Flow Field Analysis of a Racing Car based on Dimensionality Reduction and Clustering
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.):¥1,100 Members (tax incl.):¥880
- Publication code
- 20225270
- Paper/Info type
- Proceedings (Spring)
No.61-22
- Pages
- 1-8(Total 8 p)
- Date of publication
- May 2022
- Publisher
- JSAE
- Language
- English
- Event
- 2022 JSAE Annual Congress (Spring)
Detailed Information
Author(E) | 1) Michaela Reck, 2) René Hilhorst, 3) Marc Hilbert, 4) Thomas Indinger |
---|---|
Affiliation(E) | 1) Technical University of Munich, 2) Toyota GAZOO Racing Europe, 3) Leiden University, 4) Technical University of Munich |
Abstract(E) | The aerodynamic development process of a racing car involves the generation of a vast amount of data from numerical investigations. Hence, machine learning techniques are used in order to optimize the aerodynamic analysis workflow. In this study, flow fields obtained from Reynolds Averaged Navier Stokes (RANS) simulations serve as input for dimensionality reduction and clustering methods. The objective is to relate variations in flow topology to changes of corresponding performance metrics, aiming for an improved understanding of predominant fluidic phenomena. Consequently, inferences of aerodynamically relevant zones around the vehicle provide meaningful insights for future aerodynamic development. |