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Permann, Cody

Publications and source records attributed to Permann, Cody.

28 records · Page 2

NEAMS-IPL MOOSE Framework Activities 2017

The MOOSE Framework is a modular pluggable framework for building complex simulations. The ability to add new objects with custom syntax is a core capability that makes MOOSE a powerful platform for coupling multiple applications together within a single environment. The creation of a new, more standardized JSON syntax output improves the external interfaces for generating graphical components or for validating input file syntax automatically without the need for explicit developer interaction. The design of this interface and the requirements it satisfies are covered in this short report.

97 MATHEMATICS AND COMPUTING↗

MOOSE IPL Extensions (Documentation)

In FY-2015, The development of MOOSE was driven by the needs of the NEAMS MOOSE-based applications, BISON, MARMOT, and RELAP-7. An emphasis was placed on the continued upkeep and improvement MOOSE in support of the product line integration goals. New unified documentation tools have been developed, several improvements to regression testing have been enforced and overall better software quality practices have been implemented. In addition the Multiapps and Transfers systems have seen significant refactoring and robustness improvements, as has the “Restart and Recover” system in support of Multiapp simulations. Finally, a completely new “Control Logic” system has been engineered to replace the prototype system currently in use in the RELAP-7 code. The development of this system continues and is expected to handle existing needs as well as support future enhancements.

99 GENERAL AND MISCELLANEOUS↗

Model Development in MARMOT for High Burn-up Structure Formation

A phase field model for the formation of High Burn-up Structure (HBS) was developed and implemented in MARMOT. The model takes into consideration the chemical energy of gas atoms, interfacial energies of grain boundaries and bubble surfaces, and strain energy associated with dislocations, which renders the model capable of simulating the concurrent formation and growth of both bubbles and sub-grains. Moreover, in contrast to existing phase-field models of recrystallization, the current model makes no ad hoc or a priori assumptions for the nucleation rate or recrystallized grain size and shape. All the model parameters were quantified in terms of thermodynamic and kinetic properties. The model predicts a strong effect of magnitude and distribution of dislocation density, grain boundary energy, and bubble surface energy on the formation of sub-grains. The model results were shown to agree well with theoretical predictions. For polycrystalline UO 2 at 1200K, the recrystallized average grain size was found to be on the order of of 0.6-1.3 microns at a burn-up in the range of 98-105 GWD/tU. These values lie well within the range of reported data in literature.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Mammoth

The MAMMOTH radiation kinetics and reactor physics code is being developed at the Idaho National Laboratory (INL). The code is based on INL's modern scientific software development framework MOOSE (Multi-Physics Object Oriented Simulation Environment).

Ortensi, Javier↗

NEAMS-IPL MOOSE Midyear Framework Activities

The MOOSE Framework is a modular pluggable framework for building complex simulations. The ability to add new objects with custom syntax is a core capability that makes MOOSE a powerful platform for coupling multiple applications together within a single environment. The creation of a new, more standardized JSON syntax output improves the external interfaces for generating graphical components or for validating input file syntax. The design of this interface and the requirements it satisfies are covered in this short report.

97 MATHEMATICS AND COMPUTING↗

Raven

RAVEN (Risk Analysis Virtual Environment) is a flexible and multi-purpose probabilistic risk analysis, uncertainty quantification, parameter optimization and data knowledge-discovering framework. https://raven.inl.gov/

