A wall model learned from the periodic hill data and the law of the wall

Zhideng Zhou, Xiang I.A. Yang, Fengshun Zhang, Xiaolei Yang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Toward data-driven wall-modeled large-eddy simulations of different wall-bounded turbulent flows, a wall model is learned in this work using the wall-resolved large-eddy simulation (WRLES) data of the flow over periodic hills (PH) and the law of the wall (LoW). The feedforward neural network (FNN) is employed to construct the model. The obtained FNN_PH-LoW model is successfully tested using the direct numerical simulation data of turbulent channel flows and the WRLES data of PH cases, and applied to turbulent channel flows for a wide range of Reynolds numbers.

Original languageEnglish (US)
Article number055108
JournalPhysics of Fluids
Volume35
Issue number5
DOIs
StatePublished - May 1 2023

All Science Journal Classification (ASJC) codes

  • Computational Mechanics
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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