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A Combinatorial Approach of the Differential Evolution and Wingsuit Flying Search to Optimize the Free Convection in an Enclosure with Interior Perforated Louvers

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Abstract

The free convection in a divided enclosure is an ongoing attractive research topic due to its occurrence in various thermal applications, which can be utilized as a dual-purpose application to suppress or enhance the heat transfer rate. In this investigation, a vertical enclosure divided by adiabatic louvers is geometrically modified. To this end, circular perforations are made along the louvers. Then, the steady laminar free convection in the modified divided enclosure is experimentally investigated versus design parameters. The design parameters are the Rayleigh number (7 × 103 ≤ Ra ≤ 1.45 × 104), louver’s slant angle (0° ≤ φ ≤ 150°), perforation’s diameter to louver’s width ratio (0.2 ≤ d/w ≤ 0.6) as well as enclosure’s aspect ratio (7.8723 ≤ Ar ≤ 9.3671). After that, a mathematical correlation is extracted for the mean Nusselt number (Num) versus the design parameters. In the following, the differential evolution (DE) algorithm and wingsuit flying search (WFS) optimization algorithm are combined in the frame of a novel combinatorial optimization algorithm. The combinatorial algorithm is then employed to optimize the obtained mathematical correlation. It was reported that the maximum heat transfer rate corresponds to the highest level of the Rayleigh number, d/w ratio as well as Ar. Moreover, for the minimum heat transfer rate, the Rayleigh number, d/w ratio as well as Ar, must be at the lowest level. The maximum and minimum heat transfer rates both occur at the same critical value of the louver’s angle (φcrit ≈ 90°).

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Abbreviations

\(A\) :

Aluminum’s surface area (m2)

ACO:

Ant colony optimization

ANN:

Artificial neural network

Ar:

Enclosure’s aspect ratio = H/W

\(A^{\prime}\) :

Area of the material mounted at the back of the temperature source (m2)

C p :

Specific heat capacity (J/kg K)

d :

Perforation’s diameter (mm)

DE:

Differential evolution

DFE:

Degree of freedom

FA–ACO:

Firefly algorithm–ant colony optimization

FA–DE:

Firefly algorithm–differential evolution

FIS:

Fuzzy inference system

H :

Enclosure’s height (mm)

h m :

Mean heat transfer coefficient (W/m2 K)

\(k\) :

Thermal conductivity (W/m K)

L :

Enclosure’s length or louver’s length (mm)

MRE:

Mean relative error

MSCA-TLBO:

Modified Sine_Cosine Algorithm-teaching_learning-based optimization

Num :

Mean Nusselt number

Q conv :

Mean convection heat transfer (W)

Q source :

Electrical heat input (W)

P :

Louvers' pitch (mm)

PSO:

Particle swarm optimization

R 2 :

Correlation determination

Ra:

Rayleigh number based on the enclosure’s width, \(= \frac{{g\beta (T_{{{\text{HW}}}} - T_{{{\text{CW}}}} )W^{3} }}{v\alpha }\)

RMSE:

Root-mean-square error

SA:

Simulated annealing

SSE:

Sum-squared error

t :

Louver’s thickness (mm)

T :

Temperature (K)

STD:

Standard deviation

w :

Louver’s width (mm)

W :

Enclosure’s width (mm)

WFS:

Wingsuit flying search

WFSSE:

Wingsuit flying search-spherical evolution

WOA:

Whale optimization algorithm

x :

Direction along the enclosure’s width

y :

Direction along the enclosure’s height

z :

Direction along the enclosure’s length

φ :

Louver’s slant angel (°)

ρ :

Density (kg/m3)

η :

Thermal performance

b:

Refers to the backward direction

C:

Refers to the cold temperature

d:

Refers to the downward direction

f:

Refers to the fluid

H:

Refers to the hot temperature

l:

Refers to the left-side direction

r:

Refers to the right-side direction

ref:

Refers to the reference condition

u:

Refers to the upward direction

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  1. Mechanical Engineering Department, Razi University, Kermanshah, Iran

    Alimohammad Karami & Farzad Veysi

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Karami, A., Veysi, F. A Combinatorial Approach of the Differential Evolution and Wingsuit Flying Search to Optimize the Free Convection in an Enclosure with Interior Perforated Louvers. Arab J Sci Eng 48, 3157–3180 (2023). https://doi.org/10.1007/s13369-022-07105-9

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