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DOE OSTI2023

MOOSE Reactor Module: An Open-Source Capability for Meshing Nuclear Reactor Geometries

The U.S. Department of Energy (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has developed numerous physics solvers utilizing the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) framework for multiphysics reactor analysis. These solvers require input finite element meshes representing the discretized spatial domain. Typically, reactor analysts turn to licensed tools for the creation of reactor geometry meshes. Recently, open-source functionality has been added to the MOOSE framework to mesh common reactor geometries and improve MOOSE-based nuclear reactor application user workflows. The new functionality is primarily contained in the new Reactor module of MOOSE and includes support for hexagonal pins, assemblies, and cores, extended Cartesian geometry support, options for modeling static and rotating control drums within a hexagonal assembly, core periphery triangulation, and automatic tagging of pin, assembly, plane, and depletion regions for easier post processing of physics results. A set of reactor geometry mesh builder objects further streamlines the construction of hexagonal and Cartesian cores and allows mapping of materials to regions during mesh generation. The meshes produced with the MOOSE Reactor module may be used directly within MOOSE-based applications or exported as Exodus II files for use in other finite element solvers. The tools have been demonstrated and verified using a variety of NEAMS physics solvers on a range of reactor applications, including a sodium-cooled fast reactor core analysis using Griffin, a fast reactor assembly thermal deformation analysis using MOOSE Tensor Mechanics, and a heat pipe–cooled microreactor coupled analysis using Griffin, Bison, and Sockeye. MOOSE’s Reactor module provides significant advantages compared to the use of external meshing tools when analyzing Cartesian and hexagonal reactor lattices using MOOSE-based applications: immediate accessibility (open-source) to the end user, low barrier to entry for new users, speed of mesh generation, volume preservation of meshed fuel pins, and simplification of analysis workflow when used in conjunction with MOOSE-based applications.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

High-Fidelity Multiphysics Modeling of a Heat Pipe Microreactor Using BlueCrab

Researchers who are actively developing nuclear microreactors are planning to employ innovative designs and features using traditional commercial modeling tools that may be inadequate for their design and licensing activities. The codes developed under the U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) program provide flexibility in terms of geometry modeling and multiphysics coupling and are particularly well suited for modeling novel microreactor concepts. To test the maturity of these codes, this paper introduces a conceptual heat pipe microreactor (HP-MR) designed to gather various technologies of interest to microreactor developers such as control drums, heat pipes, and hydride moderators. Here, the objective of this effort is to demonstrate NEAMS tools capability to perform high-fidelity multiphysics simulations, using coupled neutronics (via the Griffin code), heat conduction (via the BISON code), heat pipe modeling (via the Sockeye code), and hydrogen redistribution in hydride metal moderator (via the SWIFT code). Codes are coupled in-memory through the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which permits flexible multiphysics data transfer schemes. The analysis confirmed two key aspects of the HP-MR concept: (1) its ability to follow the power load requested from the heat pipe and (2) its ability to avoid heat pipe cascading failure unless designed with high power close to operating failure limits of its heat pipes. The developed computational model was distributed publicly on the Virtual Test Bed for training purposes to accelerate adoption by industry and to provide a high-fidelity multiphysics solution for benchmarking against other tools. Additional multiphysics analyses including other transients and coupled physics were identified as necessary future work, together with a focus on validating multiphysics behavior against experiments.

Microreactor↗

DOE OSTI2022

Sockeye Validation Support Using the SPHERE Facility

The engineering-scale heat pipe application Sockeye, being developed through the Nuclear En- ergy Advanced Modeling and Simulation program is concurrently undergoing code development and verification and validation activities. The Single Primary Heat Extraction and Removal Emu- lator (SPHERE) facility at Idaho National Laboratory has performed numerous sodium heat pipe experiments, one of which is modeled in this work to support Sockeye validation. Heat pipe data produced by the SPHERE facility was found to be very useful not only for Sockeye validation but also as a useful resource for model development. A Sockeye simulation performed using Sockeye’s conduction model modeled an experimental run at SPHERE and was found to be able to accurately reproduce the steady temperature distribution but revealed discrepancies in transient behavior after shutoff of the power. This effort also revealed a number of challenges of modeling an experimental setup such as SPHERE, for example, the importance of details in the setup such as possible gaps in insulation. Also, continuous collaboration between Sockeye developers and SPHERE experimentalists has been established, which allows future experiments to be refined in response to questions and challenges found by simulating earlier experiments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Coupling coarse-mesh CFD with fine-mesh CFD for modeling molten-salt reactors in the Virtual Test Bed (VTB)

The Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims at developing a simulation tool kit to accelerate the development and deployment of nuclear power technologies. NEAMS multiphysics tools have been designed to provide numerical simulation support for the design and licensing of GEN IV reactors. Pronghorn is NEAMS's coarse-mesh computational fluid dynamics (CFD) tool, which is designed to run 3D core transients in GEN IV reactors at a reduced computational cost. To increase their accuracy, coarse-mesh CFD simulations require calibrated closure coefficients. One way of computing these coefficients is via the Nek5000, NEAMS's high-fidelity CFD tool. This article discusses our current research lines in informing Pronghorn closure coefficients via Nek5000 to enable multiphysics simulations of the core cavity of the molten-salt fast reactor. We present an application in which Pronghorn mixing length turbulent viscosity has been calibrated from Nek5000 simulations. The resulting Pronghorn thermal-hydraulics model is then coupled to Griffin, the NEAMS neutron transport solver, to solve for the steady-state configuration of the molten-salt fast reactor. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2015

An Update on NiCE Support for BISON

The Nuclear Energy Advanced Modeling and Simulation program (NEAMS) from the Department of Energy s Office of Nuclear Energy has funded the development of a modeling and simulation workflow environment to support the various codes in its nuclear energy scientific computing toolkit. This NEAMS Integrated Computational Environment (NiCE) provides extensible tools and services that enable efficient code execution, input generation, pre-processing visualizations, and post-simulation data analysis and visualization for a large portion of the NEAMS Toolkit. A strong focus for the NiCE development team throughout FY 2015 has been support for the Multiphysics Object Oriented Simulation Environment (MOOSE) and the NEAMS nuclear fuel performance modeling application built on that environment, BISON. There is a strong desire in the program to enable and facilitate the use of BISON throughout nuclear energy research and industry. A primary result of this desire is the need for strong support for BISON in NiCE. This report will detail improvements to NiCE support for BISON. We will present a new and improved interface for interacting with BISON simulations in a variety of ways: (1) improved input model generation, (2) embedded mesh and solution data visualizations, and (3) local and remote BISON simulation launch. We will also show how NiCE has been extended to provide support for BISON code development.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2015

Supplying materials needed for grain growth characterizations of nano-grained UO 2

This activity is supported by the US Nuclear Energy Advanced Modeling and Simulation (NEAMS) Fuels Product Line (FPL) and aims at providing experimental data for the validation of the mesoscale simulation code MARMOT. MARMOT is a mesoscale multiphysics code that predicts the coevolution of microstructure and properties within reactor fuel during its lifetime in the reactor. It is an important component of the Moose-Bison-Marmot (MBM) code suite that has been developed by Idaho National Laboratory (INL) to enable next generation fuel performance modeling capability as part of the NEAMS Program FPL. In order to ensure the accuracy of the microstructure based materials models being developed within the MARMOT code, extensive validation efforts must be carried out. In this report, we summarize our preliminary synchrotron radiation experiments at APS to determine the grain size of nanograin UO 2 . The methodology and experimental setup developed in this experiment can directly apply to the proposed in-situ grain growth measurements. The investigation of the grain growth kinetics was conducted based on isothermal annealing and grain growth characterization as functions of duration and temperature. The kinetic parameters such as activation energy for grain growth for UO 2 with different stoichiometry are obtained and compared with molecular dynamics (MD) simulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Synchrotron characterization of nanograined UO 2 grain growth

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

FASTGRASS implementation in BISON and Fission gas behavior characterization in UO 2 and connection to validating MARMOT

