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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An Integrated Framework for Risk Assessment of Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology Advancement and Application

This report documents activities performed by Idaho National Laboratory (INL) during fiscal year (FY) 2024 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, Digital Instrumentation and Control (DI&C) Risk Assessment project. The goal of the RISA Pathway is to optimize safety margins and minimize uncertainties to achieve economic efficiencies while maintaining high levels of safety. This is accomplished by providing scientific basis to better represent safety margins and factors that contribute to cost and safety, and by developing new technologies that reduce operating costs. The research efforts for FY 2024 encompass methodology refinement and exploration. The efforts include: (1) The implementation of a natural language processing tool to expedite key aspects of the reliability analysis methods developed by INL; (2) advances to support intersystem CCF analysis by providing guidance for and identification of coupling mechanisms that may contribute to CCF; (3) the investigation of how generative artificial intelligence tools can aid in hazard analysis and diversity and defense in depth (i.e., D3) assessments; (4) Industry collaboration, allowing the demonstration of and INL's risk assessment tools to support risk assessment of DI&C systems at early and late stages of development; (4) a roadmap for the development of a software for each of INL's risk assessment tools; (5) The development of a theory and methodology manual for a risk quantification methodology; (6) the development of a reliability analysis for machine learning (ML)-integrated control systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2011

Interactive image quantification tools in nuclear material forensics

Morphological and microstructural features visible in microscopy images of nuclear materials can give information about the processing history of a nuclear material. Extraction of these attributes currently requires a subject matter expert in both microscopy and nuclear material production processes, and is a time consuming, and at least partially manual task, often involving multiple software applications. One of the primary goals of computer vision is to find ways to extract and encode domain knowledge associated with imagery so that parts of this process can be automated. In this paper we describe a user-in-the-loop approach to the problem which attempts to both improve the efficiency of domain experts during image quantification as well as capture their domain knowledge over time. This is accomplished through a sophisticated user-monitoring system that accumulates user-computer interactions as users exploit their imagery. We provide a detailed discussion of the interactive feature extraction and segmentation tools we have developed and describe our initial results in exploiting the recorded user-computer interactions to improve user productivity over time.

36 MATERIALS SCIENCE↗
DOE OSTI2020

CoSim: A Simulator for Co-Scheduling of Batch and On-Demand Jobs in HPC Datacenters

The increasing scale and complexity of scientific applications are rapidly transforming the ecosystem of tools, methods, and workflows adopted by the high-performance computing (HPC) community. Big data analytics and deep learning are gaining traction as essential components in this ecosystem in a variety of scenarios, such as, steering of experimental instruments , acceleration of high-fidelity simulations through surrogate computations, and guided ensemble searches. In this context, the batch job model traditionally adopted by the supercomputing in-frastructures needs to be complemented with support to schedule opportunistic on-demand analytics jobs, leading to the problem of efficient preemption of batch jobs with minimum loss of progress. In this paper, we design and implement a simulator, CoSim, that enables on-the-fly analysis of the trade-offs arising between delaying the start of opportunistic on-demand jobs, which leads to longer analytics latency, and loss of progress due to preemption of batch jobs, which is necessary to make room for on-demand jobs. To this end, we propose an algorithm based on dynamic programming with predictable performance and scalability that enables supercomputing infrastructure schedulers to analyze the aforementioned trade-off and take decisions in near real-time. Compared with other state-of-art approaches using traces of the Theta pre-Exascale machine, our approach is capable of finding the optimal solution, while achieving high performance and scalability.

batch job preemption↗
DOE OSTI2024

Evaluation of Autonomous Vehicle Sensing and Compute Load on a Chassis Dynamometer

The sensing and compute load auxiliary energy consumption in autonomous vehicles may be significant due to the large number of sensors and the high compute load from sensor processing and route planning. To understand this issue, this study investigates the top-down energy usage of an electric 2015 Kia Soul fully instrumented with state sensors and a state-specific computer for path planning and sensor processing. A chassis dynamometer was then used to evaluate the cases of (1) no sensors or computation, (2) only sensors operating, and (3) sensors plus compute load. The vehicle was operated autonomously on the dynamometer using a PolySync drive-kit with drive-by-wire longitudinal control. The DynoJet model 224xLC was used to adapt the eddy current dynamometer's road load parameters to comply with an Environmental Protection Agency drive schedule and to evaluate performance against the Argonne National Laboratory Digital Dynamometer Dataset. On the UDDS-HWFET combined driving cycle, the stock battery's range was reduced by 5.6% for sensors alone and 12.2% for sensors and compute load. These results show that the added sensing and compute auxiliary load from automated and autonomous systems is significant and that research efforts need to be spent investigating new energy efficient systems.

