A Deep Penetration Problem Calculation Using AETIUS:An Easy Modeling Discrete Ordinates Ṯransport Code UsIng Unstructured Tetrahedral Mesh, Shared Memory Parallel

Jong Woon Kim,Young-Ouk Lee,F Malvagi,F Malouch,J Miss,C.M’B Diop,J.C Trama

doi:10.1051/epjconf/201715306025

Jong Woon Kim, Young-Ouk Lee + Show 5 more

Open Access

https://doi.org/10.1051/epjconf/201715306025

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Abstract

As computing power gets better and better, computer codes that use a deterministic method seem to be less useful than those using the Monte Carlo method. In addition, users do not like to think about space, angles, and energy discretization for deterministic codes.However, a deterministic method is still powerful in that we can obtain a solution of the flux throughout the problem, particularly as when particles can barely penetrate, such as in a deep penetration problem with small detection volumes.Recently, a new state-of-the-art discrete-ordinates code, ATTILA, was developed and has been widely used in several applications. ATTILA provides the capabilities to solve geometrically complex 3-D transport problems by using an unstructured tetrahedral mesh.Since 2009, we have been developing our own code by benchmarking ATTILA. AETIUS is a discrete ordinates code that uses an unstructured tetrahedral mesh such as ATTILA. For pre- and post- processing, Gmsh is used to generate an unstructured tetrahedral mesh by importing a CAD file (*.step) and visualizing the calculation results of AETIUS. Using a CAD tool, the geometry can be modeled very easily.In this paper, we describe a brief overview of AETIUS and provide numerical results from both AETIUS and a Monte Carlo code, MCNP5, in a deep penetration problem with small detection volumes. The results demonstrate the effectiveness and efficiency of AETIUS for such calculations.

Highlights

These days, when we develop or use a deterministic code, a lot of people ask what is its advantage compared to computer codes that use a Monte Carlo method such as MCNP5(1)
Does a deterministic method have any better features than a Monte Carlo method? This is the main motivation of this paper and why we chose a deep penetration problem with small detection volumes
We describe a brief overview of AETIUS and provide numerical results from both AETIUS and a Monte Carlo code, MCNP5, in a deep penetration problem with small detection volumes

Summary

Introduction

These days, when we develop or use a deterministic code, a lot of people ask what is its advantage compared to computer codes that use a Monte Carlo method such as MCNP5(1).The reason for its use is a continual increase in computing power and parallel processing capabilities. These days, when we develop or use a deterministic code, a lot of people ask what is its advantage compared to computer codes that use a Monte Carlo method such as MCNP5(1). Users prefer to use a method that has less approximation such as a Monte Carlo method. Even for the deterministic codes, the results of a Monte Carlo codes are used as a reference calculation for cross checking. Does a deterministic method have any better features than a Monte Carlo method? This is the main motivation of this paper and why we chose a deep penetration problem with small detection volumes. We describe a brief overview of AETIUS and provide numerical results from both AETIUS and a Monte Carlo code, MCNP5, in a deep penetration problem with small detection volumes Does a deterministic method have any better features than a Monte Carlo method? This is the main motivation of this paper and why we chose a deep penetration problem with small detection volumes.

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