This activity is supported by the US Nuclear Energy Advanced Modeling and Simulation (NEAMS) Fuels Product Line (FPL). Two major accomplishments in FY 15 are summarized in this report: (1) implementation of the FASTGRASS module in the BISON code; and (2) a Xe implantation experiment for large-grained UO 2 . Both BISON AND MARMOT codes have been developed by Idaho National Laboratory (INL) to enable next generation fuel performance modeling capability as part of the NEAMS Program FPL. To contribute to the development of the Moose-Bison-Marmot (MBM) code suite, we have implemented the FASTGRASS fission gas model as a module in the BISON code. Based on rate theory formulations, the coupled FASTGRASS module in BISON is capable of modeling LWR oxide fuel fission gas behavior and fission gas release. In addition, we conducted a Xe implantation experiment at the Argonne Tandem Linac Accelerator System (ATLAS) in order to produce the needed UO 2 samples with desired bubble morphology. With these samples, further experiments to study the fission gas diffusivity are planned to provide validation data for the Fission Gas Release Model in MARMOT codes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Sensitivity Analysis of OECD Benchmark Tests in BISON

This report summarizes a NEAMS (Nuclear Energy Advanced Modeling and Simulation) project focused on sensitivity analysis of a fuels performance benchmark problem. The benchmark problem was defined by the Uncertainty Analysis in Modeling working group of the Nuclear Science Committee, part of the Nuclear Energy Agency of the Organization for Economic Cooperation and Development (OECD ). The benchmark problem involv ed steady - state behavior of a fuel pin in a Pressurized Water Reactor (PWR). The problem was created in the BISON Fuels Performance code. Dakota was used to generate and analyze 300 samples of 17 input parameters defining core boundary conditions, manuf acturing tolerances , and fuel properties. There were 24 responses of interest, including fuel centerline temperatures at a variety of locations and burnup levels, fission gas released, axial elongation of the fuel pin, etc. Pearson and Spearman correlatio n coefficients and Sobol' variance - based indices were used to perform the sensitivity analysis. This report summarizes the process and presents results from this study.

97 MATHEMATICS AND COMPUTING↗

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↗

DOE OSTI2015

Results and Analysis of the Research and Development Work Scope Request for Information (DE-SOL-0008246)

The Department of Energy (DOE) Office of Nuclear Energy (NE) released a request for information (RFI) (DE-SOL-0008246) for “University, National Laboratory, Industry and International Input to the Office of Nuclear Energy’s Competitive Research and Development Work Scope Development” on April 13, 2015. DOE-NE solicited information for work scopes for the four main program areas as well as any others suggested by the community. The RFI proposal period closed on June 19, 2015. From the 124 responses, 238 individual work scopes were extracted. Thirty-three were associated with a DOE national laboratory, including Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Idaho National Laboratory (INL), Los Alamos National Laboratory (LANL), Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL). Thirty US universities submitted proposals as well as ten industrial/commercial institutions. Four major R&D areas emerged from the submissions, appearing in more than 15% of the proposed work scopes. These were: nuclear fuel studies, safety and risk analysis, nuclear systems analysis and design and advanced instrumentation and controls. Structural materials for nuclear power plants, used nuclear fuel disposition and various types of systems analysis were also popular, each appearing in more than 10% of the proposals. Nuclear Energy Enabling Technologies (NEET) was the most popular program area with 42% of the proposals referencing the NEET-CTD program. The order of the remaining programs was Fuel Cycle Technologies (FC) at 34%, Nuclear Energy Advanced Modeling and Simulation (NEAMS) at 29% and Reactor Concepts at 17%.

