Identifying regions of importance in wall-bounded turbulence through explainable deep learning

Andrés Cremades (), Sergio Hoyas, Rahul Deshpande, Pedro Quintero, Martin Lellep, Will Junghoon Lee, Jason P. Monty, Nicholas Hutchins, Moritz Linkmann, Ivan Marusic and Ricardo Vinuesa ()
Additional contact information
Andrés Cremades: KTH Royal Institute of Technology
Sergio Hoyas: Universitat Politècnica de València
Rahul Deshpande: University of Melbourne
Pedro Quintero: Universitat Politècnica de València
Martin Lellep: The University of Edinburgh
Will Junghoon Lee: University of Melbourne
Jason P. Monty: University of Melbourne
Nicholas Hutchins: University of Melbourne
Moritz Linkmann: University of Edinburgh
Ivan Marusic: University of Melbourne
Ricardo Vinuesa: KTH Royal Institute of Technology

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem in classical physics that requires new perspectives to be tackled. One of the key strategies has been to study interactions among the energy-containing coherent structures in the flow. Such interactions are explored in this study using an explainable deep-learning method. The instantaneous velocity field obtained from a turbulent channel flow simulation is used to predict the velocity field in time through a U-net architecture. Based on the predicted flow, we assess the importance of each structure for this prediction using the game-theoretic algorithm of SHapley Additive exPlanations (SHAP). This work provides results in agreement with previous observations in the literature and extends them by revealing that the most important structures in the flow are not necessarily the ones with the highest contribution to the Reynolds shear stress. We also apply the method to an experimental database, where we can identify structures based on their importance score. This framework has the potential to shed light on numerous fundamental phenomena of wall-bounded turbulence, including novel strategies for flow control.

Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-47954-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47954-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-47954-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().