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DOE OSTI2023

Comparison Between Pin-by-Pin Subchannel and System Level Thermal Hydraulic Results for High Burnup Loss-of-Coolant Applications

This milestone report summarizes recent work to investigate higher fidelity modeling and simulation practices for large-break loss-of-coolant accident (LBLOCA) analysis in high-burnup pressurized water reactor (PWR) cores. Because of current industry interest in extending fuel cycle lengths ranging from 18 to 24-months, the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has been investigating the susceptibility of high-burnup core designs to fuel fragmentation, release, and dispersal (FFRD) during accident conditions such as LBLOCA. This work has prioritized developing and demonstrating a methodology for calculating core-wide susceptibility to FFRD and addressing uncertainties identified in the analysis and the US Nuclear Regulatory Commission (NRC) Research Information Letter on FFRD. Part of this investigation seeks to quantify differences between system-level thermal hydraulic behavior and higher fidelity subchannel modeling methods to identify potential safety concerns or opportunities to minimize FFRD susceptibility. To this end, the NEAMS subchannel code, CTF, is being used, along with the NRC system analysis code TRACE, for analysis of LBLOCA in a core containing high-burnup fuel. This project includes two thrusts: (1) improving on the existing TRACE model for a 4-loop PWR for LBLOCA so that the model is higher fidelity and consistent with current USNRC best practices, and (2) using CTF to perform pin-resolved modeling of the core region of the reflood phase of a LBLOCA in a high-burnup PWR.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2013

Using Multi-scale Dynamic Rupture Models to Improve Ground Motion Estimates: ALCF-2 Early Science Program Technical Report

This project uses dynamic rupture simulations to investigate high-frequency seismic energy generation. The relevant phenomena (frictional breakdown, shear heating, effective normal-stress fluctuations, material damage, etc.) controlling rupture are strongly interacting and span many orders of magnitude in spatial scale, requiring highresolution simulations that couple disparate physical processes (e.g., elastodynamics, thermal weakening, pore-fluid transport, and heat conduction). Compounding the computational challenge, we know that natural faults are not planar, but instead have roughness that can be approximated by power laws potentially leading to large, multiscale fluctuations in normal stress. The capacity to perform 3D rupture simulations that couple these processes will provide guidance for constructing appropriate source models for high-frequency ground motion simulations. The improved rupture models from our multi-scale dynamic rupture simulations will be used to conduct physicsbased (3D waveform modeling-based) probabilistic seismic hazard analysis (PSHA) for California. These calculation will provide numerous important seismic hazard results, including a state-wide extended earthquake rupture forecast with rupture variations for all significant events, a synthetic seismogram catalog for thousands of scenario events and more than 5000 physics-based seismic hazard curves for California.

58 GEOSCIENCES↗

DOE OSTI2016

Evolvix BEST Names for semantic reproducibility across code2brain interfaces

Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward an Evolvix syntax for general‐purpose programming that makes accurate modeling easy for biologists, we observed how names affect C2B quality. To protect learning and coding investments, C2B interfaces require long‐term backward compatibility and semantic reproducibility (accurate reproduction of computational meaning from coder‐brains to reader‐brains by code alone). Semantic reproducibility is often assumed until confusing synonyms degrade modeling in biology to deciphering exercises. We highlight empirical naming priorities from diverse individuals and roles of names in different modes of computing to show how naming easily becomes impossibly difficult. We present the Evolvix BEST (Brief, Explicit, Summarizing, Technical) Names concept for reducing naming priority conflicts, test it on a real challenge by naming subfolders for the Project Organization Stabilizing Tool system, and provide naming questionnaires designed to facilitate C2B debugging by improving names used as keywords in a stabilizing programming language. Our experiences inspired us to develop Evolvix using a flipped programming language design approach with some unexpected features and BEST Names at its core.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Multi-Physics Simulations of Heat Pipe Micro Reactor

