Abstract
This work presents an optimization proposal to better the computational convergence time in convection-diffusion and driven-cavity problems by applying a simulated annealing (SA) metaheuristic, obtaining optimal values in relaxation factors (RF) that optimize the problem convergence during its numerical execution. These relaxation factors are tested in numerical models to accelerate their computational convergence in a shorter time. The experimental results show that the relaxation factors obtained by the SA algorithm improve the computational time of the problem convergence regardless of user experience in the initial low-quality RF proposal.
Highlights
Computational Fluid Dynamics (CFD) is a powerful tool for analyzing and understanding various physical phenomena occurring in nature and in industrial processes, which are objects of study in various fields of research
The results shown are obtained based on the relaxation factors optimized by the simulated annealing (SA) algorithm
It is concluded that the proposed methodology using the SA algorithm is an effective alternative to find the optimized relaxation factors helping to reduce the computational convergence time of the two studied problems, the Convection-Diffusion and the DrivenCavity problems, since each of the 30 executions of SA present excellent results for each one
Summary
Computational Fluid Dynamics (CFD) is a powerful tool for analyzing and understanding various physical phenomena occurring in nature and in industrial processes, which are objects of study in various fields of research. For the development of computational simulations in mechanical engineering, it is necessary to solve a set of partial differential equations (PDE) using numerical techniques such as finite-difference, finite-element and finite-volume methods These methods convert a PDE to an algebraic equation system in a discrete domain and find an approximate solution to the original problem. To simulate fluid-flow phenomena, or heat and mass transfer issues, they are first modeled as nonlinear problems and solved using iterative processes [1]. These processes commonly require high computational resources using robust convergence criteria, allowing them to find a solution that satisfies the problem conditions. Under-relaxation is very useful to solve nonlinear problems since it avoids iterative process divergence
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