Work

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

Year: 2025

Type: article

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.

Source: Physical Review Fluids

DOI: 10.1103/b36b-m5hd

Authors: Sergio Hoyas, Nils Benedikt, Andres Cremades, Ricardo Vinuesa

Institutions: Universitat Politècnica de València, KTH Royal Institute of Technology

Language: English

Topic: Fluid Dynamics and Turbulent Flows

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Funders: European Commission, Ministerio de Ciencia e Innovación, European Research Council

Awards: G3541217332, G8625477285, G8733291549