System Analysis Module (SAM) is under development at Argonne National Laboratory as a modern system-level modeling and simulation tool for advanced non-light water reactor safety analyses. It utilizes the object-oriented application framework MOOSE to leverage the modern software environment and advanced numerical methods. The capabilities of SAM are being extended to enable the transient modeling, analysis, and design of various advanced nuclear reactor systems. This report presents the development of new capabilities for modeling the heat pipe type reactor systems. The need for power at remote locations away from a reliable electrical grid is an important niche for nuclear energy. Heat pipe-cooled fast-spectrum nuclear reactors are well suited for these applications. The key feature of the heat pipe reactors is the use of heat pipes for heat removal from the reactor core. The heat pipe makes use of the phase change of the working fluid and transports a large amount of heat from the evaporator to the condensation end with very small temperature drops. In contrast to the traditional nuclear reactor system that makes use of pumped loop for extracting the thermal power, the heat pipe reactors make use of hundreds of heat pipes for removing the thermal power (including the decay heat) passively. This could potentially significantly improve the reliability and safety of the reactor systems. The essential part in the analysis of a heat pipe type reactor is the modeling of heat transport inside the heat pipe. The capability of SAM was extended to enable the modeling of the conventional heat pipe. The full suite of non-LWR codes for confirmatory analysis at NRC is known as the Comprehensive Reactor Analysis Bundle (CRAB). It makes use of existing NRC codes, and integrates them with several codes developed through the DOE-NE’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. In this report, the heat pipe micro reactor is simulated using three submodules under CRAB. The MAMMOTH module is used to simulate the reactor kinetics behavior of the micro reactor; the SAM module is used to simulate the heat conduction in the reactor core and heat removal through the heat pipe heat exchangers and Reactor Cavity Cooling System (RCCS); and the MOOSE Tensor Mechanics module is used to simulate the thermal expansion of the reactor cores. The different sub-models are coupled together using MOOSE’s MultiApp system and executed using the BlueCrab application. The new capability has been demonstrated by a steady-state operation analysis, a failure of a single central heat pipe transient analysis, and a loss of heat sink transient analysis. The fully coupled model is shown to work well.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2015

Status Report on NEAMS System Analysis Module Development

Under the Reactor Product Line (RPL) of DOE-NE’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, an advanced SFR System Analysis Module (SAM) is being developed at Argonne National Laboratory. The goal of the SAM development is to provide fast-running, improved-fidelity, whole-plant transient analyses capabilities. SAM utilizes an object-oriented application framework MOOSE), and its underlying meshing and finite-element library libMesh, as well as linear and non-linear solvers PETSc, to leverage modern advanced software environments and numerical methods. It also incorporates advances in physical and empirical models and seeks closure models based on information from high-fidelity simulations and experiments. This report provides an update on the SAM development, and summarizes the activities performed in FY15 and the first quarter of FY16. The tasks include: (1) implement the support of 2nd-order finite elements in SAM components for improved accuracy and computational efficiency; (2) improve the conjugate heat transfer modeling and develop pseudo 3-D full-core reactor heat transfer capabilities; (3) perform verification and validation tests as well as demonstration simulations; (4) develop the coupling requirements for SAS4A/SASSYS-1 and SAM integration.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Loss of Coolant High Burnup (LOC-HBu) Program

