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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Bifacial Photovoltaic Module Degradation Dynamics

In a comprehensive study conducted at NREL's 75 kW bifacial single-axis-tracked field, accelerated degradation was observed in four out of five bifacial silicon photovoltaic (PV) module technologies when compared to their monofacial counterparts. Root cause analysis of accelerated bifacial degradation involved various analytical tools and techniques. This included employing RdTools to identify rates of power loss, conducting measurements on fielded and control modules using infrared imaging, electroluminescence (EL) and photoluminescence (PL), quantum efficiency (QE) analysis, IV-curves assessment, as well as utilizing handheld Raman and reflectance measurement targeted at anti-reflective coating. Most cases pointed to carrier lifetime degradation causing Voc loss and simultaneous Isc loss. In some cases, Isc further decreases likely due to optical effects from encapsulant degradation. The outcomes and methodologies employed in this investigation are documented in this publication. This study's significance is further emphasized by placing the findings within the broader context of the performance and degradation of various bifacial systems, as identified in the PV Fleets data.

bifacial↗
DOE OSTI2021

Usability Evaluation of the Innovation Portal and Integrated Capability Analysis Platform

The usability evaluation of the ICAP and IP tools was conducted as part of the United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) ION effort with two evaluators for the Idaho National Laboratory. Many nuclear operations in the United States are facing challenges of increased business, operations, and maintenance costs due to subsidized renewables and natural gas. The commercial NPP industry has looked to other industries to develop methods such as Integrated Operations (IO) to use available technologies to reduce operating costs. One concept for reducing costs that has been developed is the Integrated Operations for Nuclear (ION). A capability Stack model was developed as part of the ION effort to break down information related to People, Technology, Processes, and Governance (PTPG). The Innovation Portal (IP) and Integrated Capability Analysis Platform (ICAP) were created from the Capability Stack model as part of the ION effort to help utilities reduce O&M costs by identifying work reduction opportunities. Two heuristic evaluations and a cognitive walkthrough of the ICAP and IP tools were completed to identify usability issues to further develop the tools for use in the ION effort. Usability issues identified were rated by priority levels and across both tools. The priority levels identified were high (5), medium (11), and low (10). Recommendation were made to help reduce usability issues of the systems and help increase the user response rate of using the systems once they are implemented for utilities. Future work on designs for the IP tool and additional heuristic evaluations and external testing are being developed to continue refinement of the ICAP and IP tools for the ION effort.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Solute clustering in polycrystals: Unveiling the interplay of grain boundary junction and long-range solute attraction effects

Spectral analysis of local atomic environments has become a powerful tool for studying solute atom segregation and interactions at grain boundaries in nanocrystalline alloys. When applied to individual grain boundaries, the spectral analysis has shown that solute-solute interaction can be either attractive or repulsive, with long-range relative attraction enhancing the likelihood for solute atoms to begin clustering. In this article, we combine this analysis with a new grain-boundary structure descriptor based on grain-boundary atom coordination, to investigate the impact of grain-boundary junctions on solute atom segregation in polycrystals. Specifically, we systematically characterize the tendency of solute clusters to begin forming at various types of ordinary grain boundaries, triple junctions, and high-order junctions in Ag polycrystals containing either Ni or Cu solute atoms. Our findings demonstrate that the formation of solute clusters at grain boundaries is primarily driven by long-range relative solute attraction, rather than short-range solute-solute interactions. Furthermore, this effect is most pronounced near grain-boundary junctions. Our study highlights the multiscale nature of solute segregation at crystalline interfaces and provides new insights into the complex phenomena governing heterogeneous solute segregation in grain-boundary networks.

