AIML Enabled Rapid Vehicle Aerodynamics Design Improvements

<div class="section abstract"><div class="htmlview paragraph">The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has significantly changed various industries. This study demonstrates the application of a Convolutional Neural Network (CNN) model in Computational Fluid Dynamics (CFD) to predict the drag coefficient of a complete vehicle profile. We have developed a design advisor that uses a custom 3D CNN with a U-net architecture in the DEP MeshWorks environment to predict drag coefficients (Cd) based on car shapes. This model understands the relationship between car shapes and air drag coefficients calculated using computational fluid dynamics (CFD).</div><div class="htmlview paragraph">The AI/ML-based design advisor feature has the potential to significantly decrease the time required for predicting drag coefficients by conducting CFD calculations. During the initial development phase, it will serve as an efficient tool for analyzing the correlation between multiple design proposals and aerodynamic drag forces within a short time frame. Additionally, the interactive AI/ML tool helps to streamline the creation of necessary shape parameters with minimal input and helps optimize the design of the vehicle to achieve the desired coefficient of drag values.</div><div class="htmlview paragraph">In summary, the implementation of MeshWorks AI/ML-based design advisor system will aid analysis engineers in assessing the vehicle's coefficient of drag values, while also enabling parametrization and optimization of the design. This advancement has the potential to significantly reduce processing time and alleviate the inherent procedural complexities associated with the traditional methods.</div></div>