Abstract:
Flow fields, including velocity and pressure fields, are typically used as references for vehicle shape design in the automotive industry to ensure steering stability and energy conservation. Generally, flow fields are calculated using computational fluid dynamics (CFD) simulations which is time-consuming and expensive. Therefore, a more efficient and interactive method is desired by designers for advanced shape discussion and design. To this end, we propose a fast estimation model using 3D convolutional neural networks. We employ a style extractor to obtain sufficient deep features of each vehicle shape and apply them using adaptive instance normalisation to improve the estimation performance. In addition, a proposed loss function which mainly includes a slice-weighted loss function is used to train the estimation model. The findings show that our proposed method outperforms previous studies, especially on flow field estimation in wake regions and regions near the vehicle surface. Therefore, the proposed method allows designing vehicle shapes while ensuring desirable aerodynamic performance within a much shorter period than extended CFD simulations.