Grain-boundary junction↗
DOE OSTI2021

NEAMS Workbench MOOSE Integration Update

The Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench is a graphical user interface (GUI) that provides a common analysis environment for the accelerated use of the NEAMS toolkit. To improve design and analysis of current and future nuclear energy systems, the NEAMS Workbench provides an integrated development environment for model creation, review, execution, output review, and visualization for integrated tools. In addition to a GUI, the NEAMS Workbench provides the Workbench Analysis Sequence Processor, an open-source tool set that facilitates input-content assistance for supported domain-specific input languages and user-friendly syntaxes. These supported syntaxes enable accelerated development and deployment of enhanced user inputs and workflows. The Multiphysics Object-Oriented Simulation Environment (MOOSE) has been supported in the NEAMS Workbench since 2016 and receives continuous updates. Efforts to improve MOOSE-based tool integration in the NEAMS Workbench are ongoing. This document details the current and planned enhancements of the NEAMS Workbench and the MOOSE framework to improve integration and usability of MOOSE-based applications within the NEAMS Workbench.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Improving radiograph analysis throughput through transfer learning and object detection

SIGN Fracture Care International partners with surgeons in low-resource hospitals worldwide to provide access to effective orthopedic care by donating educational materials and innovatively designed surgical implants. Over two decades, SIGN’s Online Surgical Database (SOSD) has grown to contain over 500,000 medical images, with radiographs holding the majority share. One challenge in working with hospitals worldwide is that both the radiographs uploaded to the SOSD and the data entry accompanying the uploads vary in quality. To improve the accuracy of data in the SOSD, we trained a model to detect surgical implants in radiographs. We first developed a tool to automatically detect radiographs, then trained an object detection model to determine the number and placement of surgical implants visible in the radiograph. Active learning was used to generate a training set containing 2,510 radiographs with screws, nails, and plates labeled by bounding boxes. Training a model to simultaneously recognize all three classes of implants gave a low average precision (AP) for the plate class, likely due to the low number of plate instances in our training set and the large variety of surgical plates used by SIGN-partnered surgeons. Applying standard image augmentation techniques to increase the plate count in our training set did not appreciably increase the AP of plate detection. To improve plate detection, we redrew the bounding boxes to account for correlations between the screw and plate classes. Training one model to detect nails and screws and a separate model to detect plates increased the AP of plate detection by 78.8 percentage points. The AP of each class was 80.7% for screws, 93.6% for nails, and 92.6% for plates; meanwhile, the sensitivity was 92% for screws, 86% for nails, and 81% for plates. We show that object detection methods can be used to detect surgical implants in radiographs of varying quality; however, the detection ability is dependent on the type of implant, and some implants, in our case plates, must be treated differently than others. Such tools can improve the throughput of radiograph analysis, assisting physicians and surgeons with the treatment of bone fractures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Co-Active Subspace Methods for the Joint Analysis of Adjacent Computer Models

Active subspace (AS) methods are a valuable tool for understanding the relationship between the inputs and outputs of a Physics simulation. In this article, an elegant generalization of the traditional ASM is developed to assess the co-activity of two computer models. This generalization, which we refer to as a Co-Active Subspace (Co-AS) Method, allows for the joint analysis of two or more computer models allowing for thorough exploration of the alignment (or non-alignment) of the respective gradient spaces. We define co-active directions, co-sensitivity indices, and a scalar “concordance” metric (and complementary “discordance” pseudo-metric) and we demonstrate that these are powerful tools for understanding the behavior of a class of computer models, especially when used to supplement traditional AS analysis. Details for efficient estimation of the Co-AS and an accompanying R package (concordance) are provided. Practical application is demonstrated through analyzing a set of simulated rate stick experiments for PBX 9501, a high explosive, offering insights into complex model dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Assessment of Local Observation of Atomic Ordering in Alloys via the Radial Distribution Function: A Computational and Experimental Approach

