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

Abstract

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.

Small-scale turbocompressors for wide-range operation with large tip-clearances for a two-stage heat pump concept

“…An appropriate 3D impeller blade design suppressing the secondary zone is, therefore, very important. Modern turbomachinery design methods utilize complex black-box optimization algorithms supplemented with fast metamodels [46,47,48,49]. However, such approach also requires a considerable effort to build the appropriate metamodels that can incorporate a suitable number of design variables.…”

Volume Flow Rate Optimization of an Axial Fan by Artificial Neural Network and Genetic Algorithm

“…Optimization of the turbomachinery has been made much progress with the aid of CFD techniques in the past decades and has found wide applications in practice. Kim et al [15] optimized the hub-to-tip ratio, hub cap installation distance, hub cap ratio, and the stagger angles at the mid-span and tip of an axial fan through a hybrid multi-objective evolutionary algorithm (MOEA) coupled with a response surface approximation (RSA) surrogate model. Kim et al [16] improved the total efficiency of an axial fan through optimizing six variables defining the blade lean angle and the blade profiles using the Non-dominated Sorting of Genetic Algorithm (NSGA-II) coupled with the response surface approximation (RSA) surrogate model.…”

A Computational Study on the Performance Improvement of Low-Speed Axial Flow Fans with Microplates

“…Their numerical results are in generally good agreement with the experiment data. Kim et al (2011Kim et al ( , 2014 conducted optimizations for axial flow fans to enhance their aerodynamic performance, based on the Reynolds-averaged Navier-Stokes (RANS) method. The CFD simulations were performed using the commercial software ANSYS CFX and shear stress transport (SST) turbulence model (Menter 1994).…”