Brown, Nicholas E.↗
DOE OSTI2024

Extension of SCALE/Sampler’s sensitivity index for the assessment of cross section, fission yield, and decay data uncertainties

Accurate prediction of nuclide compositions and neutron and gamma sources and spectra for fresh and spent nuclear fuel through computational modeling and simulations is the basis for safeguards instruments design, optimization, and calibration, and for validation of the measured responses. The accuracy of these simulations can be significantly impacted by uncertainties in input parameters to the applied computer code. Input parameters are, for example, dimensions, material compositions, and temperatures of the model. Additionally, important but sometimes neglected input parameters for simulations of nuclear systems are the nuclear data that include nuclear reaction cross sections, fission product yields, and decay data. It has been shown that uncertainties of calculated results are dominated by uncertainties of these nuclear data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Corrosion Monitors for Embedded Evaluation

We have developed and characterized novel in-situ corrosion sensors to monitor and quantify the corrosive potential and history of localized environments. Embedded corrosion sensors can provide information to aid health assessments of internal electrical components including connectors, microelectronics, wires, and other susceptible parts. When combined with other data (e.g. temperature and humidity), theory, and computational simulation, the reliability of monitored systems can be predicted with higher fidelity.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A Roadmap for Edge Computing Enabled Automated Multidimensional Transmission Electron Microscopy

The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since analysis, collection, long-term storage, and networking of such datasets remain challenging. Some common issues are their large and unwieldy size that often are several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope. The existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is challenging. One solution is to integrate microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to high-performance computing (HPC) systems. In this work, we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Physics-Guided Machine Learning (PGML) for Improved Aerostructure Manufacturing

The Physics-Guided Machine Learning (PGML) for Improved Aerostructure Manufacturing project objective is to automatically tune machining parameter predictions from physics-based models using process data and Bayesian machine learning. The intent is to enable a step change in aerospace manufacturing by combining machine learning, physics-based process models, and sensors/data in a comprehensive digital environment that simultaneously considers the computer numerically controlled (CNC) machining center capabilities, the workpiece material and geometry, and the workpiece support (fixturing). The project hypothesis is that this combination will enable improved performance in machining operations.

42 ENGINEERING↗
DOE OSTI2016

Hybrid Ultra‐Microporous Materials for Selective Xenon Adsorption and Separation

Abstract The demand for Xe/Kr separation continues to grow due to the industrial significance of high‐purity Xe gas. Current separation processes rely on energy intensive cryogenic distillation. Therefore, less energy intensive alternatives, such as physisorptive separation, using porous materials, are required. Herein we show that an underexplored class of porous materials called hybrid ultra‐microporous materials (HUMs) affords new benchmark selectivity for Xe separation from Xe/Kr mixtures. The isostructural materials, CROFOUR‐1‐Ni and CROFOUR‐2‐Ni, are coordination networks that have coordinatively saturated metal centers and two distinct types of micropores, one of which is lined by CrO 4 2− (CROFOUR) anions and the other is decorated by the functionalized organic linker. These nets offer unprecedented selectivity towards Xe. Modelling indicates that the selectivity of these nets is tailored by synergy between the pore size and the strong electrostatics afforded by the CrO 4 2− anions.

Mohamed, Mona H.↗
DOE OSTI2015

The Pacific Northwest National Laboratory library of bacterial and archaeal proteomic biodiversity

This dataset deposition announces the submission to public repositories of the PNNL Biodiversity Library, a large collection of global proteomics data for 112 bacterial and archaeal organisms. The data comprises 35,162 tandem mass spectrometry (MS/MS) datasets from ~10 years of research. All data has been searched, annotated and organized in a consistent manner to promote reuse by the community. Protein identifications were cross-referenced with KEGG functional annotations which allows for pathway oriented investigation. We present the data as a freely available community resource. A variety of data re-use options are described for computational modeling, proteomics assay design and bioengineering. Instrument data and analysis files are available at ProteomeXchange via the MassIVE partner repository under the identifiers PXD001860 and MSV000079053.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Development of low temperature fuel cell holders for Operando x-ray micro and nano computed tomography to visualize water distribution