As a powerful analytical technique, atom probe tomography (APT) has the capacity to acquire the spatial distribution of millions of atoms from a complex sample. However, extracting information at the Ångstrom-scale on atomic ordering remains a challenge due to the limits of the APT experiment and data analysis algorithms. The development of new computational tools enable visualization of the data and aid understanding of the physical phenomena such as disorder of complex crystalline structures. Here, we report progress towards this goal using two steps. We describe a computational approach to evaluate atomic ordering in the crystal structure by generating radial distribution functions (RDF). Atomic ordering is rendered as the Fractional Cumulative Radial Distribution Function (FCRDF) which allows for greater visibility of local compositions at short range in the structure. Further, we accommodate in the analysis additional parameters such as uncertainty in the atomic coordinates and the atomic abundance to ascertain short-range ordering in APT data sets. We applied the FCRDF analysis to synthetic and experimental APT data sets for Ni 3 Al. The ability to observe a signal of atomic ordering consistent with the known L1 2 crystal structure is heavily dependent on spatial uncertainty, irrespective of abundance. Detection of atomic ordering is subject to an upper limit of spatial uncertainty of atoms described with Gaussian distributions with a standard deviation of 1.3 Å. The FCRDF analysis was also applied to the APT data set for a six-component alloy, Al 1.3 CoCrCuFeNi. In this case, we are currently able to visualize elemental segregation at the nanoscale, though unambiguous identification of atomic ordering at the Ångstrom (nearest-neighbor) scale remains a goal.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High-Temperature Gas-Cooled Pebble-Bed Reactors Running In And Transient Modeling Capabilities Demonstration

This study presents a comprehensive benchmarking and verification effort of several thermal-hydraulic and multiphysics capabilities for high-temperature gas-cooled reactor (HTGR) applications. The first part of this effort focuses on the running-in verification of Griffin's multiphysics capabilities, specifically for simulating the evolution of Pebble Bed reactor cores from startup to equilibrium. In the absence of validation data, code-to-code comparisons are conducted with Kugelpy, showing good agreement for key quantities like maximum power density and fresh core k-eigenvalue predictions. However, discrepancies in equilibrium core predictions suggest potential issues with cross sections, underscoring the need for further refinement and evaluation. The HTTF system analysis code benchmark involves RELAP5-3D, SAM, and GAMMA+ to assess their predictive capabilities for HTTF behavior under both normal operation and pressurized conduction cooldown (PCC) transient conditions. While there is good agreement in predicting major parameters such as coolant temperature, solid temperature, and flow distribution, discrepancies in transient behavior highlight differences in modeling approaches, nodalizations, and heat transfer models. The HTTF lower plenum CFD benchmark employs nekRS to simulate flow mixing phenomena, successfully capturing relevant flow physics and demonstrating mesh independence in complex geometries. Preliminary results suggest a relatively uniform temperature field but significant unsteadiness in the flow, requiring time-averaging analyses. The GPBR200 system analysis code benchmark uses SAM's core channel and porous media models, incorporating an RCCS loop for decay heat removal. During steady-state and transient conditions, including protected de-pressurized and pressurized loss of forced cooling (DLOFC and PLOFC), both models show good agreement in predicting temperature profiles and key parameters. Notably, while the core channel model underpredicts convective heat transfer effects, both models maintain temperatures well below the TRISO fuel safety limit. These benchmarking efforts collectively enhance the predictive capabilities of the tools used in HTGR design and safety analysis, guiding developments to improve their accuracy and applicability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra

Abstract Transit spectroscopy is a powerful tool for decoding the chemical compositions of the atmospheres of extrasolar planets. In this paper, we focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. After cleaning and validating the data, we demonstrate methods for: (i) initial exploratory data analysis, based on summary statistics (estimates of location and variability); (ii) exploring and quantifying the existing correlations in the data; (iii) preprocessing and linearly transforming the data to its principal components; (iv) dimensionality reduction and manifold learning; (v) clustering and anomaly detection; and (vi) visualization and interpretation of the data. To illustrate the proposed unsupervised methodology, we use a well-known public benchmark data set of synthetic transit spectra. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We explore a number of different techniques for such dimensionality reduction and identify several suitable options in terms of summary statistics, principal components, etc. We uncover interesting structures in the principal component basis, namely well-defined branches corresponding to different chemical regimes of the underlying atmospheres. We demonstrate that those branches can be successfully recovered with a K-means clustering algorithm in a fully unsupervised fashion. We advocate for lower-dimensional representations of the spectroscopic data in terms of the main principal components, in order to reveal the existing structure in the data and quickly characterize the chemical class of a planet.