LOC-HBu is a joint experimental program (JEEP) operating within the Nuclear Energy Agency’s (NEA’s) framework for irradiation experiments II (FIDES-II). LOC-HBu is dedicated to the understanding of light water reactor (LWR) fuel performance at high burnup under loss of coolant accident (LOCA) conditions. LOC-HBu aims to support burnup extension needs by addressing identified R&D priorities to achieve an improved understanding of fuel fragmentation, relocation, and dispersal (FFRD) of HBu fuel during LOCA events. The need for such experiments was identified in the United States by the Fuel Performance Testing Technical Expert Group (FPTTEG) under the Electric Power Research Institute (EPRI)’s Collaborative Research on Advanced Fuel Technologies (CRAFT) framework. A combined in-pile and furnace testing program plan was developed by researchers at Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL) which was reviewed and approved by the FPTTEG Combined TREAT-LOC and SATS LOCA Experiment Plan. The first 4 tests identified in this plan are being put forward by the U.S. Department of Energy (U.S. DOE) and INL for inclusion in the FIDES-II second triannual work period. The data produced under this plan will be used to further validate and confirm existing models and inform future R&D and model development. The experimental program was specifically developed to address data gaps and opportunities identified via detailed review of the existing public knowledge base on LOCA FFRD, as well as reviewing specific experimental development activities regarding prototypic LOCA conditions for light-water reactor (LWR) systems. The program will systematically investigate the impacts of prototypic HBu fuel/cladding thermomechanical behaviors under postulated LWR LOCA conditions not yet fully investigated. These conditions correspond with prototypic decay-energy heatup (DEH) and stored-energy heatup (SEH) conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Kinetics of low-temperature methane activation on IrO 2 (1 1 0): Role of local surface hydroxide species

The ability of the IrO 2 (1 1 0) surface to promote CH 4 activation at low temperatures (~150 K) suggests possibilities for developing IrO 2 -based catalysts to selectively convert light alkanes to high-value chemicals. In this study, we present experimental results and microkinetic simulations, based on density functional theory (DFT) calculations, of temperature programmed reaction spectra (TPRS) obtained for CH 4 σ-complexes adsorbed on IrO 2 (1 1 0), and focus on clarifying how surface OH groups modify the branching between CH 4 desorption, activation and subsequent reactions during TPRS. Our DFT results predict that surface OH groups strongly destabilize CH 4 σ-complexes on IrO 2 (1 1 0) in addition to deactivating surface O-atoms that are needed to achieve CH 4 activation at low temperature. We demonstrate that a microkinetic model that incorporates the influence of surface OH groups on the CH 4 binding and reactivity reproduces experimental TPRS results which show that adsorbed CH 4 σ-complexes preferentially dissociate at low CH 4 coverage, but that an increasing fraction of the adsorbed CH 4 desorbs at low temperature (~120 K) with increasing initial CH 4 or H coverage. The simulations reveal that low-temperature CH4 desorption during TPRS arises primarily from CH 4 σ-complexes that are kinetically-trapped between adjacent surface OH groups, and are thus destabilized and unable to access reactive O-atoms. In addition, when the effect of adjacent OH groups is incorporated we find that the PBE-D3 functional incorporating dispersion provides CH 4 binding energies that agree more closely with that obtained from the TPRS experiments. Overall, our results demonstrate that the local effect of adjacent OH groups must be incorporated into any microkinetic models to properly capture the selectivity between extensive and partial oxidation in alkane conversion on IrO 2 (1 1 0) under reaction conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Data-Driven Protection Software to classify fault locations by protective zone in distribution systems with high PV penetration

The software contains (a) the source codes to generate Point-on-Wave (PoW) transient data for any feeder model in Alternative Transient Program (ATP) format. Codes provide options to change different steady state settings, including the loading condition and PV capacity and transient state setting like faults type, location and initiation time (b) data post-processing source code to converted data from native format to COMTRADE, csv, HDF5 (c) Docker container to train CNN to classify fault locations by protective zone. The container takes dataset and other training parameters (sampling rate, training epochs, batch size etc) as input to train CNN. The container writes back the trained CNN model, training and testing metrics and plots to the local workstation

Ramesh, Meghana↗

DOE OSTI2023

Release on the Virtual Test Bed of a Molten Salt Reactor Experiment SAM-Pronghorn Coupled Model using the Domain Overlapping Approach

