TOWARDS CAD-BASED GEOMETRY MODELLING WITH THE RANDOM RAY METHOD

The Advanced Random Ray Code (ARRC) is a high performance computing application capable of high-fidelity simulations of full core nuclear reactor models. ARRC leverages a recently developed stochastic method for neutron transport, known as The Random Ray Method (TRRM), which offers a variety of computational and numerical advantages as compared to existing methods. In particular, TRRM has been shown to be capable of efficient simulation of explicit three dimensional geometry representations without assumptions about axial homogeneity. To date, ARRC has utilized Constructive Solid Geometry (CSG) combined with a nested lattice geometry which works well for typical pressurized water reactors, but is not performant for the general case featuring arbitrary geometries.

To facilitate simulation of arbitrarily complex geometries in ARRC efficiently, we propose performing transport directly on Computer-Aided Design (CAD) models of the geometry. In this study, we utilize the Direct-Accelerated Geometry Monte Carlo (DAGMC) toolkit which tracks particles on tessellated CAD geometries using a bounding volume hierarchy to accelerate the process, as a replacement for ARRC’s current lattice-based accelerations. Additionally, we present a method for automatically subdividing the large CAD regions in the DAGMC model into smaller mesh cells required by random ray to achieve high accuracy. We test the new DAGMC geometry implementation in ARRC on several test problems, including a 3D pincells, 3D assemblies, and an axial section of the Advanced Test Reactor. We show that DAGMC allows for simulation of complex geometries in ARRC that would otherwise not be possible using the traditional approach while maintaining solution accuracy.

DESIGN OF A CODE-AGNOSTIC DRIVER APPLICATION FOR HIGH-FIDELITY COUPLED NEUTRONICS AND THERMAL-HYDRAULIC SIMULATIONS

While the literature has numerous examples of Monte Carlo and computational fluid dynamics (CFD) coupling, most are hard-wired codes intended primarily for research rather than as standalone, general-purpose codes. In this work, we describe an open source application, ENRICO, that allows coupled neutronic and thermal-hydraulic simulations between multiple codes that can be chosen at runtime (as opposed to a coupling between two specific codes). In particular, we outline the class hierarchy in ENRICO and show how it enables a clean separation between the logic and data required for a coupled simulation (which is agnostic to the individual solvers used) from the logic/data required for individual physics solvers. ENRICO also allows coupling between high-order (and generally computationally expensive) solvers to low-order “surrogate” solvers; for example, Nek5000 can be swapped out with a subchannel solver.

ENRICO has been designed for use on distributed-memory computing environments. The transfer of solution fields between solvers is performed in memory rather than through file I/O.We describe the process topology among the different solvers and how it is leveraged to carry out solution transfers. We present results for a coupled simulation of a single light-water reactor fuel assembly using Monte Carlo neutron transport and CFD.

A COMPARISON OF STOCHASTIC MESH CELL VOLUME COMPUTATION STRATEGIES FOR THE RANDOM RAY METHOD OF NEUTRAL PARTICLE TRANSPORT

The random ray method is a recently developed neutron transport method that can be used to perform efficient full-core, general-purpose, high-fidelity 3D simulations of nuclear reactors. While Tramm et al. have so far documented the new random ray algorithm in several publications, one critical detail has not yet been published: how to best determine the volume of each source region (or cell) of the simulation. As the “true” analytical constructive solid geometry cell volumes are typically not known a priori they must be computed by the application at runtime, which is not straightforward in TRRM as different rays are used each power iteration such that the sampled volume of each cell also changes between iterations. In the present study, we analyze two different on-the-fly stochastic methods for computing the cell volumes and quantify their impacts on the accuracy of scalar flux estimates. We find that the “na¨ıve” stochastic volume estimator (which arises naturally from the derivation of the Method of Characteristics), is highly biased and can result in over 1,000 pcm error in eigenvalue. Conversely, we find that the “simulation averaged” estimator is unbiased and is therefore equivalent to the use of analytical cell volumes even when using a coarse ray density. Thus, the new simulation averaged method is a critical (and as yet undocumented) component of the TRRM algorithm, and is therefore vital information for those in the reactor physics community working to implement random ray solvers of their own.