Abstract Synchrotron x-ray imaging techniques, like x-ray computed tomography (CT) and radiography have proven instrumental in expanding the communities knowledge of complex transport and reaction kinetics in electrochemical devices such as fuel cells and electrolyzers. This work presents the development of novel x-ray CT imaging techniques for operando visualization of water within low temperature fuel cells at spatial resolutions spanning the micro and nano scales. The design of operando sample holders, for both micro x-ray CT and nano CT experiments is described in depth, and prototypes of these sample holders were evaluated across a set of requirements, the most important of which are x-ray transmissibility, electrical conductivity and mechanical stability. Water segmentation from micro x-ray CT data was enabled by an image subtraction method, where the image without water is subtracted from the one with water. Through iterative experimentations, the operando nano CT cell was developed to optimize mechanical compression, electric conductivity and gas flow. While three-dimensional fuel cell reconstructions were shown possible, there remain challenges to overcome at typical lower energies (8 keV) due to beam damage, whereas it is not as significant for higher energies (>17.5 keV).

Kulkarni, Devashish↗
DOE OSTI2022

Determination of physically based pseudo-Voigt powder diffraction profile terms from the fundamental parameters approach

In this study, a methodology is developed where a fundamental parameters approach (FPA) description of a laboratory powder diffraction instrument (configured in divergent-beam Bragg–Brentano geometry) is used to determine GSAS-II profile parameters for peak asymmetry and instrumental peak widths. This allows the instrumental contribution to peak shapes to be robustly determined directly from a physical description of the instrument, even though GSAS-II does not directly implement FPA for peak shape computation. The FPA-derived parameters can be used as the starting point for instrument characterization, or to characterize sample broadening without the use of a standard to determine the instrument profile function. This new method can facilitate generation of training sets for machine learning. A plot is generated that shows the differences between the two approaches, demonstrating upper bounds for the accuracy of the GSAS-II profile model for a particular instrumental configuration.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Algorithm and program for precise determination of unit-cell parameters of single crystal taking into account the sample eccentricity

A technique has been developed to refine the unit-cell parameters of single crystals with minimization of the influence of instrumental errors on the result. The corresponding computational procedure HuberUB is added to the software package of Huber-5042 diffractometer with a point detector and closedcycle helium cryostat Displex DE-202. The parameters of unit cell, its orientation, the goniometer zero angles, the sample eccentricity, the distances in the goniometer, and the radiation wavelength were refined by the nonlinear least-squares method, which allows imposition of constraints on the unit-cell parameters, depending on the crystal symmetry. The technique is approved on a LuB{sub 12} single crystal. The unit-cell parameters are determined in a temperature range of 20–295 K, with an absolute error not larger than 0.0004 Å (the relative error is of ~5 × 10{sup –5}). The estimates of the unit-cell parameters obtained by the proposed method are evidenced to be unbiased. Some specific features of the behavior of parameters in the ranges of 120–140 and 20–50 K are revealed, which correlate with the anomalies of the physical properties of the crystal.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

SWARM: Reimagining scientific workflow management systems in a distributed world

Modern scientific workflows process massive amounts of data from diverse instruments and sensors, leveraging geographically distributed, heterogeneous compute and storage resources—from leadership-class systems to edge devices—connected by high-performance networks. The diversity of resources introduces challenges in harnessing their full potential, with resilience issues arising across applications, system software, networks, storage, and hardware. Today, workflow management systems (WMS) coordinate the execution of computation and data management tasks across target resources. However, WMS’s centralized nature makes them vulnerable to faults and scalability issues that may result in failures of entire computational campaigns. In conclusion, this paper introduces a novel agentic framework for workflow management, fully distributing and decentralizing the WMS functions and modeling them as swarm intelligence agents infused with advanced artificial intelligence solutions and traditional distributed computing algorithms that can make coordinated decisions in the presence of failures of the underlying cyberinfrastructure.

