Development of the random ray method of neutral particle transport for high-fidelity nuclear reactor simulation

Abstract

A central goal in computational nuclear engineering is the high-fidelity simulation of a full nuclear reactor core by way of a general simulation method. General full core simulations can potentially reduce design and construction costs, increase reactor performance and safety, reduce the amount of nuclear waste generated, and allow for much more complex and novel designs. To date, however, the time to solution and memory requirements for a general full core high fidelity 3D simulation have rendered such calculations impractical, even using leadership class supercomputers. Reactor designers have instead relied on calibrated methods that are accurate only within a narrow design space, greatly limiting the exploration of innovative concepts. One numerical simulation approach, the Method of Characteristics (MOC), has the potential for fast and efficient performance on a variety of next generation computing systems, including CPU, GPU, and Intel Xeon Phi architectures. While 2D MOC has long been used in reactor design and engineering as an efficient simulation method for smaller problems, the transition to 3D has only begun recently, and to our knowledge no 3D MOC based codes are currently used in industry. The delay of the onset of full 3D MOC codes can be attributed to the impossibility of "naively" scaling current 2D codes into 3D due to prohibitively high memory requirements. To facilitate transition of MOC based methods to 3D, we have developed a fundamentally new computational algorithm. This new algorithm, known as The Random Ray Method (TRRM), can be viewed as a hybrid between the Monte Carlo (MC) and MOC methods. Its three largest advantages compared to MOC are that it can handle arbitrary 3D geometries, it offers extreme improvements in memory efficiency, and it allows for significant reductions in algorithmic complexity on some simulation problems. It also offers a much lower time to solution as compared to MC methods. In this thesis, we will introduce the TRRM algorithm and a parallel implementation of it known as the Advanced Random Ray Code (ARRC). Then, we will evaluate its capabilities using a series of benchmark problems and compare the results to traditional deterministic MOC methods. A full core simulation will be run to assess the performance characteristics of the algorithm at massive scale. We will also discuss the various methods to parallelize the algorithm, including domain decomposition, and will investigate the new method's scaling characteristics on two current supercomputers, the IBM Blue Gene/Q Mira and the Cray XC40 Theta. The results of these studies show that TRRM is capable of breakthrough performance and accuracy gains compared to existing methods which we demonstrate to enable general, full core 3D high-fidelity simulations that were previously out of reach.