Macro-voxel algorithm for adaptive grid generation to accelerate grid traversal in the radiative heat transfer analysis via Monte Carlo method |
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Affiliation: | 1. Department of Mechanical Engineering, University of Bojnord, P.O. 9453155111, Bojnord, North Khorasan, Iran;2. School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran;1. State Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;2. Key Laboratory of Highway Construction and Maintenance Technology in Permafrost Regions, Ministry of Transport, CCCC First Highway Consultants Co., LTD, Xi''an 710065, China;1. Bernoulli Institute, University of Groningen, Nijenborgh 9, Groningen 9747AG, the Netherlands;2. Department of Mathematics and Computer Science, Eindhoven University of Technology, Den Dolech 2, Eindhoven 5600MB, the Netherlands;3. Department of Information and Computing Sciences Utrecht University, Princetonplein 5, CCUtrecht 3584, the Netherlands |
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Abstract: | In the thermal radiation analysis via Monte Carlo method, the ray tracing algorithm often consumes a significant fraction of CPU time. As such, an efficient grid traversal algorithm can considerably affect the performance of the Monte Carlo method. This paper presents a new grid traversal acceleration algorithm by merging adjacent small empty voxels in a preprocessing step due to the fact that larger empty space, named “macro-voxel”, allows for traversing a ray over a large distance at a smaller cost. The proposed algorithm is validated theoretically, and the results are examined for a gray box with diffuse surfaces. Timing results of the new algorithm are compared with the USD method in a typical 3D radiation furnace with concave geometry and the speedup ratio of both the macro-voxel algorithm and the USD method with respect to direct method are calculated for an optimal grid of voxels. For the considered geometry, the macro-voxel algorithm is found to be clearly superior to the USD even if the size of the problem is large and the geometry is not convex. |
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