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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 OSTI2018

Automatic Imagery Data Analysis for Proactive Computer-Based Workflow Management during Nuclear Power Plant Outages

This report is being submitted for the task “Final Report” of DOE NEUP Project 15-8121 “Automatic Imagery Data Analysis for Proactive Computer-Based Workflow Management during Nuclear Power Plant Outages.” Typical nuclear power plant (NPP) outages always involve thousands of maintenance and refueling activities and a large number of workers in limited workspaces, while having tight schedules and zero-tolerance for accidents. During an outage, thousands of workers will be working in various workspaces across the NPP. High outage costs and expensive delays (approximately 1.5 million dollars of loss per day of delay) in NPP maintenance demand tight outage schedules. In packed workspaces, an automatic system that monitors human behaviors in real-time and provides insights about current and pending schedule deviations from the plan is critical for ensuring: 1) effective collaboration among workers and worker teams from different trades; 2) less waste of time and resources due to the lack of situational awareness; and 3) proactive outage project control. The overall goal of this project is to test the hypothesis that real-time imagery-based object tracking and spatial analysis, as well as human behavior modeling of outage participants, will significantly improve the efficiency of outage control while lowering the rates of accidents and incidents. Three objectives of this project are: 1) Establish real-time object tracking and spatiotemporal analysis methods that automatically assess the productivity of field activities and detect anomalous spatiotemporal relationships among activities that cause inefficiencies and risks; 2) Establish realtime human tracking and human factor modeling methods for automatically diagnosing unexpected actions of and interactions between outage participants, those which cause inefficient collaborations between Advanced Outage Control Center (AOCC), satellite outage centers, NPP workers, and maintenance service providers; and 3) Test the proposed automated object tracking, human behavior modeling, and spatiotemporal analysis methods in outage control case studies in order to characterize the effectiveness of automated imagery-data-driven methods in proactively improving the efficiency and safety of workflows in outage coordination and risk management. Recent studies of detailed human behavior monitoring on construction sites have examined the potential of applying advanced computer vision algorithms in detecting and tracking anomalous workers (i.e., workers who do not wear hard hats or safety vests) for ensuring job site safety. Some studies of human factor studies revealed the importance of modeling detailed interactions between individuals within and across teams in better understanding the impact of human in proactive project control. Other studies in the construction domain have developed computational simulation frameworks that formalize detailed spatiotemporal interactions between tasks in order to simulate the impacts of individual tasks on the performance of workflows. While integrated analyses that combine human factor assessment, as well as image processing and simulation, are in demand for effective decision-making, limited studies have examined the potential of such integrated analyses in NPP outage control. This research project has examined an automatic outage monitoring and control system that integrates human factor analysis, computer vision techniques, and simulation methods in order to enable engineers to better understand the interactions between humans, resources, and workflows during outage processes. The project aims at providing NPP maintenance agencies insights into more efficient use of limited resources in extending the life of a nuclear plant, as well as reducing waste while ensuring sufficient generation of electricity. This study is significant for the safety of nuclear plants, sustainable electricity generation for livable communities, and cost savings for maintaining electricity infrastructures in the United States.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1989

Parallel eigenanalysis of finite element models in a completely connected architecture

A parallel algorithm is presented for the solution of the generalized eigenproblem in linear elastic finite element analysis, (K)(phi) = (M)(phi)(omega), where (K) and (M) are of order N, and (omega) is order of q. The concurrent solution of the eigenproblem is based on the multifrontal/modified subspace method and is achieved in a completely connected parallel architecture in which each processor is allowed to communicate with all other processors. The algorithm was successfully implemented on a tightly coupled multiple-instruction multiple-data parallel processing machine, Cray X-MP. A finite element model is divided into m domains each of which is assumed to process n elements. Each domain is then assigned to a processor or to a logical processor (task) if the number of domains exceeds the number of physical processors. The macrotasking library routines are used in mapping each domain to a user task. Computational speed-up and efficiency are used to determine the effectiveness of the algorithm. The effect of the number of domains, the number of degrees-of-freedom located along the global fronts and the dimension of the subspace on the performance of the algorithm are investigated. A parallel finite element dynamic analysis program, p-feda, is documented and the performance of its subroutines in parallel environment is analyzed.

