The aerodynamic optimization process of cars requires multiple iterations between aerodynamicists and stylists. Response surface modeling and reduced-order modeling (ROM) are commonly used to eliminate the overhead due to computational fluid dynamics (CFD), leading to faster iterations. However, a primary drawback of these models is that they can work only on the parameterized geometric features they were trained with. This study evaluates if deep learning models can predict the drag coefficient (cd ) for an arbitrary input geometry without explicit parameterization. We use two similar data sets (total of 1000 simulations) based on the publicly available DrivAer geometry for training. We use a modified U-Net architecture that uses signed distance fields (SDF) to represent the input geometries. Our models outperform the existing models by at least 11% in prediction accuracy for the drag coefficient. We achieved this improvement by combining multiple data sets that were created using different parameterizations, which is not possible with the methods currently used. We have also shown that it is possible to predict velocity fields and drag coefficient concurrently and that simple data augmentation methods can improve the results. In addition, we have performed an occlusion sensitivity study on our models to understand what information is used to make predictions. From the occlusion sensitivity study, we showed that the models were able to identify the geometric features and have discovered that the model has learned to exploit the global information present in the SDF. In contrast to the currently operational method, where design changes are restricted to the initially defined parameters, this study brings data-driven surrogate models one step closer to the long-term goal of having a model that can be used for approximate aerodynamic evaluation of unseen, arbitrary vehicle shapes, thereby providing complete design freedom to the vehicle stylists.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Learning for Real-Time Aerodynamic Evaluations of Arbitrary Vehicle Shapes


    Additional title:

    Sae Int. J. Passeng. Veh. Syst


    Contributors:


    Publication date :

    2022-03-22


    Size :

    14 pages




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Aerodynamic Load Maps of Vehicle Shapes at Arbitrary Attitude

    Turbeville, Franklin / Hiremath, Nandeesh / Komerath, Narayanan et al. | SAE Technical Papers | 2015


    Aerodynamic Load Maps of Vehicle Shapes at Arbitrary Attitude

    Motahari, Nicholas R. / Turbeville, Franklin / Hiremath, Nandeesh et al. | British Library Conference Proceedings | 2015


    Aerodynamic Solutions for Finite-span Wings of Arbitrary Shapes

    Mateescu, Dan / Seytre, Jean Francois / Berhe, Awot | AIAA | 2003


    Rapid Aerodynamic Development of Two-Volume Vehicle Shapes

    Koromilas, C. / Harris, C. / Sumantran, V. et al. | British Library Conference Proceedings | 2000


    Rapid aerodynamic development of two-volume vehicle shapes

    Koromilas,C. / Harris,C. / Sumantran,V. et al. | Automotive engineering | 2000