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

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...

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Bibliographic Details
Published in:Mathematical problems in engineering Vol. 2021; pp. 1 - 10
Main Authors: Hossain, Shakhawat, Islam, Farzana, Tayeb, Nass Toufik, Aslam, Muhammad, Kim, Jin-Hyuk
Format: Journal Article
Language:English
Published: New York Hindawi 03.08.2021
John Wiley & Sons, Inc
Subjects:
Computational fluid dynamics
Design
Design optimization
Design parameters
Domains
Fluid flow
Fluids
Genetic algorithms
Hypercubes
Investigations
Latin hypercube sampling
Mathematical problems
Metamodels
Multiple objective analysis
Navier-Stokes equations
Numerical analysis
Optimization techniques
Pareto optimization
Pressure drop
Reynolds number
Serpentine
Tradeoffs
Viscosity
ISSN:1024-123X, 1563-5147
Online Access:Get full text
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Summary: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 (w/P) 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.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2021/9924849