Performance Enhancement of the Micromixer by the Multiobjective Genetic Algorithm and Surrogate Model Based on a Navier–Stokes Analysis Using Trade-Off Objective Functions

Shakhawat Hossain, Farzana Islam, Nass Toufik Tayeb, Muhammad Aslam and Jin-Hyuk Kim

Mathematical Problems in Engineering, 2021, vol. 2021, 1-10

Abstract:

Optimal structure of the micromixer with a two-layer serpentine crossing device was accomplished by a multiobjective genetic algorithm and surrogate modeling based on a Navier–Stokes analysis using the trade-off objective functions behavior. The optimization analysis was conducted with three design parameters, i.e., channel width to the pitch span ( ) ratio, major channel width to the pitch span ( H / P ) ratio, and channel depth to the pitch span ( d / P ) ratio. Two objective functions (i.e., mixing index and pressure drop) with trade-off characteristics have been used to solve the multiobjective optimization problem. The design domain was predetermined by a parametric investigation; afterward, the Latin hypercube sampling method was employed to select the appropriate design points surrounded by the design domain. The numerical data of the thirty-two design points were used to create the surrogate model; among the different surrogate models, in this study, the Kriging metamodel has been used. The concave pareto-optimal curve signifies the trade-off characteristics linking the objective functions.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:9924849

DOI: 10.1155/2021/9924849

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