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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↗

DOE OSTI2016

MARMOT update for oxide fuel modeling

This report summarizes the lower-length-scale research and development progresses in FY16 at Idaho National Laboratory in developing mechanistic materials models for oxide fuels, in parallel to the development of the MARMOT code which will be summarized in a separate report. This effort is a critical component of the microstructure based fuel performance modeling approach, supported by the Fuels Product Line in the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. The progresses can be classified into three categories: 1) development of materials models to be used in engineering scale fuel performance modeling regarding the effect of lattice defects on thermal conductivity, 2) development of modeling capabilities for mesoscale fuel behaviors including stage-3 gas release, grain growth, high burn-up structure, fracture and creep, and 3) improved understanding in material science by calculating the anisotropic grain boundary energies in UO$_2$ and obtaining thermodynamic data for solid fission products. Many of these topics are still under active development. They are updated in the report with proper amount of details. For some topics, separate reports are generated in parallel and so stated in the text. The accomplishments have led to better understanding of fuel behaviors and enhance capability of the MOOSE-BISON-MARMOT toolkit.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

Behavior of U 3 Si 2 Fuel and FeCrAl Cladding under Normal Operating and Accident Reactor Conditions

As part of the Department of Energy's Nuclear Energy Advanced Modeling and Simulation program, an Accident Tolerant Fuel High Impact Problem was initiated at the beginning of fiscal year 2015 to investigate the behavior of \usi~fuel and iron-chromium-aluminum (FeCrAl) claddings under normal operating and accident reactor conditions. The High Impact Problem was created in response to the United States Department of Energy's renewed interest in accident tolerant materials after the events that occurred at the Fukushima Daiichi Nuclear Power Plant in 2011. The High Impact Problem is a multinational laboratory and university collaborative research effort between Idaho National Laboratory, Los Alamos National Laboratory, Argonne National Laboratory, and the University of Tennessee, Knoxville. This report primarily focuses on the engineering scale research in fiscal year 2016 with brief summaries of the lower length scale developments in the areas of density functional theory, cluster dynamics, rate theory, and phase field being presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2017

Integration of Dakota into the NEAMS Workbench

This report summarizes a NEAMS (Nuclear Energy Advanced Modeling and Simulation) project focused on integrating Dakota into the NEAMS Workbench. The NEAMS Workbench, developed at Oak Ridge National Laboratory, is a new software framework that provides a graphical user interface, input file creation, parsing, validation, job execution, workflow management, and output processing for a variety of nuclear codes. Dakota is a tool developed at Sandia National Laboratories that provides a suite of uncertainty quantification and optimization algorithms. Providing Dakota within the NEAMS Workbench allows users of nuclear simulation codes to perform uncertainty and optimization studies on their nuclear codes from within a common, integrated environment. Details of the integration and parsing are provided, along with an example of Dakota running a sampling study on the fuels performance code, BISON, from within the NEAMS Workbench.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Treatment of Nuclear Data Covariance Information in Sample Generation

This report summarizes a NEAMS (Nuclear Energy Advanced Modeling and Simulation) project focused on developing a sampling capability that can handle the challenges of generating samples from nuclear cross-section data. The covariance information between energy groups tends to be very ill-conditioned and thus poses a problem using traditional methods for generated correlated samples. This report outlines a method that addresses the sample generation from cross-section matrices.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2017

ARC integration into the NEAMS Workbench

One of the objectives of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Integration Product Line (IPL) is to facilitate the deployment of the high-fidelity codes developed within the program. The Workbench initiative was launched in FY-2017 by the IPL to facilitate the transition from conventional tools to high fidelity tools. The Workbench provides a common user interface for model creation, real-time validation, execution, output processing, and visualization for integrated codes.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2018

NEAMS Workbench 1.0 Beta

The mission of the US Department of Energy (DOE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) Program is to develop, apply, deploy, and support state-of-the-art predictive modeling and simulation tools for the design and analysis of current and future nuclear energy systems. This is accomplished using computing architectures that range from laptops to leadership-class facilities. The NEAMS Workbench is a new initiative that will facilitate the transition from conventional tools to high-fidelity tools by providing a common user interface for model creation, review, execution, output review, and visualization for integrated codes. The Workbench can use common user input, including engineering scale specifications that are expanded into application-specific input requirements through the use of customizable templates. The templating process enables multi-fidelity analysis of a system from a common set of input data. Additionally, the common user input processor can provide an enhanced alternative application input that provides additional conveniences over the native input, especially for legacy codes. Expansion of the integrated codes and application templates available in the Workbench will broaden the NEAMS user community and will facilitate system analysis and design. Current and planned capabilities of the NEAMS Workbench are detailed here.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Final Report - PyVERAIn: An Open Source, Python-based CASL Preprocessing Platform for Reactor Geometry, Code Discretizations, and Parallel Domain Decompositions

The introduction of advanced modeling and simulation practices over the last several decades has proven to produce better products, more quickly, and at the reduced cost. This computational experimentation has enabled the design and development of a variety of innovative products by reducing the need to build expensive prototypes, and by allowing rapid, comprehensive design studies across a wide range of products. Despite these successes, there are significant barriers to widespread adoption of these tools. This project aims to address several of these barriers.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

RAVEN User Manual

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

In-Pile Instrumentation Initiative: A Multidisciplinary Scientific Approach for Characterization of Fuels and Materials

The In-Pile Instrumentation Initiative (I3) will conduct research needed to develop and deploy unique instruments to characterize the behavior of fuels and materials during irradiation tests conducted by Nuclear Energy (NE) programs within the Department of Energy. (Vision) I3 will provide real-time, accurate, spatially resolved information regarding performance of fuels and materials that can be directly tied to microstructure. This vision is reflected in the research direction of several NE programs that have identified a need to correlate material performance with evolving microstructure. Currently, these relationships can only be inferred through examination of materials in a post-irradiation environment. Realization of this vision will involve a sharp departure from the current approach that prioritizes instrument reliability over delivering optimal measurement capabilities. To realize this vision will require technical breakthroughs in several key areas, including materials science, modeling and simulation, advanced manufacturing, and instrument development. Insights gained from this initiative will be critical for advancing a number of NE objectives. Examples include expediting the development of accident-tolerant fuels, providing a deep understanding of materials suitable for the extreme environments associated with new reactor technologies, and providing unparalleled validation metrics for new science-based fuel performance codes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