Physics-based multiscale coupling for full core nuclear reactor simulation

Numerical simulation of nuclear reactors is a key technology in the quest for improvements in efficiency, safety, and reliability of both existing and future reactor designs. Historically, simulation of an entire reactor was accomplished by linking together multiple existing codes that each simulated a subset of the relevant multiphysics phenomena. Recent advances in the MOOSE (Multiphysics Object Oriented Simulation Environment) framework have enabled a new approach: multiple domain-specific applications, all built on the same software framework, are efficiently linked to create a cohesive application. This is accomplished with a flexible coupling capability that allows for a variety of different data exchanges to occur simultaneously on high performance parallel computational hardware. Examples based on the KAIST-3A benchmark core, as well as a simplified Westinghouse AP-1000 configuration, demonstrate the power of this new framework for tackling—in a coupled, multiscale manner—crucial reactor phenomena such as CRUD-induced power shift and fuel shuffle.Massachusetts Institute of Technology. Department of Nuclear Science and EngineeringIdaho National Laboratory (Contract DE-AC07-05ID14517

Scalable Distributed Feature Tracking and Remapping on Adaptive Unstructured Meshes for Finite Element Simulations

Phase field modeling (PFM) is a well-known technique for simulating microstructural evolution. To model grain growth using PFM, typically each grain feature is assigned a unique non-conserved spatial variable known as an order parameter. Each order parameter field is then evolved in time. Traditional approaches for modeling these individual grains uses a one-to-one mapping of grains to order parameters since the interactions among grains is not known a priori. This presents a challenge when modeling large numbers of grains due to the computational expense of using many order parameters. This problem is exacerbated when using common numerical solution schemes including the fully-implicit finite element method (FEM), as the global matrix size is proportional to the number of order parameters squared. While previous work has developed methods to reduce the number of required variables and thus the computational complexity, none of the existing approaches can be applied to an implicit FEM implementation of PFM. Additionally, polycrystal modeling with grain growth and other coupled physics requires careful tracking of each grain's position and orientation, which is lost when using a reduced number of variables. Here, we present a modular, scalable distributed feature tracking and remapping algorithm suitable for solving these deficiencies. The algorithm presented in this dissertation maintains a unique ID for each grain even after variable remapping without restricting the underlying modeling method. This approach enables fully-coupled multiphysics using a fully generalized finite element method. Implementation details and comparative results of using this approach are presented.Thesis (Ph.D., Computer Science) -- University of Idaho, 201

Breaking the power law: Multiscale simulations of self-ion irradiated tungsten

The initial stage of radiation defect creation has often been shown to follow a power law distribution at short time scales, recently so with tungsten, following many self-organizing patterns found in nature. The evolution of this damage, however, is dominated by interactions between defect clusters, as the coalescence of smaller defects into clusters depends on the balance between transport, absorption, and emission to/from existing clusters. The long-time evolution of radiation-induced defects in tungsten is studied with cluster dynamics parameterized with lower length scale simulations, and is shown to deviate from a power law size distribution. The effects of parameters such as dose rate and total dose, as parameters affecting the strength of the driving force for defect evolution, are also analyzed. Excellent agreement is achieved with regards to an experimentally measured defect size distribution at 30 K. This study provides another satisfactory explanation for experimental observations in addition to that of primary radiation damage, which should be reconciled with additional validation data

Massive Hybrid Parallelism for Fully Implicit Multiphysics M&C 2013 MASSIVE HYBRID PARALLELISM FOR FULLY IMPLICIT MULTIPHYSICS

ABSTRACT As hardware advances continue to modify the supercomputing landscape, traditional scientific software development practices will become more outdated, ineffective, and inefficient. The process of rewriting/retooling existing software for new architectures is a Sisyphean task, and results in substantial hours of development time, effort, and money. Software libraries which provide an abstraction of the resources provided by such architectures are therefore essential if the computational engineering and science communities are to continue to flourish in this modern computing environment. The Multiphysics Object Oriented Simulation Environment (MOOSE) framework enables complex multiphysics analysis tools to be built rapidly by scientists, engineers, and domain specialists, while also allowing them to both take advantage of current HPC architectures, and efficiently prepare for future supercomputer designs. MOOSE employs a hybrid shared-memory and distributed-memory parallel model and provides a complete and consistent interface for creating multiphysics analysis tools. In this paper, a brief discussion of the mathematical algorithms underlying the framework and the internal object-oriented hybrid parallel design are given. Representative massively parallel results from several applications areas are presented, and a brief discussion of future areas of research for the framework are provided

An Implicit Solution Framework for Reactor Fuel Performance Simulation

The simulation of nuclear reactor fuel performance involves complex thermomechanical processes between fuel pellets, made of fissile material, and the protective cladding that surrounds the pellets. An important design goal for a fuel is to maximize the life of the cladding thereby allowing the fuel to remain in the reactor for a longer period of time to achieve higher degrees of burnup. This presentation presents an initial approach for modeling the thermomechanical response of reactor fuel, and details of the solution method employed within INL's fuel performance code, BISON. The code employs advanced methods for solving coupled partial differential equation systems that describe multidimensional fuel thermomechanics, heat generation, and oxygen transport within the fuel. This discussion explores the effectiveness of a JFNK-based solution of a problem involving three dimensional fully coupled, nonlinear transient heat conduction and that includes pellet displacement and oxygen diffusion effects. These equations are closed using empirical data that is a function of temperature, density, and oxygen hyperstoichiometry. The method appears quite effective for the fuel pellet / cladding configurations examined, with excellent nonlinear convergence properties exhibited on the combined system. In closing, fully coupled solutions of three dimensional thermomechanics coupled with oxygen diffusion appear quite attractive using the JFNK approach described here, at least for configurations similar to those examined in this report

MASSIVE HYBRID PARALLELISM FOR FULLY IMPLICIT MULTIPHYSICS

As hardware advances continue to modify the supercomputing landscape, traditional scientific software development practices will become more outdated, ineffective, and inefficient. The process of rewriting/retooling existing software for new architectures is a Sisyphean task, and results in substantial hours of development time, effort, and money. Software libraries which provide an abstraction of the resources provided by such architectures are therefore essential if the computational engineering and science communities are to continue to flourish in this modern computing environment. The Multiphysics Object Oriented Simulation Environment (MOOSE) framework enables complex multiphysics analysis tools to be built rapidly by scientists, engineers, and domain specialists, while also allowing them to both take advantage of current HPC architectures, and efficiently prepare for future supercomputer designs. MOOSE employs a hybrid shared-memory and distributed-memory parallel model and provides a complete and consistent interface for creating multiphysics analysis tools. In this paper, a brief discussion of the mathematical algorithms underlying the framework and the internal object-oriented hybrid parallel design are given. Representative massively parallel results from several applications areas are presented, and a brief discussion of future areas of research for the framework are provided