Akl, F. A.↗
NASA NTRS1999

Using Transom Jack in the Human Engineering Analysis of the Materials Science Research Rack-1 and Quench Module Insert

This paper describes the human engineering analysis performed on the Materials Science Research Rack-1 and Quench Module Insert (MSRR-1/QMI) using Transom Jack (Jack) software. The Jack software was used to model a virtual environment consisting of the MSRR-1/QMI hardware configuration and human figures representing the 95th percentile male and 5th percentile female. The purpose of the simulation was to assess the human interfaces in the design for their ability to meet the requirements of the Pressurized Payloads Interface Requirements Document - International Space Program, Revision C (SSP 57000). Jack was used in the evaluation because of its ability to correctly model anthropometric body measurements and the physical behavior of astronauts working in microgravity, which is referred to as the neutral body posture. The Jack model allows evaluation of crewmember interaction with hardware through task simulation including but not limited to collision avoidance behaviors, hand/eye coordination, reach path planning, and automatic grasping to part contours. Specifically, this virtual simulation depicts the human figures performing the QMI installation and check-out, sample cartridge insertion and removal, and gas bottle drawer removal. These tasks were evaluated in terms of adequate clearance in reach envelopes, adequate accessibility in work envelopes, appropriate line of sight in visual envelopes, and accommodation of full size range for male and female stature maneuverability. The results of the human engineering analysis virtual simulation indicate that most of the associated requirements of SSP 57000 were met. However, some hardware design considerations and crew procedures modifications are recommended to improve accessibility, provide an adequate work envelope, reduce awkward body posture, and eliminate permanent protrusions.

Dunn, Mariea C.↗
DOE OSTI2021

Experimental Evaluation of Deformation and Fracture Mechanisms in Highly Irradiated Austenitic Steels

The present report documents recent experimental results of analysis using scanning electron microscopy/electron backscatter diffraction (SEM-EBSD) of plastic deformation mechanisms and strain localization phenomena in austenitic steels irradiated by neutrons. Experiments were performed with specimens irradiated to 125 dpa and, additionally, with specimens that experienced radiation-induced swelling up to 3%. Section 1 briefly analyzes the deformation localization in irradiated steels and its consequences on the material performance. The section describes the advantages and importance of the SEM-EBSD approach combined with in situ mechanical testing capability. Section 2 briefly introduces the experimental tools and methods (i.e., SEM/EBSD in situ tensile frame, electric discharge machine to manufacture irradiated specimens) and describes the investigated materials (i.e., element composition, irradiation conditions, and general microstructure). Section 3 describes the key experimental results and provides a brief analysis and comparison with the datasets obtained earlier within the same task (i.e., low-dose specimens). The discussion focuses on EBSD microstructure maps with strain localization features, misorientation evolution as a function of strain, and observed deformation mechanisms. Section 4 evaluates data collected in recent years on highly irradiated steel and estimates the possible misorientation evolution under irradiation. The section introduces and discusses the concept of in-service-induced damage as an irradiation-assisted stress-corrosion cracking precursor. Section 5 summarizes the work performed. As expected, the present work results are beneficial for exploring and understanding degradation mechanisms in highly irradiated in-core materials found in light water reactors after long-term in-service life.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2014

An Analysis of Earth Science Data Analytics Use Cases

The increase in the number and volume, and sources, of globally available Earth science data measurements and datasets have afforded Earth scientists and applications researchers unprecedented opportunities to study our Earth in ever more sophisticated ways. In fact, the NASA Earth Observing System Data Information System (EOSDIS) archives have doubled from 2007 to 2014, to 9.1 PB (Ramapriyan, 2009; and https:earthdata.nasa.govaboutsystem-- performance). In addition, other US agency, international programs, field experiments, ground stations, and citizen scientists provide a plethora of additional sources for studying Earth. Co--analyzing huge amounts of heterogeneous data to glean out unobvious information is a daunting task. Earth science data analytics (ESDA) is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. It can include Data Preparation, Data Reduction, and Data Analysis. Through work associated with the Earth Science Information Partners (ESIP) Federation, a collection of Earth science data analytics use cases have been collected and analyzed for the purpose of extracting the types of Earth science data analytics employed, and requirements for data analytics tools and techniques yet to be implemented, based on use case needs. ESIP generated use case template, ESDA use cases, use case types, and preliminary use case analysis (this is a work in progress) will be presented.

