Application of multi-objective optimization techniques to improve the aerodynamic performance of a tunnel ventilation jet fan

This paper describes the design optimization of a tunnel ventilation jet fan through multi-objective optimization techniques. Four design variables were selected for design optimization. To analyze the performance of the fan, numerical analyses were conducted, and three-dimensional Reynolds-averaged...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science Journal of mechanical engineering science, 2015-01, Vol.229 (1), p.91-105
Main Authors: Kim, Joon-Hyung, Kim, Jin-Hyuk, Yoon, Joon-Yong, Choi, Young-Seok, Yang, Sang-Ho
Format: Article
Language:English
Subjects:
Aerodynamics
Design engineering
Design optimization
Mathematical analysis
Mathematical models
Navier-Stokes equations
Optimization
Optimization techniques
Outlets
Reynolds equation
Tunnels (transportation)
Turbulence models
Ventilators
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Summary:This paper describes the design optimization of a tunnel ventilation jet fan through multi-objective optimization techniques. Four design variables were selected for design optimization. To analyze the performance of the fan, numerical analyses were conducted, and three-dimensional Reynolds-averaged Navier–Stokes equations with a shear stress transport turbulence model were solved. Two objective functions, the total efficiency of the forward direction and the ratio of the reverse direction outlet velocity to the forward direction outlet velocity, were employed, and multi-objective optimization was carried out to improve the aerodynamic performance. A response surface approximation surrogate model was constructed for each objective function based on numerical solutions obtained at specified design points. The non-dominated sorting genetic algorithm with a local search procedure was used for multi-objective optimization. The tradeoff between the two objectives was determined and described with respect to the Pareto-optimal solutions. Based on the analysis of the optimization results, we propose an optimization model to satisfy the objective function. Finally, to verify the performance, experiments with the base model and the optimization model were carried out.
ISSN:0954-4062
2041-2983
DOI:10.1177/0954406214531566