Research Papers: Flows in Complex Systems

Axial-Flow Ventilation Fan Design Through Multi-Objective Optimization to Enhance Aerodynamic Performance

[+] Author and Article Information
Jin-Hyuk Kim, Jae-Woo Kim

Department of Mechanical Engineering,  Inha University, 253 Yonghyun-Dong, Nam-Gu, Incheon 402-751, Rep. of Korea

Kwang-Yong Kim1

Department of Mechanical Engineering,  Inha University, 253 Yonghyun-Dong, Nam-Gu, Incheon 402-751, Rep. of Koreakykim@inha.ac.kr


Corresponding author.

J. Fluids Eng 133(10), 101101 (Oct 04, 2011) (12 pages) doi:10.1115/1.4004906 History: Received November 22, 2010; Accepted August 11, 2011; Published October 04, 2011; Online October 04, 2011

This paper presents an optimization procedure for axial-flow ventilation fan design through a hybrid multiobjective evolutionary algorithm (MOEA) coupled with a response surface approximation (RSA) surrogate model. Numerical analysis of a preliminary fan design is conducted by solving three-dimensional (3-D) Reynolds-averaged Navier-Stokes (RANS) equations with the shear stress transport (SST) turbulence model. The multiobjective optimization processes are performed twice to understand the coupled effects of diverse variables. The first multiobjective optimization process is conducted with three design variables defining stagger angles at the hub, mid-span, and tip, and the second is conducted with five design variables defining hub-to-tip ratio, hub cap installation distance, hub cap ratio, and the stagger angles at the mid-span and tip. Two aerodynamic performance parameters, the total efficiency and total pressure rise, are selected as the objective functions for optimization. These objective functions are numerically assessed through 3-D RANS analysis at design points sampled by Latin hypercube sampling in the design space. The optimization yields a maximum increase in efficiency of 1.8% and a 31.4% improvement in the pressure rise. The off-design performance is also improved in most of the optimum designs except in the region of low flow rate.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 1

Sections of blade profiles from hub to tip

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Figure 2

Three-dimensional geometry and computational domain of axial-flow fan

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Figure 3

Results of grid-dependency test

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Figure 4

Validation of flow analysis

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Figure 5

Comparison of the results of RANS analysis and the preliminary design: (a) Diffusion factor along the spanwise direction (preliminary design), (b) Separation region near trailing edge of hub (RANS analysis)

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Figure 6

Definition of design variables: (a) Definition of the stagger angle, (b) α angle distribution according to a Bezier curve, (c) Blade generation by interpolation with B-spline curve, (d) Schematic diagram defining the hub-to-tip ratio, (e) Meridional view defining the hub cap

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Figure 7

Results of sensitivity tests: (a) Stagger angle at hub, (b) Stagger angle at mid-span, (c) Stagger angle at tip, (d) Hub-to-tip ratio, (e) Hub cap installation distance, (f) Hub cap ratio

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Figure 8

Multiobjective optimization methodology

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Figure 9

Results of the first and second MOPTs

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Figure 10

Performance curves of clustered optimal solutions in the first and second MOPTs: (a) Efficiency curves, (b) Pressure rise curves

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Figure 11

Axial velocity contours at leading edge (unit: m/s): (a) Reference, (b) COS A, (c) COS I

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Figure 12

Axial velocity contours at trailing edge (unit: m/s): (a) Reference, (b) COS A, (c) COS I

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Figure 13

Static pressure distributions on blade surfaces: (a) At 15% span, (b) At 50% span, (c) At 85% span

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Figure 14

Static and total pressure distributions at trailing edge: (a) Static pressure, (b) Total pressure

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Figure 15

Static pressure contours on the passage surface (unit: Pa): (a) At 15% span, (b) At 50% span, (c) At 85% span




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