data analytics↗
DOE OSTI2020

ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data

ChemML is an open machine learning (ML) and informatics program suite that is designed to support and advance the data-driven research paradigm that is currently emerging in the chemical and materials domain. ChemML allows its users to perform various data science tasks and execute ML workflows that are adapted specifically for the chemical and materials context. Key features are automation, general-purpose utility, versatility, and user-friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community. Finally, ChemML is also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data-driven in silico research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Statistical analysis on random quantum circuit sampling by Sycamore and Zuchongzhi quantum processors

Random quantum circuit sampling, a task to sample bit strings from a random quantum circuit, is considered a suitable benchmark task to demonstrate the outperformance of quantum computers even with noisy qubits. Recently, random quantum circuit sampling was performed on the Sycamore quantum processor with 53 qubits [Nature (London) 574, 505 (2019)] and on the Zuchongzhi quantum processor with 56 qubits [Phys. Rev. Lett. 127, 180501 (2021)]. Here, we analyze and compare the statistical properties of the outputs of the random quantum circuit sampling by the Sycamore and Zuchongzhi processors. Using the Marchenko-Pastur law of random matrices of bit strings and the Wasssertein distances between bit strings, we find that the statistical properties of Sycamore bit strings are quite different from those of Zuchongzhi bit strings, while both processors score similar values of linear cross-entropy fidelity for random circuit sampling. Some bit strings sampled by the Zuchongzhi processor pass the NIST random number tests while both Sycamore and Zuchongzhi processors show similar patterns in the heat maps of bit strings. Zuchongzhi bit strings are much closer to classical uniform random bits than those of Sycamore. It is shown that the statistical properties of bit strings of both random quantum circuits change little as the depth of the random quantum circuits increases. Our findings raise a question about the computational reliability of noisy quantum processors because two quantum processors with similar noise levels and similar qubit structures produced statistically different outputs for the same random quantum circuit sampling.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

AmpSuite

Seismic amplitudes offer vital information about explosion source characteristics, including discrimination and yield estimation. To take advantage of this, we developed an interactive Python package to measure, control data quality, generate broad area propagation models and perform discrimination and estimate yield. Propagation models are essential in support of transportable yield and broad area discrimination. The key benefit of this package will be its ability to continuously integrate data and new techniques. The AmpSuite framework will provide standardized, repeatable, and accurate model generation and characterization routines. The capability is crucial for monitoring agencies tasked with rapid and high-quality seismic event characterization. The AmpSuite software includes a series of independent modules to perform: • Direct Phase Amplitude Measurement and Storage • Coda Envelope Measurement and Storage • Data Quality Control • New Propagation Model Developments • Seismic Discrimination and Analysis • Yield Estimation and supporting utility software. The AmpSuite software provides comprehensive solutions for monitoring agencies seeking to optimize model generation and event analysis within a contemporary Python framework. Stakeholders (AFTAC) have begun to move towards the Python language for scientific analysis as a new workforce emerges.

Alfaro, Richard↗
DOE OSTI2021

Extracting Material Property Measurement Data from Scientific Articles

Machine learning-based prediction of material properties is often hampered by the lack of sufficiently large training datasets. The majority of such measurement data is embedded in scientific literature and the ability to automatically extract these data is essential to support the development of reliable property prediction methods. In this work, we describe a methodology for an automatic property extraction framework using material solubility as the target property. We create an annotated dataset containing tags for solubility-related entities using a combination of regular expressions and manual tagging. We then compare five entity recognition models leveraging both token-level and span-level architectures on the task of classifying solute names, solubility values, and solubility units. Additionally, we explore a novel pretraining approach that leverages automated chemical name and quantity extraction tools to generate large datasets that do not rely on intensive manual effort. Finally, we perform an analysis to identify the causes of classification errors.

