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Deep-learning-based assessment of skin friction in wall-bounded turbulence
Univ Politecn Valencia, Inst Univ Matemat Pura & Aplicada, Valencia 46022, Spain.
Univ Politecn Valencia, Inst Univ Matemat Pura & Aplicada, Valencia 46022, Spain; Tech Univ Darmstadt, Chair Fluid Dynam, Otto Bernd Str 2, D-64287 Darmstadt, Germany.
KTH, School of Engineering Sciences (SCI), Engineering Mechanics.ORCID iD: 0000-0002-7052-4913
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics.ORCID iD: 0000-0001-6570-5499
2025 (English)In: Physical Review Fluids, E-ISSN 2469-990X, Vol. 10, no 6, article id L062601Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
American Physical Society (APS) , 2025. Vol. 10, no 6, article id L062601
National Category
Fluid Mechanics
Identifiers
URN: urn:nbn:se:kth:diva-372721DOI: 10.1103/b36b-m5hdISI: 001528699900006OAI: oai:DiVA.org:kth-372721DiVA, id: diva2:2017183
Note

QC 20251127

Available from: 2025-11-27 Created: 2025-11-27 Last updated: 2025-11-27Bibliographically approved

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Cremades, AndrésVinuesa, Ricardo

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