Surrogate model benchmark for kω-SST RANS turbulence closure coefficients

Philipp Schlichter, Michaela Reck, Jutta Pieringer, Thomas Indinger

Research output: Contribution to journalArticlepeer-review

Abstract

AI-based methods show immense potential to assist engineers in further improving vehicle aerodynamics. It is vital to assess the performance of different surrogate models based on the provided training data size and shape to aid future model selections. This study uses data from the simulated flow around the 2D NACA 8810 airfoil. The closure coefficients of the kω-SST RANS turbulence model are varied by Design of Experiment to achieve the desired amount of varying data for the training and validation datasets. Each dataset uses Principal Component Analysis to generate various levels of dimensional reduction.

Original languageEnglish
Article number105678
JournalJournal of Wind Engineering and Industrial Aerodynamics
Volume246
DOIs
StatePublished - Mar 2024

Keywords

  • Benchmark
  • Machine learning
  • Regression methods

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