Technical Paper
Machine Learning Approach to Predict Aerodynamic Performance of Underhood and Underbody Drag Enablers
2020-04-14
2020-01-0684
Implementing stringent emission norms and fuel economy requirement in the coming decade will be very challenging to the whole automotive industry. Aerodynamic losses contribute up to 13% to 22 % of overall fuel economy and aerodynamicists will be challenged to have optimum content on the vehicle to reduce this loss. Improving Aerodynamic performance of ground vehicles has already reached its peak and the industry is moving towards active mechanisms to improve performance. Calibrating or simulating these active mechanisms in the wind tunnel or in Computational Fluid Dynamics (CFD) would be very challenging as the model complexity increases. Computationally expensive CFD models are required to predict the transient behaviors of model complexity.