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  • Letter
  • Open Access

Deep-learning-based assessment of skin friction in wall-bounded turbulence

Sergio Hoyas* and Nils Benedikt†

Andres Cremades and Ricardo Vinuesa‡

  • *Contact author: serhocal@mot.upv.es
  • †Present address: Technical University of Darmstadt, Chair of Fluid Dynamics, Otto-Bernd-Straße 2, 64287 Darmstadt, Germany.
  • ‡Contact author: rvinuesa@mech.kth.se
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Phys. Rev. Fluids 10, L062601 – Published 25 June, 2025

DOI: https://doi.org/10.1103/b36b-m5hd

Abstract

This work investigates the influence of classically coherent structures on wall-shear stress and energy dissipation in turbulent channel flow, utilizing direct numerical simulations (DNS) data and explainable deep learning (XDL). Sweeps, defined as regions of low streamwise velocity moving toward the wall, are found to be the most influential structures for both energy dissipation and drag. Moreover, the volume of these key structures falls within a narrow range, making it easier to identify the most significant ones. Consequently, this work paves the way for the development of novel, highly efficient turbulence-control strategies for the reduction of drag.

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Phys. Rev. Fluids 10, L062601– Published 25 June, 2025
  • Received 27 February 2025
  • Accepted 27 May 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by Bibsam.

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