99 GENERAL AND MISCELLANEOUS↗

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 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 OSTI2016

Dakota Uncertainty Quantification Methods Applied to the CFD code Nek5000

This report presents the state of advancement of a Nuclear Energy Advanced Modeling and Simulation (NEAMS) project to characterize the uncertainty of the computational fluid dynamics (CFD) code Nek5000 using the Dakota package for flows encountered in the nuclear engineering industry. Nek5000 is a high-order spectral element CFD code developed at Argonne National Laboratory for high-resolution spectral-filtered large eddy simulations (LESs) and unsteady Reynolds-averaged Navier-Stokes (URANS) simulations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

M3MS-16OR0401086 – Report on NEAMS Workbench Support for MOOSE Applications

This report summarizes the status of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench from Oak Ridge National Laboratory (ORNL) and the integration of the MOOSE framework. This report marks the completion of NEAMS milestone M3MS-16OR0401086. This report documents the developed infrastructure to support the MOOSE framework applications, the applications’ results, visualization status, the collaboration that facilitated this progress, and future considerations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2016

Experimental studies of Micro- and Nano-grained UO 2 : Grain Growth Behavior, Sufrace Morphology, and Fracture Toughness

This activity is supported by the US Nuclear Energy Advanced Modeling and Simulation (NEAMS) Fuels Product Line (FPL) and aims at providing experimental data for the validation of the mesoscale simulation code MARMOT. MARMOT is a mesoscale multiphysics code that predicts the coevolution of microstructure and properties within reactor fuel during its lifetime in the reactor. It is an important component of the Moose-Bison-Marmot (MBM) code suite that has been developed by Idaho National Laboratory (INL) to enable next generation fuel performance modeling capability as part of the NEAMS Program FPL. In order to ensure the accuracy of the microstructure-based materials models being developed within the MARMOT code, extensive validation efforts must be carried out. In this report, we summarize the experimental efforts in FY16 including the following important experiments: (1) in-situ grain growth measurement of nano-grained UO 2 ; (2) investigation of surface morphology in micrograined UO 2 ; (3) Nano-indentation experiments on nano- and micro-grained UO 2 . The highlight of this year is: we have successfully demonstrated our capability to in-situ measure grain size development while maintaining the stoichiometry of nano-grained UO 2 materials; the experiment is, for the first time, using synchrotron X-ray diffraction to in-situ measure grain growth behavior of UO 2 .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

Nuclear-Renewable Hybrid Energy Systems: 2016 Technology Development Program Plan

The United States is in the midst of an energy revolution, spurred by advancement of technology to produce unprecedented supplies of oil and natural gas. Simultaneously, there is an increasing concern for climate change attributed to greenhouse gas (GHG) emissions that, in large part, result from burning fossil fuels. An international consensus has concluded that the U.S. and other developed nations have an imperative to reduce GHG emissions to address these climate change concerns. The global desire to reduce GHG emissions has led to the development and deployment of clean energy resources and technologies, particularly renewable energy technologies, at a rapid rate. At the same time, each of the major energy sectors—the electric grid, industrial manufacturing, transportation, and the residential/commercial consumers— is increasingly becoming linked through information and communications technologies, advanced modeling and simulation, and controls. Coordination of clean energy generation technologies through integrated hybrid energy systems, as defined below, has the potential to further revolutionize energy services at the system level by coordinating the exchange of energy currency among the energy sectors in a manner that optimizes financial efficiency (including capital investments), maximizes thermodynamic efficiency (through best use of exergy, which is the potential to use the available energy in producing energy services), reduces environmental impacts when clean energy inputs are maximized, and provides resources for grid management. Rapid buildout of renewable technologies has been largely driven by local, state, and federal policies, such as renewable portfolio standards and production tax credits that incentivize investment in these generation sources. A foundational assumption within this program plan is that renewable technologies will continue to be major contributors to the future U.S. energy infrastructure. While increased use of clean renewable technologies will aid in achieving reduced GHG emissions, it also presents new challenges to grid management that must be addressed. These challenges primarily derive from the fundamental characteristics of variable renewable generators, such as wind and solar: non-dispatchability, variable production, and reduced electromechanical inertia. This document presents a preliminary research and development (R&D) plan for detailed dynamic simulation and analysis of nuclear-renewable hybrid energy systems (N-R HES), coupled with integrated energy system design, component development, and integrated systems testing. N-R HES are cooperatively-controlled systems that dynamically apportion thermal and/or electrical energy to provide responsive generation to the power grid.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis 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 uncer- tainty 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 in- terested 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 assess- ment capability to RELAP-7, currently under develop-ment 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 explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