IGV Optimization for a Large Axial Flow Fan Based on MRGP Model and Sobol’ Method

Abstract

Large axial flow fans with inlet guide vanes (IGVs) have been widely used in building ventilation systems. However, it does not readily satisfy the increasing demand for energy saving, high efficiency, or noise reduction. The rotor-stator interaction between the IGVs and the impeller is particularly important for the aerodynamic performance and noise of the fans. Therefore, this article takes a large axial fan, combined with parameterization methods to optimize the IGVs. Based on numerical simulation analysis, the multiple-response Gaussian process (MRGP) approximate model was established to optimize the IGVs structure, and the Sobol´ method was employed for sensitivity analysis. The best model was selected for proofing analysis, and the experimental and numerical simulation results show that the total pressure of the optimized fan increased by 144.4 Pa and the noise decreased by 7.2 dB. These results verify that the multi-objective optimization design method combining the MRGP approximate model and the Sobol´ method demonstrates high credibility and provides a key design direction for the design optimization of large axial flow fans. This novel optimization method also has easy-to-understand parameters and the coupling relationships between parameters and responses, which has potential value for the design of other types of fluid machinery and provides new ideas for the optimization of fluid machinery.

Multi-Objective optimization of an IGV for a large axial fan based on NSGA-II

“…In addition, sensitivity analysis is rarely performed. Therefore, based on a preliminary investigation (Zhou et al, 2022), this paper optimized the complex structures of large axial fans and analyzed the responsivity of multiple influencing factors to pressure and noise by using global sensitivity analysis method and combining the research results and the internal flow characteristics of the fans to determine the structure after verifying the feasibility of numerical simulations. On this basis, Kriging model with relevant optimization variables and target response was established and optimized by NSGA-II with pressure and noise as the target.…”

Intelligent Simulation and Optimization of Axial Flow Fan Based on CFD

“…In literature 5, the IGV was optimized by parametric method. The results show that the multi-objective optimization design method combining MRGP approximate model and Sobol method has high reliability, and provides a key design direction for the optimization design of large axial flow fans [5]. Literature 6 studies that the TLV trajectory can be controlled by modifying the tip chord shape.…”