Panapitiya, Gihan U.↗
DOE OSTI2014

Evaluation of the Computing Systems for Yucca Mountain Repository TSPA-LA Model Operational Readiness

Sandia National Laboratories (SNL) was tasked to conduct an evaluation of the legacy computing systems of the now-closed Yucca Mountain Project (YMP) to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment (TSPA) type analysis, in the event that the License Application (LA) review by the U.S. Nuclear Regulatory Commission (NRC) is re-started and involves additional requests for information (RAIs). Six problem areas or components of the computing system were identified and subsequently resolved or improved to ensure the operational readiness of the TSPA-LA model capability on the server cluster. As part of this readiness review, the legacy TSPA computational cluster that was relocated from the SNL YMP Lead Lab Project Office in Las Vegas, Nevada to the SNL offices in Albuquerque, New Mexico was replaced with new hardware. Three floating licenses of Goldsim Version 9.60.300 were installed on the new cluster head node, and its distributed processing capability was mapped on the cluster processors. Other supporting software was tested and installed to support the TSPA- type analysis on the server cluster. TSPA-LA modeling cases were tested and verified for the model reproducibility on the current server cluster. All test runs were executed on multiple processors on the server cluster utilizing the Goldsim distributed processing capability, and all runs were completed successfully. The model reproducibility verification was evaluated by two approaches: numerical value comparison and graphical comparison, and the analysis demonstrated an excellent reproducibility of the TSPA-LA model runs on the server cluster. The current server cluster and supporting software systems are fully operational to support TSPA-LA type analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Physics-Based Optical Neuromorphic Classification

Typical approaches to classify scenes from light convert the light field to electrons to perform the computation in the digital electronic domain. This conversion and downstream computational analysis require significant power and time. Diffractive neural networks have recently emerged as unique systems to classify optical fields at lower energy and high speeds. Previous work has shown that a single layer of diffractive metamaterial can achieve high performance on classification tasks. In analogy with electronic neural networks, it is anticipated that multilayer diffractive systems would provide better performance, but the fundamental reasons for the potential improvement have not been established. In this work, we present extensive computational simulations of two - layer diffractive neural networks and show that they can achieve high performance with fewer diffractive features than single layer systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1978

A model of human event detection in multiple process monitoring situations

It is proposed that human decision making in many multi-task situations might be modeled in terms of the manner in which the human detects events related to his tasks and the manner in which he allocates his attention among his tasks once he feels events have occurred. A model of human event detection performance in such a situation is presented. An assumption of the model is that, in attempting to detect events, the human generates the probability that events have occurred. Discriminant analysis is used to model the human's generation of these probabilities. An experimental study of human event detection performance in a multiple process monitoring situation is described and the application of the event detection model to this situation is addressed. The experimental study employed a situation in which subjects simulataneously monitored several dynamic processes for the occurrence of events and made yes/no decisions on the presence of events in each process. Input to the event detection model of the information displayed to the experimental subjects allows comparison of the model's performance with the performance of the subjects.

Greenstein, J. S.↗
NASA NTRS1991

Compiling global name-space parallel loops for distributed execution

Distributed memory machines do not provide hardware support for a global address space. Thus programmers are forced to partition the data across the memories of the architecture and use explicit message passing to communicate data between processors. The compiler support required to allow programmers to express their algorithms using a global name-space is examined. A general method is presented for analysis of a high level source program and its translation into a set of independently executing tasks communicating via messages. If the compiler has enough information, this translation can be carried out at compile time. Otherwise, run-time code is generated to implement the required data movement. The analysis required in both situations is described and the performance of the generated code on the Intel iPSC/2 is presented.

Koelbel, Charles↗
NASA NTRS1997

CARES/Life Software for Designing More Reliable Ceramic Parts

Products made from advanced ceramics show great promise for revolutionizing aerospace and terrestrial propulsion, and power generation. However, ceramic components are difficult to design because brittle materials in general have widely varying strength values. The CAPES/Life software eases this task by providing a tool to optimize the design and manufacture of brittle material components using probabilistic reliability analysis techniques. Probabilistic component design involves predicting the probability of failure for a thermomechanically loaded component from specimen rupture data. Typically, these experiments are performed using many simple geometry flexural or tensile test specimens. A static, dynamic, or cyclic load is applied to each specimen until fracture. Statistical strength and SCG (fatigue) parameters are then determined from these data. Using these parameters and the results obtained from a finite element analysis, the time-dependent reliability for a complex component geometry and loading is then predicted. Appropriate design changes are made until an acceptable probability of failure has been reached.