Matchev, Konstantin T. (ORCID:0000000341829096)↗
DOE OSTI2025

Covariance-Free Bifidelity Control Variates Importance Sampling for Rare Event Reliability Analysis

Multifidelity modeling has been steadily gaining attention as a tool to address the problem of exorbitant model evaluation costs that makes the estimation of failure probabilities a significant computational challenge for complex real-world problems, particularly when failure is a rare event. To implement multifidelity modeling, estimators that efficiently combine information from multiple models/sources are necessary. In past works, the variance reduction techniques of control variates (CV) and importance sampling (IS) have been leveraged for this task. In this paper, we present the CVIS framework—a creative take on a coupled CV and IS estimator for bifidelity reliability analysis. The framework addresses some of the practical challenges of the CV method by using an estimator for the control variate mean and sidestepping the need to estimate the covariance between the original estimator and the control variate through a clever choice for the tuning constant. Furthermore, the task of selecting an efficient IS distribution is also considered, with a view towards maximally leveraging the bifidelity structure and maintaining expressivity. Additionally, a diagnostic is provided that indicates both the efficiency of the algorithm as well as the relative predictive quality of the models utilized. Finally, the behavior and performance of the framework is explored through analytical and numerical examples.

Markov chain Monte Carlo↗
DOE OSTI2025

JUSTIFI: Software for Improving Performance Objectives via Energy Efficiency

With growing energy supply concerns and rising costs, energy efficiency is a critical component of industrial energy resilience and competitiveness by directly reducing energy operating costs. Energy efficiency projects in manufacturing also yield valuable benefits to other key metrics, such as improved quality, reduced maintenance costs, improved safety, decreased pollution, and enhanced productivity. However, it is difficult to receive approval for energy efficiency projects, so implementation rates are low, even when meeting capital project payback period criteria. The inclusion and quantification of non-energy benefits (NEBs) in the decision-making process for energy efficiency projects can improve the overall financial payback period while demonstrating a positive impact on the firm's key performance metrics and business strategy. Despite their significant financial and strategic value, NEBs are rarely factored into decision-making due to lack of tools to effectively identify and quantify them. Therefore, a comprehensive and integrative approach is needed for the rapidly evolving energy landscape. To address these challenges, through funding from U.S. Department of Energy, our new assessment methodology integrates common continuous improvement six sigma concepts, such as the DMAIC process, and a protocol of guiding questions, into energy efficiency assessments to identify NEBs. We have also developed open-source software, JUSTIFI, to guide users through this process, data collection, and quantification. It is designed to be used concurrently with DOE energy system analysis software suite, MEASUR. Our methodology and tools inform energy assessors, firm engineering, decision makers, and workforce seeking to increase energy resilience and to maximize benefits aligned with performance metrics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

JUSTIFI: Software for Improving Performance Objectives via Energy Efficiency

With growing energy supply concerns and rising costs, energy efficiency is a critical component of industrial energy resilience and competitiveness by directly reducing energy operating costs. Energy efficiency projects in manufacturing also yield valuable benefits to other key metrics, such as improved quality, reduced maintenance costs, improved safety, decreased pollution, and enhanced productivity. However, it is difficult to receive approval for energy efficiency projects, so implementation rates are low, even when meeting capital project payback period criteria. The inclusion and quantification of non-energy benefits (NEBs) in the decision-making process for energy efficiency projects can improve the overall financial payback period while demonstrating a positive impact on the firm's key performance metrics and business strategy. Despite their significant financial and strategic value, NEBs are rarely factored into decision-making due to lack of tools to effectively identify and quantify them. Therefore, a comprehensive and integrative approach is needed for the rapidly evolving energy landscape. To address these challenges, through funding from U.S. Department of Energy, our new assessment methodology integrates common continuous improvement six sigma concepts, such as the DMAIC process, and a protocol of guiding questions, into energy efficiency assessments to identify NEBs. We have also developed open-source software, JUSTIFI, to guide users through this process, data collection, and quantification. It is designed to be used concurrently with DOE energy system analysis software suite, MEASUR. Our methodology and tools inform energy assessors, firm engineering, decision makers, and workforce seeking to increase energy resilience and to maximize benefits aligned with performance metrics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Multi-Physics Coupled Seismic Safety Analysis of Molten-Salt Reactors

