Constrained re-calibration of two-equation Reynolds-averaged Navier–Stokes models

Yuanwei Bin, Xiaohan Hu, Jiaqi Li, Samuel J. Grauer, Xiang I.A. Yang

Research output: Contribution to journalArticlepeer-review

Abstract

Machine-learned augmentations to turbulence models can be advantageous for flows within the training dataset but can often cause harm outside. This lack of generalizability arises because the constants (as well as the functions) in a Reynolds-averaged Navier–Stokes (RANS) model are coupled, and un-constrained re-calibration of these constants (and functions) can disrupt the calibrations of the baseline model, the preservation of which is critical to the model's generalizability. To safeguard the behaviors of the baseline model beyond the training dataset, machine learning must be constrained such that basic calibrations like the law of the wall are kept intact. This letter aims to identify such constraints in two-equation RANS models so that future machine learning work can be performed without violating these constraints. We demonstrate that the identified constraints are not limiting. Furthermore, they help preserve the generalizability of the baseline model.

Original languageEnglish (US)
Article number100503
JournalTheoretical and Applied Mechanics Letters
Volume14
Issue number2
DOIs
StatePublished - Mar 2024

All Science Journal Classification (ASJC) codes

  • Computational Mechanics
  • Environmental Engineering
  • Civil and Structural Engineering
  • Biomedical Engineering
  • Aerospace Engineering
  • Ocean Engineering
  • Mechanics of Materials
  • Mechanical Engineering

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