Analyzing the relationship between wake flow patterns and design element changes of automobile using machine learning

During the automotive vehicle design process, it is crucial to identify the design elements that influence wake characteristics to improve the vehicle's aerodynamic performance. Traditionally, this analysis involved comparing the wake flow of a reference vehicle shape with that of a new design. However, when multiple design changes are made simultaneously, it can be challenging to assess their individual impact on the wake flow. To overcome this challenge, we have developed artificial intelligence models to accurately detect the design element changes that affect the wake flow. Specifically, we trained a ResNet18 model using two different approaches. The first approach is a multi-label classification model that identifies which design elements have been changed, supported by grad-CAM visualization for better interpretability. The second approach is a multi-target regression model that quantifies the magnitude of the design parameter changes. In our study, we utilized the cosine similarity of gradients of the main flow (Ux) or vorticity fields at a plane perpendicular to the main flow in the wake region as the training data format. The results showed that both models achieved effective detection of design elements and their respective impact on the wake flow.