An optimized and accurate Monte Carlo method to simulate 3D complex radiative enclosures

Naeimi, Hooman; Kowsary, Farshad

doi:10.1016/j.icheatmasstransfer.2017.04.002

Cited by 11 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Monte Carlo (MC) statistical approach is a powerful tool that is being widely used by researchers to calculate VF of radiative heat exchanges in complex geometries. [ 13,14 ] This technique works in the following manner: an emitting surface A 1 in an enclosure with m surfaces is set to emit an arbitrary number of energy packages (bundles) N . Each bundle is emitted from a random position in the surface A 1 in a random 3D direction.…”

Section: Calculation Of Vfmentioning

confidence: 99%

Calculation of View Factors in Electric Arc Furnace Process Modeling

Hay

Hernández

Roberts

et al. 2020

steel research int.

View full text Add to dashboard Cite

The determination of view factors (VF) is a central component of the heat transfer model in comprehensive electric arc furnace process models. Due to the complex nature of the process, simplifying assumptions are necessary to allow the efficient estimation of VF. These simplifications mainly deal with the geometrical representation of the furnace and the methods to calculate the resulting VF. Such assumptions have been applied in several studies; however, while the results of the complete process models have been validated extensively, the calculated VF have not been examined in detail. Herein, two methods of calculating VF based on a geometry similar to that used in the process models proposed by MacRosty et al., Logar et al., and Meier et al. are presented and validated by comparing the results obtained using a commercial computational fluid dynamics software. The number of assumptions and simplifications is reduced when compared with previously published results and both methods are proven to be accurate while remaining computationally efficient enough for the application in process models for online applications and process optimization, where fast execution is necessary.

show abstract

Section: Calculation Of Vfmentioning

confidence: 99%

Calculation of View Factors in Electric Arc Furnace Process Modeling

Hay

Hernández

Roberts

et al. 2020

steel research int.

View full text Add to dashboard Cite

show abstract

“…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.

Self Cite

View full text Add to dashboard Cite

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%.

show abstract