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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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Cyber-CHAMP Task analysis survey tool is a web-hosted code platform for an individual to select their every day tasking, based on industry documentation and standards, and produce an education and training mapping to provide them the proper associated cyber competency level(s).
The National Renewable Energy Laboratory (NREL) is contracted by the U.S. Department of Energy (DOE) Weatherization Assistance Program (WAP) to develop and maintain the resources under the Guidelines for Home Energy Professionals (GHEP) project. The purpose of the GHEP project is to increase the quality of work conducted for residential energy retrofits in the United States through the WAP network and other residential retrofit programs.
The National Renewable Energy Laboratory (NREL) is contracted by the U.S. Department of Energy (DOE) Weatherization Assistance Program (WAP) to develop and maintain the resources under the Guidelines for Home Energy Professionals (GHEP) project. The purpose of the GHEP project is to increase the quality of work conducted for residential energy retrofits in the United States through the WAP network and other residential retrofit programs. The HEP certifications support WAP and the broader residential home performance industry through the credentialing process and development of defined JTAs for Energy Auditors (EA) and Quality Control Inspectors (QCI). This report outlines the most recent updates (2022) to the EA JTA.
El Laboratorio Nacional de Energía Renovable (NREL), bajo el contrato con el Programa de Asistencia para la Climatización (WAP) del Departamento de Energía de EE.UU. (DOE), desarrolla y mantiene los recursos en el marco del proyecto Directrices para Profesionales de la Energía Doméstica (GHEP). El objetivo del proyecto GHEP es aumentar la calidad del trabajo de remodelación energética residencial realizado por WAP y otros programas de remodelación residencial en Estados Unidos. Para cumplir el objetivo de “Establecer certificaciones nacionales de la mano de obra y normas de formación”, el DOE encargó al NREL el desarrollo de recursos GHEP, incluido un conjunto de certificaciones avanzadas, basadas en aptitud, para el personal de los profesionales de la energía doméstica (HEP). Desde 2010, el NREL ha reclutado voluntarios expertos en la materia de WAP y de la industria del mantenimiento del hogar para que formen parte de comités que desarrollen y actualicen programas de certificación y sus análisis de tareas (JTA) necesarios como base de programas de certificación y formación estandarizados. Las certificaciones HEP apoyan al WAP y al sector del mantenimiento de viviendas residenciales en general mediante el proceso de acreditación y el desarrollo de JTA definidos para auditores energéticos (EA) e inspectores de control de calidad (QCI). Este informe es un resumen de las actualizaciones más recientes (2022) del JTA del EA. [The National Renewable Energy Laboratory (NREL) is contracted by the U.S. Department of Energy (DOE) Weatherization Assistance Program (WAP) to develop and maintain the resources under the Guidelines for Home Energy Professionals (GHEP) project. The purpose of the GHEP project is to increase the quality of work conducted for residential energy retrofits in the United States through the WAP network and other residential retrofit programs. This is the Spanish translation of NREL/TP-7A40-85300.]
El Laboratorio Nacional de Energía Renovable (NREL) ha sido contratado por el Programa de Asistencia para la Climatización (WAP) del Departamento de Energía de EE.UU. (DOE) para desarrollar y mantener los recursos en el marco del proyecto de Directrices para Profesionales de la Energía Doméstica (GHEP). El objetivo del proyecto GHEP es aumentar la calidad del trabajo realizado para la remodelación energética residencial en Estados Unidos a través de WAP y otros programas de remodelación residencial. Para cumplir el objetivo de “Establecer certificaciones nacionales de la mano de obra y normas de formación”, el DOE encargó al NREL el desarrollo de recursos GHEP, incluido un conjunto de certificaciones avanzadas, basadas en aptitud, para el personal de los profesionales de la energía doméstica (HEP). Desde 2010, el NREL ha reclutado voluntarios expertos en la materia de WAP y de la industria del mantenimiento del hogar para que formen parte de comités que desarrollen y actualicen programas de certificación y sus análisis de tareas (JTA) necesarios como base de programas de certificación y formación estandarizados. Las certificaciones HEP apoyan al WAP y al sector del mantenimiento de viviendas residenciales en general mediante el proceso de acreditación y el desarrollo de JTA definidos para auditores energéticos (EA) e inspectores de control de calidad (QCI). Este informe es un resumen de las actualizaciones más recientes (2022) del JTA del QCI. [The National Renewable Energy Laboratory (NREL) is contracted by the U.S. Department of Energy (DOE) Weatherization Assistance Program (WAP) to develop and maintain the resources under the Guidelines for Home Energy Professionals (GHEP) project. The purpose of the GHEP project is to increase the quality of work conducted for residential energy retrofits in the United States through the WAP network and other residential retrofit programs. This is the Spanish translation of NREL/TP-7A40-85789.]
