Sensitivity analysis of natural convection in a porous cavity filled with nanofluid and equipped with horizontal fins using various optimization methods and MRT-LB

H Sajjadi; N Mansouri; S N Nabavi; A Amiri Delouei; M Atashafrooz

doi:10.1038/s41598-024-60330-0

Sensitivity analysis of natural convection in a porous cavity filled with nanofluid and equipped with horizontal fins using various optimization methods and MRT-LB

Sci Rep. 2024 Apr 29;14(1):9847. doi: 10.1038/s41598-024-60330-0.

Authors

H Sajjadi¹, N Mansouri², S N Nabavi³, A Amiri Delouei³, M Atashafrooz⁴

Affiliations

¹ Department of Mechanical Engineering, University of Bojnord, Bojnord, Iran. hsajjadi@clarkson.edu.
² Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran. mansouri_nematollah@alumni.iust.ac.ir.
³ Department of Mechanical Engineering, University of Bojnord, Bojnord, Iran.
⁴ Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran.

Abstract

In the present study, natural convection heat transfer is investigated in a porous cavity filled with Cu/water nanofluid and equipped with horizontal fins. Optimization and sensitivity analysis of the fin's geometry, porous medium and nanofluid properties to maximize heat transfer rate is the aim of this work. To achieve this purpose, a design space is created by input parameters which include length, number of fins, distance between fins, porosity, Darcy number and volumetric fraction of the nanoparticles. Several tools have been used to implement optimization methods including the Taguchi method (TM) for design points generation, sensitivity analysis of design variables by using signal-to-noise ratio (SNR) and analysis of variance (ANOVA), response surface method (RSM) for interpolation and regression by using nonparametric regression, and genetic algorithm (GA) for finding optimum design point. The double multi-relaxation time lattice Boltzmann method (MRT-LBM) is used to analyze and simulate the flow field and heat transfer in each design point. The results show that the optimal configuration leads to an average Nusselt number of 5.56. This optimal configuration is at the length of fins L/2, the number of fins 2, the distance between fins L/12, porosity 0.8, Darcy number 0.1, and the volumetric fraction of the nanoparticles 0.02. By using the SNR results, the Darcy number and the number of fins have the most and the least effect in maximizing the average Nusselt number, respectively. The ANOVA results and global sensitivity analysis (GSA) findings further validated this conclusion.

Keywords: Genetic algorithm; Multi-relaxation time lattice Boltzmann method; Nanofluid; Natural convection; Porous media; Response surface method; Taguchi method.