverse information collected from HRMS allows identifying unknown compounds with relatively high degree of confidence without reference standards injected in the same analytical sequence. However, there is a necessity to evaluate the degree of confidence in putative identifications, possibly before further targeted analyses. This is why a confidence scale and a score in the identification of (non-peptidic) known-unknown, defined as compounds with entries in database, is proposed for (LC-) HRMS data. The scale is based on two representative documents edited by the European Commission (2007/657/EC) and the Metabolomics Standard Initiative (MSI), in an attempt to build a bridge between the communities of metabolomics and screening labs. With this confidence scale, an identification (ID) score is determined as [a number, a letter, and a number] (e.g., 2D3), from the following three criteria: I, a General Identification Category (1, confirmed, 2, putatively identified, 3, annotated compounds/classes, and 4, unknown); II, a Chromatography Class based on the relative retention time (from the narrowest tolerance, A, to no chromatographic references, D); and III, an Identification Point Level (1, very high, 2, high, and 3, normal level) based on the number of identification points collected. Three putative identification examples of known-unknown will be presented. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Design for Recycling Principles Applicable to Selected Clean Energy Technologies: Crystalline-Silicon Photovoltaic Modules, Electric Vehicle Batteries, and Wind Turbine Blades

Abstract The global growth of clean energy technology deployment will be followed by parallel growth in end-of-life (EOL) products, bringing both challenges and opportunities. Cumulatively, by 2050, estimates project 78 million tonnes of raw materials embodied in the mass of EOL photovoltaic (PV) modules, 12 billion tonnes of wind turbine blades, and by 2030, 11 million tonnes of lithium-ion batteries. Owing partly to concern that the projected growth of these technologies could become constrained by raw material availability, processes for recycling them at EOL continue to be developed. However, none of these technologies are typically designed with recycling in mind, and all of them present challenges to efficient recycling. This article synthesizes and extends design for recycling (DfR) principles based on a review of published industrial and academic best practices as well as consultation with experts in the field. Specific principles developed herein apply to crystalline-silicon PV modules, batteries like those used in electric vehicles, and wind turbine blades, while a set of broader principles applies to all three of these technologies and potentially others. These principles are meant to be useful for stakeholders—such as research and development managers, analysts, and policymakers—in informing and promoting decisions that facilitate DfR and, ultimately, increase recycling rates as a way to enhance the circularity of the clean energy economy. The article also discusses some commercial implications of DfR. Graphical Abstract

36 MATERIALS SCIENCE↗
DOE OSTI2026

Velocity Extraction Using Complete Time-Domain Waveform Data and Audio Machine Learning

We developed a new machine learning-based tool for extracting information from interferometry measurements: MIDWAZE (Modular Interferometry Direct Waveform AnalyZEr). This paper showcases MIDWAZE’s ability to extract an object’s velocity information from Photonic Doppler Velocimetry (PDV) data at near-human accuracy with little to no human intervention. MIDWAZE can extract velocities roughly 350 times as fast as a human analyst "rushing" to complete their extractions, with similar extraction accuracy. MIDWAZE’s most outstanding feature is that it operates directly in waveform/temporal space, freeing analysis from certain limitations imposed by traditional spectrogram-based approaches and opening the way to "phase aware" PDV analysis. MIDWAZE also has limited ability to discriminate between different solid objects, which we develop as a first step towards automated discrimination of different kinds of objects such as ejecta clouds.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Machine Learning

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

Anomaly detection↗
DOE OSTI2019

Wilks’ formula applied to computational tools: A practical discussion and verification

Wilks’ non-parametric approach for setting tolerance limits using order statistics has recently become popular in the nuclear industry. The method allows analysts to predict a desired tolerance limit with some confidence that the estimate is conservative. The style is popular because it is simple and fits well into established regulatory frameworks. An essential analysis of the underlying statistics is presented in this work, including a derivation, analytical and statistical verification, and a broad discussion. Possible impacts of the underlying assumptions for application to computational tools are discussed. An in-depth discussion of the order statistic rank used in Wilks’ formula is provided, including when it might be necessary to use a higher rank estimate.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2019

Using facility-level emissions data to estimate the technical potential of alternative thermal sources to meet industrial heat demand