Pool-type Molten Salt Reactors (MSRs) are a promising advanced nuclear reactor concept relying on passive physical behavior to provide increased safety characteristics. The high heat capacity, high boiling point, unpressurized liquid salt contains the nuclear fuel and as it passes through the vessel achieves criticality and produces nuclear power. The salt then carries away the heat produced along with the delayed neutron precursors towards heat exchangers and primary pumps before entering the core. This forms a highly coupled multiphysics problem between neutronics and fluid flow, which makes modeling these reactors challenging. The safety/licensing case heavily relies on modeling and simulation to prove their safety, and significant effort has been expanded by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to develop the appropriate simulation tools. Their safety/licensing will require a thorough analysis of their behavior during earthquake-based transients. Earthquakes are a transient condition commonly experienced in many regions, and are safely survived by dozens of reactors every year. Nonetheless, significant conservatisms were introduced with limited predictive modeling and simulation available. The first-of-a-kind capability developed by this research will allow for engineers to analyze ranges of design-basis and beyond-design basis accident scenarios to assess the integrity of the reactor vessel and surrounding system, possibly allowing for limiting the costly conservatisms in the design. This projects uses MASTODON, Griffin, and Pronghorn in the NEAMS ecosystem to model the coupled multiphysics problem posed by an earthquake in a molten salt reactor. It marches through a number of number of coupling schemes, from uncoupled simulations to tightly two-way coupled simulations. The appropriate level of coupling for these simulations will then be determined based on the accuracy in the quantities of interest (QoI) and computational effort involved in each scheme. The QoIs chosen for this paper include the mass flow rate across the heat exchanger (for Pronghorn simulations), power output of the reactor (for Griffin simulations), and the maximum Von Mises stress (for MASTODON simulations). This summary paper reports on the current progress of this project, with 2D tightly coupled multiphysics results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Thermal Hydraulics Analysis of a Divertor Monoblock Using SALAMANDER

Divertors are critical components in magnetic confinement fusion devices. One of the Divertor's crictial roles is absorbing the highest heat flux from the plasma. The Divertor accomplishes this through the use of divertor monoblocks and cooling channels. This work presents a thermal hydraulic analysis of a divertor monoblock using the Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering and Research (SALAMANDER). SALAMANDER is an open source tool developed at Idaho National Laboratory for conducting multiphysics and multscale analysis of Plasma Facing Components (PFCs). For the monoblock, solid heat conduction is modeled in the block with a constant heat flux of 1E7 W/m^2 applied to the top of the block to represent peak heat flux from the plasma. The cooling channel (which is pressurized water at a Reynolds number of ~1,000,000) is modeled using a k-epsilon turbulence model with standard wall functions. The heat transfer between the solid and fluid domain is modeled using the Dittus-Boelter correlation for the convective heat transfer coefficient. This work aims to show the importance of a multiphysics modeling approach when performing simulations on PFCs.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Modeling Electric Vehicle Charging Station Siting Suitability with a Focus on Equity

As adoption of electric vehicles increases, the infrastructure to charge them must keep pace. Determining where to add new charging infrastructure is a complex process subject to many factors, including electrical service availability, vehicle dwell time, the type(s) of drivers and vehicles the stations will serve, traffic levels and timing, and land ownership. In addition, advancing social equity is a current priority of federal efforts to invest in electric vehicle charging infrastructure. Conducting Multi-criteria Decision Analysis (MCDA) within Argonne’s Energy Zones Mapping Tool (EZMT) is a useful method for analyzing many of the factors that influence how suitable a location is for potentially adding new charging infrastructure, and we show how equity metrics can be included in the analysis. However, data limitations impose challenges to using MCDA to evaluate and prioritize locations. We use three examples to demonstrate how to use publicly available data and MDCA to analyze different siting objectives. Each example starts with defining a specific objective and ends with how to use the results to identify specific potential locations that could be investigated further. This analysis demonstrates how interested stakeholders can use the EZMT to run the example MCDA models defined in this study, modify them to suit their needs, or create new MCDA models.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Asc-Seurat: analytical single-cell Seurat-based web application