Slaughter, AndrewE.↗

THE APPLICATION OF MAMMOTH FOR A DETAILED TIGHTLY COUPLED FUEL PIN SIMULATION WITH A STATION BLACKOUT

Accurate calculation of desired quantities to predict fuel behavior requires the solution of interlinked equations representing different physics. Traditional fuels performance codes often rely on internal empirical models for the pin power density and a simplified boundary condition on the cladding edge. These simplifications are performed because of the difficulty of coupling applications or codes on differing domains and mapping the required data. To demonstrate an approach closer to first principles, the neutronics application Rattlesnake and the thermal hydraulics application RELAP-7 were coupled to the fuels performance application BISON under the master application MAMMOTH. A single fuel pin was modeled based on the dimensions of a Westinghouse 17x17 fuel rod. The simulation consisted of a depletion period of 1343 days, roughly equal to three full operating cycles, followed by a station blackout (SBO) event. The fuel rod was depleted for 1343 days for a near constant total power loading of 65.81 kW. After 1343 days the fission power was reduced to zero (simulating a reactor shut-down). Decay heat calculations provided the time-varying energy source after this time. For this problem, Rattlesnake, BISON, and RELAP-7 are coupled under MAMMOTH in a split operator approach. Each system solves its physics on a separate mesh and, for RELAP-7 and BISON, on only a subset of the full problem domain. Rattlesnake solves the neutronics over the whole domain that includes the fuel, cladding, gaps, water, and top and bottom rod holders. Here BISON is applied to the fuel and cladding with a 2D axi-symmetric domain, and RELAP-7 is applied to the flow of the circular outer water channel with a set of 1D flow equations. The mesh on the Rattlesnake side can either be 3D (for low order transport) or 2D (for diffusion). BISON has a matching ring structure mesh for the fuel so both the power density and local burn up are copied accurately from Rattlesnake. At each depletion time step, Rattlesnake calculates a power density, fission density rate, burn-up distribution and fast flux based on the current water density and fuel temperature. These are then mapped to the BISON mesh for a fuels performance solve. BISON calculates the fuel temperature and cladding surface temperature based upon the current power density and bulk fluid temperature. RELAP-7 then calculates the fluid temperature, water density fraction and water phase velocity based upon the cladding surface temperature. The fuel temperature and the fluid density are then passed back to Rattlesnake for another neutronics calculation. Six Picard or fixed-point style iterations are preformed in this manner to obtain consistent tightly coupled and stable results. For this paper a set of results from the detailed calculation are provided for both during depletion and the SBO event. We demonstrate that a detailed calculation closer to first principles can be done under MAMMOTH between different applications on differing domains.

36 MATERIALS SCIENCE↗

MOOSE IPL Extensions (Control Logic)

In FY-2015, the development of MOOSE was driven by the needs of the NEAMS MOOSE-based applications, BISON, MARMOT, and RELAP-7. An emphasis was placed on the continued upkeep and improvement MOOSE in support of the product line integration goals. New unified documentation tools have been developed, several improvements to regression testing have been enforced and overall better software quality practices have been implemented. In addition the Multiapps and Transfers systems have seen significant refactoring and robustness improvements, as has the “Restart and Recover” system in support of Multiapp simulations. Finally, a completely new “Control Logic” system has been engineered to replace the prototype system currently in use in the RELAP-7 code. The development of this system continues and is expected to handle existing needs as well as support future enhancements.

97 MATHEMATICS AND COMPUTING↗

Memory Optimization for Phase-field Simulations

Phase-field simulations are computationally and memory intensive applications. Many of the phase-field simulations being conducted in support of NEAMS were not capable of running on “normal clusters” with 2-4GB of RAM per core, and instead required specialized “big-memory” clusters with 64GB per core. To address this issue, the MOOSE team developed a new Python-based utility called MemoryLogger, and applied it to locate, diagnose, and eradicate memory bottlenecks within the MOOSE framework. MemoryLogger allows for a better understanding of the memory usage of an application being run in parallel across a cluster. Memory usage information is captured for every individual process in a parallel job, and communicated to the head node of the cluster. Console text output from the application itself is automatically matched with this memory usage information to produce a detailed picture of memory usage over time, making it straightforward to identify the subroutines which contribute most to the application’s peak memory usage. The information produced by the MemoryLogger quickly and effectively narrows the search for memory optimizations to the most data-intensive parts of the simulation.

99 GENERAL AND MISCELLANEOUS↗