Industry is frequently highlighted as the world's largest energy-using end-use sector. More specifically, the demand for heat drives much of the demand for energy and fossil fuels in the industrial sector. We conduct a top-down analysis to characterize historical energy and fossil fuel use in 14 top-GHG-emitting industries in the United States, their heat demand requirements, and the potential to substitute heat from geothermal, solar thermal (including concentrating technologies), and small modular nuclear reactors to meet these needs while reducing fossil-fuel use and greenhouse gas (GHG) emissions. We base this analysis on publicly-available facility-level GHG emissions and fuel-combustion data, in addition to assumed requirements for process temperature, to demonstrate the potential value to industry energy analysts of the U.S. Environmental Protection Agency's Greenhouse Gas Reporting Program data. We estimate on a county level that the adoption of these alternative heat sources could have reduced 2015 fossil-fuel use by approximately 31% and combustion GHG emissions by approximately 24%. The most extensive substitution opportunities are in the ethyl alcohol manufacturing and wet corn-milling industries; petroleum refining represents the largest absolute emissions mitigation potential. This initial top-down analysis of substitution potential does not consider more detailed technical factors, including resource availability that will influence the actual deployment of alternative energy technologies. The analysis also does not consider the economic or market factors, including the expected cost to build and operate these generators. We do assume process byproducts that are extensively used for combustion fuels would not be good candidates for substitution for alternative energy generators. Based on these caveats, our analysis could be considered a top-range estimate for this mix of heat generators and industry heat demands. Any subsequent analysis of these alternate energy sources should increase the level of technical, economic, and policy detail.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Modification of conventional peak shapes to accurately represent spectral asymmetry: High-Resolution X-ray photoelectron spectra of [C 4 C 1 Pyrr][NTf 2 ] and [C 8 C 1 Im][NTf 2 ] ionic liquids

X-ray photoelectron spectroscopy (XPS) is one of the most widely used techniques for surface characterization. Analysis of XPS data is challenging and requires the analyst to fit the data with synthetic line shapes to reach physically meaningful interpretations. Experimental spectral envelopes, however, are complex and display asymmetric features that are often ignored or attributed to additional chemical components. The high-resolution XPS spectra of [C 4 C 1 Pyrr][NTf 2 ] and [C 8 C 1 Im][NTf 2 ] all exhibit a degree of asymmetry which is systematically observed at the higher binding energy side of photoemission envelopes. Here, we present the development of a refined fitting procedure for XPS spectra of these ionic liquid-based systems which include (a) Shirley background offset necessary to account for the insulator-like region and (b) spectral asymmetry in C 1s and N 1s regions. Further, Shirley and trapezoid components are applied to compensate for inelastic scattering taking place during electron transitions as high as 7.8 eV above the start of the fitting region in C 1s high-resolution spectrum. To demonstrate the fitness of this model, we present an analysis of a 2:1 mixture of [C 4 C 1 Pyrr][NTf 2 ]: [C 8 C 1 Im][NTf 2 ].

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

Testing SOAR tools in use

Investigations within Security Operation Centers (SOCs) are tedious as they rely on manual efforts to query diverse data sources, overlay related logs, correlate the data into information, and then document results in a ticketing system. Security Orchestration, Automation, and Response (SOAR) tools are a relatively new technology that promise, with appropriate configuration, to collect, filter, and display needed diverse information; automate many of the common tasks that unnecessarily require SOC analysts’ time; facilitate SOC collaboration; and, in doing so, improve both efficiency and consistency of SOCs. There has been no prior research to test SOAR tools in practice; hence, understanding and evaluation of their effect is nascent and needed. Here, in this paper, we design and administer the first hands-on user study of SOAR tools, involving 24 participants and six commercial SOAR tools. Our contributions include the experimental design, itemizing six characteristics of SOAR tools, and a methodology for testing them. We describe configuration of a cyber range test environment, including network, user, and threat emulation; a full SOC tool suite; and creation of artifacts allowing multiple representative investigation scenarios to permit testing. We present the first research results on SOAR tools. Concisely, our findings are that: per-SOC SOAR configuration is extremely important; SOAR tools increase efficiency and reduce context switching, although with potentially decreased ticketing accuracy/completeness; user preference is slightly negatively correlated with their performance with the tool; internet dependence varies widely among SOAR tools; and balance of automation with assisting decision making is preferred by senior participants. We deliver a public user- and tool-anonymized and -obfuscated version of the data.

97 MATHEMATICS AND COMPUTING↗