Nemeth, Noel N.↗
NASA NTRS1998

High-Alpha Research Vehicle Lateral-Directional Control Law Description, Analyses, and Simulation Results

This report contains a description of a lateral-directional control law designed for the NASA High-Alpha Research Vehicle (HARV). The HARV is a F/A-18 aircraft modified to include a research flight computer, spin chute, and thrust-vectoring in the pitch and yaw axes. Two separate design tools, CRAFT and Pseudo Controls, were integrated to synthesize the lateral-directional control law. This report contains a description of the lateral-directional control law, analyses, and nonlinear simulation (batch and piloted) results. Linear analysis results include closed-loop eigenvalues, stability margins, robustness to changes in various plant parameters, and servo-elastic frequency responses. Step time responses from nonlinear batch simulation are presented and compared to design guidelines. Piloted simulation task scenarios, task guidelines, and pilot subjective ratings for the various maneuvers are discussed. Linear analysis shows that the control law meets the stability margin guidelines and is robust to stability and control parameter changes. Nonlinear batch simulation analysis shows the control law exhibits good performance and meets most of the design guidelines over the entire range of angle-of-attack. This control law (designated NASA-1A) was flight tested during the Summer of 1994 at NASA Dryden Flight Research Center.

Davidson, John B.↗
NASA NTRS2002

Research on Intelligent Synthesis Environment

The ultimate goal of this research project is to develop a methodology for the assessment and continuous improvement of engineering team effectiveness in distributed collaborative environments. This review provides the theoretical foundation upon which subsequent empirical work will be based. Our review of the team performance literature has identified the following 12 conceptually distinct team interaction processes as characteristic of effective teams. 1) Mission Analysis; 2) Resource Distribution; 3) Leadership; 4) Timing; 5) Intra-team Feedback; 6) Motivational Functions; 7) Team Orientation; 8) Communication; 9) Coordination; 10) Mutual Performance Monitoring; 11) Back-up Behaviors; and 12) Cooperation. In addition, this review summarizes how team task characteristics (i.e., task type, task complexity, motivation, and temporal changes), team characteristics (i.e., team structure and team knowledge), and individual team member characteristics (i.e., dispositions and teamwork knowledge, skills, and abilities) affect team interaction processes, determine the relevance of these processes, and influence team performance. The costs and benefits of distributed team collaboration are also considered. The review concludes with a brief discussion of the nature of collaborative team engineering tasks.

Loftin, R. Bowen↗
DOE OSTI2021

Risk Informed Timelines (RIT) v.1.0

Risk Informed Timelines (RIT) is a more modern and holistic method for understanding how access delay timelines are developed and utilized in physical security system evaluations. The software utilizes Bayesian methods to combine subject matter expert judgement and small performance test datasets in a consistent and defensible way. It allows a more holistic view of delay performance that provides distributions of task times and task success probabilities to account for tasks that, if failed, would result in failure of the attack. The RIT methodology is meant to enable security risk managers to begin the transition into more comprehensive delay timeline analysis. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-8294 O

Noel, ToddG.↗
DOE OSTI2024

Nanoscopic Imaging of Self-Propelled Ultrasmall Catalytic Nanomotors

Ultrasmall nanomotors (<100 nm) are highly desirable nanomachines for their size-specific advantages over their larger counterparts in applications spanning nanomedicine, directed assembly, active sensing, and environmental remediation. While there are extensive studies on motors larger than 100 nm, the design and understanding of ultrasmall nanomotors have been scant due to the lack of high-resolution imaging of their propelled motions with orientation and shape details resolved. Here, we report the imaging of the propelled motions of catalytically powered ultrasmall nanomotors─hundreds of them─at the nanometer resolution using liquid-phase transmission electron microscopy. These nanomotors are Pt nanoparticles of asymmetric shapes (“tadpoles” and “boomerangs”), which are colloidally synthesized and observed to be fueled by the catalyzed decomposition of NaBH4 in solution. Statistical analysis of the orientation and position trajectories of fueled and unfueled motors, coupled with finite element simulation, reveals that the shape asymmetry alone is sufficient to induce local chemical concentration gradient and self-diffusiophoresis to act against random Brownian motion. Our work elucidates the colloidal design and fundamental forces involved in the motions of ultrasmall nanomotors, which hold promise as active nanomachines to perform tasks in confined environments such as drug delivery and chemical sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