Application of 3D Inverse Design Based Multi-Objective Optimization of Axial Cooling Fan with Large Tip Gap

In many automotive highway/off-highway engine cooling applications the fan has to provide a fairly large pressure rise and operate with a large gap between the tip of the blade and the shroud surface (tip clearance). This can pose difficult design challenges.

This paper presents a design process coupling 3D inverse design with a Multi Objective Genetic Algorithm (MOGA) for an axial cooling fan. The aim is to reduce the leakage loss and profile losses to improve performance. The inverse design method parameterizes the 3D shape of the axial fan with a reduced number of design parameters allowing a larger exploration of the design space in the optimization process. The methodology is applied to the design of a highway truck engine cooling fan with a tip gap of 8% of blade height. Two designs from the optimization are analyzed in detail using 3D Computational Fluid Dynamic (CFD) simulations, confirming that the design optimized for minimizing leakage losses meets the design specification. Detailed CFD simulation also confirms the reduction in leakage losses in this design.