The cooling system of modern automobiles is the subject of intense reflections to maximize efficiency and reduce the energy consumption. Large fan design diameters are preferred to enhance thermal exchanges over the large surface of the radiator, whereas high rotational speeds are seeking to benefit from higher efficiency and low weight of the electrical motor that drive the fan. This leads to reconsider the design of the blades for these conditions for which stagger angles are very high and the aerodynamic load very low. These unconventional geometries are so far little known, but represent an interesting potential in terms of design of the cooling system, which could benefit from a high permeability favorable to the heat transfer when the vehicle is moving, even at low speed.
The aim of this paper is to apply 3D inverse design method coupled with multi-objective/multi-point automatic optimization method to design a low loaded axial fan. In this approach Design of Experiment Method (DoE) is used to create a Response Surface (RSM) relating the various performance parameters to inverse design base design parameters. Multi-objective Genetic Algorithm (MOGA) is then run on the response surface to find the trade-offs between design parameters and constraints. All designs are evaluated by using unsteady 3D CFD simulation to create the Response Surface. An estimate of aeroacoustics far field noise is obtained by using an acoustic FW&H model. Some experimental results from a prototyped version are also presented to evaluate the accuracy of numerical simulations.
Two designs are selected by the optimization process with different compromises in term of multi-objectives performances, and their final qualities are assessed by a thermal simulation on a cooling module. These proposed new geometries represent a proof of concept that is analyzed for performance evaluation before further developments for the automotive application.