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Summary & Details

Benchmark of Aerodynamic Surrogate Models on Varying Training Datasets

Delivery: Available on this site

Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20235328

Paper/Info type: Proceedings (Spring)

No.72-23

Pages: 1-2(Total 2 p)

Date of publication: May 2023

Publisher: JSAE

Language: English

Event: 2023 JSAE Annual Congress (Spring)

Detailed Information

Author(J)	1) Philipp Schlichter, 2) Michaela Reck, 3) Jutta Pieringer, 4) Thomas Indinger
Author(E)	1) Philipp Schlichter, 2) Michaela Reck, 3) Jutta Pieringer, 4) Thomas Indinger
Affiliation(J)	1) Technical University of Munich, 2) Technical University of Munich, 3) Audi, 4) Technical University of Munich
Affiliation(E)	1) Technical University of Munich, 2) Technical University of Munich, 3) Audi, 4) Technical University of Munich
Abstract(E)	AI-based methods show huge potential assisting engineers in their pursuit to further improve vehicle aerodynamics. To aid future model selections it is important to test the performance of different surrogate models based on the provided training data size and shape. In this study, data of the simulated flow around the 2D NACA 8810 airfoil is used. To achieve the desired amount of varying data for the training and validation datasets the closure coefficients of the kw-SST RANS turbulence model are varied by Design of Experiment. Different levels of dimensional reduction are generated for each dataset by using Principal Component Analysis.

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