Mathematics (Mar 2021)

Metaheuristic to Optimize Computational Convergence in Convection-Diffusion and Driven-Cavity Problems

  • Juana Enríquez-Urbano,
  • Marco Antonio Cruz-Chávez,
  • Rafael Rivera-López,
  • Martín H. Cruz-Rosales,
  • Yainier Labrada-Nueva,
  • Marta Lilia Eraña-Díaz

DOI
https://doi.org/10.3390/math9070748
Journal volume & issue
Vol. 9, no. 7
p. 748

Abstract

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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.

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