Shape optimization of axial cooling fan via 3D CFD simulation and surrogate modeling

Abstract: Due to legislative reasons and environmental concerns the automotive and transport sector are shifting their focus from traditional internal combustion engine (ICE) vehicles to development of battery electric vehicles (BEVs). This brings new challanges to design of cooling systems where axial fans are one of the key components. Axial fans are usually designed with regards to a certain operating condition and outside this region the efficiency of the fan drops drastically. Due to difficulty in specifying the exact operational parameters when placed in a car, post-design optimization may be necessary to ensure maximized performance. This thesis focuses on fan blade shape optimization through mesh morphing using the surrogate based optimization algorithm called Efficient Global Optimization (EGO). The target fans was a 9 bladed prototype fan by Johnson Electric with uneven blade spacing. The optimization uses steady state Reynolds-averaged Navier-Stokes (RANS) simulations to evaluate the fan designs and a Bezier curve parametrization in order to change the fan blade shape together with mesh morphing. The simulation setup was evaluated before peceding with the optimization, and showed good agreement close to intended operational conditions. Differences in turbulence modeling treatments were also evaluated in order to have a satisfactory agreement with measurement data. The EGO algorithm manages to provide fan designs with higher total-to-static efficiency at several different operational conditions. Evaluation of the optimized fan designs was limited to comparison with the provided measurement data and corrensponding simulations. Acoustic evaluation of selected fan designs is also attemped, but further work is required in order for the study to result in a quantitative comparison.