The nuclear industry is taking leaps in innovations with companies seeking a sustainable energy future through advanced nuclear reactors. The Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program seeks to substantiate and bolster the deployment of advanced reactors through flexible multifidelity, multiphysics simulations of advanced nuclear reactors. Applications like SAM for one-dimensional systems thermalhydraulics, and Pronghorn for multidimensional coarse mesh thermal-hydraulics, are geared to support innovations in industry by facilitating design, optimization, and licensing of advanced nuclear reactors. Coupling systems thermal-hydraulics and computational fluid dynamics codes can be difficult as the pressure coupling converges slowly; however, it is important to obtain the desired accuracy in each part of the primary loop. The authors of this model created an Overlapping-Domain Coupling (ODC) approach to coupling SAM and Pronghorn. Leveraging this coupling technique, a Molten Salt Reactor Experiment (MSRE) model was developed and released to the NEAMS/National Reactor Innovation Center (NRIC) Virtual Test Bed (VTB). The MSRE was chosen to be modeled because of the wealth of experimental data available and because of the strong physics coupling between the core and primary circuit. This paper contextualizes the history of the MSRE, describes the thermal hydraulics models used, and detail the implementation of multidimensional thermal-hydraulics and system codes based on the ODC method for the MSRE model. Finally, this paper presents how other modelers could apply the SAM Pronghorn ODC for other advanced reactor models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

High-Burnup BWR LOCA Burst Analysis Framework Development and Demonstration

Nuclear power currently contributes approximately 20% of total electricity generation in the United States and more than 10% globally. Given the increasing reliance on nuclear energy to achieve our nation’s goal of reaching net-zero carbon emissions by 2050, there is significant pressure on the existing nuclear industry to extend plant operational licenses and improve efficiency. This is crucial as the existing nuclear fleet serves as a vital bridge until new light water and advanced reactors can be developed and deployed, bolstering the supply of carbon-free energy to meet domestic demands. Operational costs primarily consist of plant operation and maintenance and fuel costs, influenced by materials and reactor core designs. These factors, coupled with heavily subsidized renewable energy markets, create a challenging economic environment for the existing light water reactor fleet, as well as for new build projects. To address these economic challenges, the nuclear industry has developed a strategic blueprint aimed at enhancing nuclear power’s economic sustainability. Past initiatives, such as efforts to eliminate fuel failures by 2010 and reduce operating costs by 30% before 2020, have laid the groundwork. Optimizing core design parameters, including burnup limits and enrichment levels, can lengthen cycles, reduce outages, reduce batch reload batch fractions and spent fuel storage requirements, and lower maintenance and operating expenses, thereby enhancing economic viability. In the United States, boiling water reactors (BWRs) comprise approximately one-third of the fleet, although much of the research and development focus has traditionally been on pressurized water reactors (PWRs). Advances in modeling and simulation, particularly through the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, are crucial to the long-term viability of BWRs, just as they are for PWRs. A key research area of the high burnup/increased enriched fuel initiative is focused on addressing loss-of-coolant-accident (LOCA)-related issues. NEAMS has dedicated significant effort to enhancing tools to better support BWRs, with a current focus on showcasing the BWR framework for high-burnup LOCA analysis. This high-fidelity work will demonstrate a best estimate pin-by-pin high-burnup BWR LOCA analysis to assess full-core cladding rupture behavior. This modeling capability will help with better understanding and realistic evaluation of fuel fragmentation, relocation, and dispersal (FFRD) phenomena at BWRs, which then could be used to prevent FFRD at BWRs without penalizing operational parameters. In addition, the results of this work will help identify strategies to identify additional margins or to potentially limit cladding rupture through core design optimizations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2018

Optimal Control for Battery Storage Using Nonlinear Models

Battery storage system (BSS) becomes increasingly popular for grid applications due to the advance in battery and power electronics technologies as well as the growing need of flexibility and reserve from power systems with rapidly developed renewable generation. Successful assessment and deployment of BSS requires optimizing its operation and therefore maximizing the potential benefits. Although there exist studies that develop optimal control for evaluation or operational scheduling of BSS, modeling of charging/discharging operation and the corresponding impacts on state-of-charge (SOC) is over simplified in these methods. This paper develops an optimal control for BSS evaluation and operational scheduling using a general nonlinear model that expresses the change of SOC as a function of charging/discharging power and SOC level. The proposed method is compared with a typical existing method through a real-world energy storage evaluation project to show the significance of the proposed method.