Under the SPP with Korea Institute of Nuclear Safety (KINS) (SPP No. 24SP91), INL research team has a plan to visit KINS on December 3, 2024 and have a project meeting with KINS in-person. INL researchers will give this presentation about what we have done on Task 2 under the contract as below. Task 2: Support KINS in the treatment of human actions. INL efforts consist of: Provide technical expertise on recovery analysis based on INL’s methods or recent research Provide technical expertise on dependency analysis based on INL’s methods or recent research Provide technical expertise on analyzing the effects of HSI degradation on operator actions
This presentation was given at the 2023 U.S. Department of Energy National Energy Technology Laboratory Resource Sustainability Project Review Meeting. The presentation topics cover recent updates and current research directions for the Strategic Systems Analysis and Engineering Directorate in Water Management for Power Systems. The presentation includes preliminary results to meet objectives in reducing freshwater consumption and lowering the cost of treating effluent streams for energy production.
This presentation was given at the 2024 U.S. Department of Energy National Energy Technology Laboratory Resource Sustainability Project Review Meeting. The presentation topics cover recent updates and current research directions for the Strategic Systems Analysis and Engineering Directorate in Critical Minerals.
The purpose of this course is for you to learn the expectations of LANL and the DOE for the analysis phase of the Systematic Approach to Training.
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Computer vision-based analysis of micrographs of nuclear materials is an emerging technique for property prediction, synthetic route identification, and other material analysis tasks. These analysis tasks play a pivotal role in many material characterization applications such as signature development for treaty verification, process optimization, etc. The backbone in many of the recent computer vision-based techniques is a deep learning model, which takes a fixed-size set of pixels and provides a class prediction for that set of pixels. For example, previous work developed a deep convolutional neural network (CNN) to predict the synthetic route from a 256 px x 256 px patch taken from a larger image of uranium ore concentrates. In this work, we present several methods for first calibrating these models in a manner that they can provide accurate probabilities of their predictions’ veracity, and several methods of combining these probabilities. Overall, the combination of these two steps into a pipeline allows for full-image and even full-sample (where a sample has many images) predictions with associated confidence values. Finally, we show that one can also use the patch predictions and confidence to produce a visualization to map predicted constituents through the image. Results and examples for predicting and mapping uranium ore concentrates’ synthetic process from imagery will be presented.
To support data collection for dynamic human reliability analysis (HRA), this study investigates time distributions for task primitives defined in the Goals, Operators, Methods, and Selection rules (GOMS)–Human Reliability Analysis (HRA) method and Human Reliability data EXtraction (HuREX). GOMS-HRA was developed to provide cognition-based time and human error probability (HEP) information for dynamic HRA calculations within the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework, while HuREX is a comprehensive HRA data collection method developed by the Korea Atomic Energy Research Institute (KAERI). In this paper, we examine time distributions by using experimental data collected from the Simplified Human Error Experimental Program (SHEEP) study, which proposes an HRA data collection framework to complement full-scope simulator research and gather input data for dynamic HRA by using simplified simulators such as the Rancor Microworld simulator. This paper investigates whether the time required for GOMS-HRA and HuREX task primitives fits 13 statistical distributions. Additionally, we compare and discuss the time distributions obtained from both student operators and professional operators. The result was that this study identified several time distributions for five GOMS-HRA and four HuREX task primitives. In the future, the results of this study are expected to provide objective reference data on the elapsed time for task primitives and aid in realistically simulating scenarios within dynamic HRA.
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Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.
Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.