Automotive Heating Ventilation and Air Conditioning (HVAC) systems have improved significantly in terms of cabin comfort over the past decades. In particular, with the rise of Electric Connected Autonomous Vehicles (ECAV), acoustic comfort is becoming more important than ever, with HVAC systems now a dominant source of noise. A key component to achieve required airflow performance in the HVAC system is the blower fan unit. However, improving the design is quite challenging due to complex non-linear flow and aeroacoustics mechanisms inside the unit. Therefore, traditional iterative design approaches are not systematically well adapted to the development of quieter HVAC blower fans. This paper demonstrates the ability of a cloud-based automated optimization workflow at optimizing the shape of blower fan blades. The workflow uses a transient and compressible flow solver based on Lattice Boltzmann Methods (LBM) and the blade design is parameterized using a mesh geometry-morphing feature with geometrical constraints. Total mass flow and overall acoustic power are predicted at a fixed blower speed, and are the optimization objectives of the study. A Design of Experiments (DOE) is performed to obtain a response surface model based on design parameters and objectives, and an acoustically optimized blade design was obtained while maintaining a desired airflow performance. Noise reduction between the baseline and the optimized blades design simulation is confirmed with the predictions of overall acoustic power levels and acoustic post-processing analysis.
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