battery, energy storage system, dynamic programmin↗

DOE OSTI2016

RAVEN Theory Manual

RAVEN is a software framework able to perform parametric and stochastic analysis based on the response of complex system codes. The initial development was aimed at providing dynamic risk analysis capabilities to the thermohydraulic code RELAP-7, currently under development at Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose stochastic and uncertainty quantification platform, capable of communicating with any system code. In fact, the provided Application Programming Interfaces (APIs) allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating system response and explore input space using various sampling schemes such as Monte Carlo, grid, or Latin hypercube. However, RAVEN strength lies in its system feature discovery capabilities such as: constructing limit surfaces, separating regions of the input space leading to system failure, and using dynamic supervised learning techniques. The development of RAVEN started in 2012 when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework arose. RAVEN’s principal assignment is to provide the necessary software and algorithms in order to employ the concepts developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just to identify the frequency of an event potentially leading to a system failure, but the proximity (or lack thereof) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. peak pressure in a pipe) is exceeded under certain conditions. Most of the capabilities, implemented having RELAP-7 as a principal focus, are easily deployable to other system codes. For this reason, several side activates have been employed (e.g. RELAP5-3D, any MOOSE-based App, etc.) or are currently ongoing for coupling RAVEN with several different software. The aim of this document is to provide a set of commented examples that can help the user to become familiar with the RAVEN code usage.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

RAVEN Theory Manual

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

Energy Impacts of Nonlinear Behavior of PCM When Applied into Building Envelope: Preprint

Previous research on phase change materials (PCM) for building applications has been done for several decades resulting in plenty of literature on PCM properties, temperature, and peak reduction potential. Thus, PCMs are a potential technology to reduce peak loads and HVAC energy consumption in buildings. There are few building energy simulation programs that have PCM modeling features, and evenfewer have been validated. Additionally, there is no previous research that indicates the level of accuracy when simulating PCM from a building energy simulation perspective. This study analyzes the effects a nonlinear enthalpy profile has on thermal performance and expected energy benefits for PCM-enhanced insulation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focusing on the input structure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Dataset Repository for Investigating Suicide Risk Using Social and Environmental Determinants of Health

Suicide is frequently modeled as a function of genetics and environment, where the latter refers to factors other than direct biological consequences, such as air quality, financial level, social connectivity, transportation and food access, and homelessness status. According to the World Health Organization, clean air, a stable climate, adequate water, sanitation and hygiene, safe chemical use, radiation protection, healthy and safe workplaces, sound agricultural practices, health-supportive cities and built environments, and a preserved natural environment are all prerequisites for good health. Understanding the relationships between these determinants and mental health outcomes requires standardized data that can be included in healthcare programs and health outcome models. There is a wealth of publicly available data on social and environmental factors provided by various US organizations that can benefit the design of health care systems and public health interventions, as well as improve our comprehension of factors that impact health. Such information would not only help improve the understanding of individual and community risk but also identify new risk factors that have not previously been therapeutically targeted, especially in terms of their impact on mental health. However, curating and standardizing such datasets is challenging because they are often recorded at numerous geographical and temporal resolutions and with varying spatial and temporal granularities. To address this challenge, we launched an endeavor in conjunction with the Veterans Health Administration to collect publicly available socioeconomic and environmental determinants of health statistics in the US. In this manuscript, we describe a social and environmental determinants of health (SEDH) datasets repository, data curation documentation, and a pipeline framework for data generation; This effort started in 2020, when we began constructing a scalable pipeline to automate the download, extraction, preparation, analysis, and production of datasets. These datasets have been made available to the VHA and may be shared upon agreement with collaborating organizations.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activaties are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