Macro-voxel algorithm for adaptive grid generation to accelerate grid traversal in the radiative heat transfer analysis via Monte Carlo method

Naeimi, Hooman; Kowsary, Farshad

doi:10.1016/j.icheatmasstransfer.2017.06.020

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…During the early days of the Monte Carlo method, it was a time-consuming approach to produce accurate results compared to other numerical methods. The past few years have seen tremendous technological developments of processing speed, product price and memory capacity, and many researches have been conducted on the optimization of the Monte Carlo method which evolved the Monte Carlo method from a highly expensive tool to a more cost-effective algorithm for most science and engineering applications [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Finite Volume Monte Carlo (FVMC) method for the analysis of conduction heat transfer

Naeimi

Kowsary

2019

J Braz. Soc. Mech. Sci. Eng.

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The numerical solution of the heat equation is a particularly challenging subject in complex, practical applications such as functionally graded materials for which analytical solution is not available or hardly attainable. The Monte Carlo method is a powerful technique with some advantages compared to the conventional methods and is often used when all else fail. In this paper, we introduce the Finite Volume Monte Carlo (FVMC) method for solving 3D steady-state heat equation where, instead of using the usual finite difference scheme for discretization of the heat equation, the finite volume scheme is used. The FVMC method is tested for three problems to assess the robustness of the method, first one in a simple geometry for validation and evaluation of the predictive performance, the second one in a complex geometry with unstructured mesh and the last one in a problem with a variable heat source and different kinds of boundary conditions. Comparisons were made to the analytical solution in the first test case, whereas for the remaining test cases, the CFD methods were utilized in the absence of the analytical solutions. It was observed that the FVMC temperature distribution agrees perfectly with analytical and CFD solutions in all problems. Despite expecting computational accuracy to improve by increasing total number of particles in the FVMC method, a very good accuracy was obtained for all considered problems after a small number of walks, and the calculated relative root-mean-square errors were below 1%.

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Section: Introductionmentioning

confidence: 99%

Finite Volume Monte Carlo (FVMC) method for the analysis of conduction heat transfer

Naeimi

Kowsary

2019

J Braz. Soc. Mech. Sci. Eng.

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“…This method relies on a proper subdivision tree and an optimal choice of the underlying parameters, which need to be determined beforehand. Naeimi et al accelerate their ray-tracing based heat transfer model by merging empty voxels, explicitly representing their geometry, inside their static simulation domain [ 20 ]. Hence, when a ray is traversing through the domain, it can quickly traverse the empty voxels, since they are much bigger than those on the respective surface.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Flux Calculations Using Sparse Sampling

et al. 2018

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The ongoing miniaturization in electronics poses various challenges in the designing of modern devices and also in the development and optimization of the corresponding fabrication processes. Computer simulations offer a cost- and time-saving possibility to investigate and optimize these fabrication processes. However, modern device designs require complex three-dimensional shapes, which significantly increases the computational complexity. For instance, in high-resolution topography simulations of etching and deposition, the evaluation of the particle flux on the substrate surface has to be re-evaluated in each timestep. This re-evaluation dominates the overall runtime of a simulation. To overcome this bottleneck, we introduce a method to enhance the performance of the re-evaluation step by calculating the particle flux only on a subset of the surface elements. This subset is selected using an advanced multi-material iterative partitioning scheme, taking local flux differences as well as geometrical variations into account. We show the applicability of our approach using an etching simulation of a dielectric layer embedded in a multi-material stack. We obtain speedups ranging from 1.8 to 8.0, with surface deviations being below two grid cells (0.6–3% of the size of the etched feature) for all tested configurations, both underlining the feasibility of our approach.

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Impacts of scattering of non-gray gas particulate mixtures on radiative heat transfer and mid-infrared image temperature in a 2D oxy-coal combustion furnace

Zheng,

Na,

et al. 2024

J Therm Anal Calorim

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Macro-voxel algorithm for adaptive grid generation to accelerate grid traversal in the radiative heat transfer analysis via Monte Carlo method

Cited by 4 publications

References 19 publications

Finite Volume Monte Carlo (FVMC) method for the analysis of conduction heat transfer

Finite Volume Monte Carlo (FVMC) method for the analysis of conduction heat transfer

Accelerating Flux Calculations Using Sparse Sampling

Impacts of scattering of non-gray gas particulate mixtures on radiative heat transfer and mid-infrared image temperature in a 2D oxy-coal combustion furnace

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