Swarm intelligence↗
DOE OSTI2025

Automatic Generation of Algorithms for High-Speed Reliable Lossy Data Compression (Final Report)

Fast reliable data compression is urgently needed for many leading-edge scientific instruments and for exascale high-performance computing applications because they produce vast amounts of data at extremely high rates. The goal of this project has been to develop a framework named LC that is able to automatically generate high-speed lossless and reliable lossy compression and decompression algorithms that can be customized for different kinds of data. The resulting LC framework is freely available on GitHub. To achieve high-speed operation, LC outputs optimized and parallelized CPU and GPU implementations of the generated algorithms. To ensure the quality of lossily compressed data, LC guarantees the user-provided error bound. To be able to customize the compression algorithm to various use cases, LC can synthesize millions of different algorithms and automatically search for the one that works best for the given data. We have already employed LC to create state-of-the-art lossless and lossy compressors for scientific data as well as leading lossless compressors for images. We hope that LC and the customized, fast, reliable, and CPU/GPU-compatible compression algorithms that it can generate will greatly benefit the many scientific applications that need not only high trustworthiness but also high performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

OES CO 2 Pipeline FEED Project Design Basis Memorandum

The OES CO₂ Pipeline project will move captured carbon dioxide from two ethanol facilities near Gibson City, Illinois, roughly 7.8 miles southeast to three injection wells outside Anchor, where it will be permanently stored underground. The system is designed to handle up to 4.5 million metric tonnes per year of dense-phase CO₂ at pressures up to 2,500 psig, using 16-inch mainline pipe and 10.750-inch laterals made from API 5L X-60 and X-65 steel. Wall thicknesses vary depending on location, with thinner pipe in open country, heavier wall at road crossings, and the heaviest where the pipe passes under highways or railroads via horizontal directional drill. The pipe gets a fusion-bonded epoxy coating, with an added abrasion-resistant layer wherever it's bored or drilled. Major water crossings will use HDD rather than open trenching. The pipeline will be cathodically protected, equipped with SCADA-compatible pressure and temperature instrumentation, and monitored for leaks using a computational pipeline monitoring system per API RP 1130. Hydrostatic testing will be performed at 1.25 times design pressure, and an ILI caliper run will follow to catch any construction defects. Several items, including fracture toughness requirements, specific NDE methods, and ILI tool selection, are left for the detailed design phase. The whole system falls under 49 CFR Part 195 and ASME B31.4, and Gulf Interstate Engineering prepared this document as the FEED-level design basis under the CarbonSAFE Phase III program.

09 BIOMASS FUELS↗
DOE OSTI2014

User Environment Tracking and Problem Detection with XALT

This work improves our understanding of individual users software needs, then leverages that understanding to help stakeholders conduct business in a more efficient, effective, and systematic way. The product, XALT, builds on work that is already improving the user experience and enhancing support programs for thousands of users on twelve supercomputers across the United States and Europe. XALT will instrument individual jobs on high-end computers to generate a picture of the compilers, libraries, and other software that users need to run their jobs successfully. It will highlight the products our researchers need and do not need, and alert users and support staff to the root causes of software configuration issues as soon as the problems occur. A key objective of this work is generating the information needed to improve efficiency and effectiveness for an extensive community of stakeholders including users, sponsoring institutions, support organizations, and development teams. Efficiency, effectiveness, and responsible stewardship each require a clear picture of users needs. XALT is an important step in the quest to achieve that clarity.

Agrawal, Kapil↗
DOE OSTI2017

Lightweight Provenance Service for High-Performance Computing

Provenance describes detailed information about the history of a piece of data, containing the relationships among elements such as users, processes, jobs, and workflows that contribute to the existence of data. Provenance is key to supporting many data management functionalities that are increasingly important in operations such as identifying data sources, parameters, or assumptions behind a given result; auditing data usage; or understanding details about how inputs are transformed into outputs. Despite its importance, however, provenance support is largely underdeveloped in highly parallel architectures and systems. One major challenge is the demanding requirements of providing provenance service in situ. The need to remain lightweight and to be always on often conflicts with the need to be transparent and offer an accurate catalog of details regarding the applications and systems. To tackle this challenge, we introduce a lightweight provenance service, called LPS, for high-performance computing (HPC) systems. LPS leverages a kernel instrument mechanism to achieve transparency and introduces representative execution and flexible granularity to capture comprehensive provenance with controllable overhead. Extensive evaluations and use cases have confirmed its efficiency and usability. We believe that LPS can be integrated into current and future HPC systems to support a variety of data management needs.

Dai, Dong↗