Abstract Background Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of transcriptomes, arising as a powerful tool for discovering and characterizing cell types and their developmental trajectories. However, scRNA-seq analysis is complex, requiring a continuous, iterative process to refine the data and uncover relevant biological information. A diversity of tools has been developed to address the multiple aspects of scRNA-seq data analysis. However, an easy-to-use web application capable of conducting all critical steps of scRNA-seq data analysis is still lacking. Summary We present Asc-Seurat, a feature-rich workbench, providing an user-friendly and easy-to-install web application encapsulating tools for an all-encompassing and fluid scRNA-seq data analysis. Asc-Seurat implements functions from the Seurat package for quality control, clustering, and genes differential expression. In addition, Asc-Seurat provides a pseudotime module containing dozens of models for the trajectory inference and a functional annotation module that allows recovering gene annotation and detecting gene ontology enriched terms. We showcase Asc-Seurat’s capabilities by analyzing a peripheral blood mononuclear cell dataset. Conclusions Asc-Seurat is a comprehensive workbench providing an accessible graphical interface for scRNA-seq analysis by biologists. Asc-Seurat significantly reduces the time and effort required to analyze and interpret the information in scRNA-seq datasets.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Seamless integration of commercial Clouds with ATLAS Distributed Computing

The CERN ATLAS Experiment successfully uses a worldwide dis-tributed computing Grid infrastructure to support its physics programme at the Large Hadron Collider (LHC). The Grid workflow system PanDA routinely manages up to 700,000 concurrently running production and analysis jobs to process simulation and detector data. In total more than 500 PB of data are distributed over more than 150 sites in the WLCG and handled by the ATLAS data management system Rucio. To prepare for the ever growing data rate in future LHC runs new developments are underway to embrace industry accepted protocols and technologies, and utilize opportunistic resources in a standard way. This paper reviews how the Google and Amazon Cloud computing ser-vices have been seamlessly integrated as a Grid site within PanDA and Rucio. Performance and brief cost evaluations will be discussed. Such setups could offer advanced Cloud tool-sets and provide added value for analysis facilities that are under discussions for LHC Run-4.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Evaluation of Physical Security Risk for Potential Implementation of FLEX using Dynamic Simulation Methods

The requirements for United States nuclear power plants to maintain a large onsite physical security force contribute to their large operational costs. The cost of maintaining the current physical security posture is approximately 10% of the overall operation and maintenance budget for commercial nuclear power plants. The goal of the Light Water Reactor Sustainability Program Physical Security Pathway is to develop tools, methods, and technologies and provide the technical basis for an optimized physical security posture. This pathway will analyze and minimize the conservatisms built into current security postures in order to reduce security costs while still ensuring adequate security and operational safety. The research performed at Idaho National Laboratory within this pathway has successfully developed a dynamic force-on-force (FOF) modeling framework using various computer simulation tools and integrated them with the dynamic assessment Event Modeling Risk Assessment using Linked Diagrams (EMRALD) tool. This document provides an overview of lessons learned in applying a dynamic computational framework that links results from a commercially available FOF simulation tool, a commercially available thermal-hydraulic tool, and EMRALD to an operating commercial nuclear power plant. This process of including plant procedures and multiple analysis results is being called Modeling and Analysis for Safety Security using Dynamic EMRALD Framework. Previous reports described how a user could integrate their plant-specific FOF models with the dynamic simulation tool EMRALD, model operator actions, integrate with probabilistic risk assessment tools, such as Computer Aided Fault Tree Analysis System or Systems Analysis Programs for Hands-on Integrated Reliability Evaluations, and with thermal-hydraulic tools, such as RELAP-5. Previous reports applied various combinations of available simulations codes with EMRALD using generic plant models to demonstrate how to perform the analysis. This report documents the results of applying the dynamic computational framework to an actual nuclear facility using their security scenarios and timelines. The purpose of this study was to verify that results achieved using generic models are similar to actual plant results and to refine our guidance of the use of the framework. Such an assessment enables further analysis, such as what-if scenarios and staff-reduction evaluation, thereby optimizing physical security at plants. NOTE: The work performed in this report is based on a generic EMRALD model with actual plant data used for the analysis. However, only the generic model and general results of the analysis are in the report. No plant’s sensitive information is discussed in this report. The discussion shows examples of insights that can be obtained from the MASS-DEF methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