Toward the Usage of Deep Learning Surrogate Models in Ground Vehicle Aerodynamics

Benet Eiximeno (), Arnau Miró, Ivette Rodríguez and Oriol Lehmkuhl
Additional contact information
Benet Eiximeno: Barcelona Supercomputing Center, 08034 Barcelona, Spain
Arnau Miró: Barcelona Supercomputing Center, 08034 Barcelona, Spain
Ivette Rodríguez: Turbulence and Aerodynamics Research Group, Universitat Politècnica de Catalunya (UPC), 08221 Terrassa, Spain
Oriol Lehmkuhl: Barcelona Supercomputing Center, 08034 Barcelona, Spain

Mathematics, 2024, vol. 12, issue 7, 1-23

Abstract: This study introduces a deep learning surrogate model designed to predict the evolution of the mean pressure coefficient on the back face of a Windsor body across a range of yaw angles from 2 . 5 ∘ to 10 ∘ . Utilizing a variational autoencoder (VAE), the model effectively compresses snapshots of back pressure taken at yaw angles of 2 . 5 ∘ , 5 ∘ , and 10 ∘ into two latent vectors. These snapshots are derived from wall-modeled large eddy simulations (WMLESs) conducted at a Reynolds number of R e L = 2.9 × 10 6 . The frequencies that dominate the latent vectors correspond closely with those observed in both the drag’s temporal evolution and the dynamic mode decomposition. The projection of the mean pressure coefficient to the latent space yields an increasing linear evolution of the two latent variables with the yaw angle. The mean pressure coefficient distribution at a yaw angle of 7 . 5 ∘ is predicted with a mean error of e ¯ = 3.13 % when compared to the WMLESs results after obtaining the values of the latent space with linear interpolation.

Keywords: surrogate models; deep learning; variational autoencoder; computational fluid dynamics; wall-modeled large eddy simulations; car aerodynamics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/7/998/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/7/998/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:7:p:998-:d:1365039

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().