Work

Prediction of flow and polymeric stresses in a viscoelastic turbulent channel flow using convolutional neural networks

Year: 2025

Type: article

Abstract: Neural network models have been employed to predict the instantaneous flow close to the wall in a viscoelastic turbulent channel flow. Numerical simulation data at the wall are used to predict the instantaneous velocity fluctuations and polymeric-stress fluctuations at three different wall-normal positions in the buffer region. Such an ability of non-intrusive predictions has not been previously investigated in non-Newtonian turbulence. Our comparative analysis with reference simulation data shows that velocity fluctuations are predicted reasonably well from wall measurements in viscoelastic turbulence. The network models exhibit relatively improved accuracy in predicting quantities of interest during the hibernation intervals, facilitating a deeper understanding of the underlying physics during low-drag events. This method could be used in flow control or when only wall information is available from experiments (for example, in opaque fluids). More importantly, only velocity and pressure information can be measured experimentally, while polymeric elongation and orientation cannot be directly measured despite their importance for turbulent dynamics. We therefore study the possibility to reconstruct the polymeric-stress fields from velocity or pressure measurements in viscoelastic turbulent flows. The neural network models demonstrate a reasonably good accuracy in predicting polymeric shear stress and the trace of the polymeric stress at a given wall-normal location. The results are promising, but also underline that a lack of small scales in the input velocity fields can alter the rate of energy transfer from flow to polymers, affecting the prediction of the polymeric-stress fluctuations.

Source: Journal of Fluid Mechanics

DOI: 10.1017/jfm.2025.240

Authors: Arivazhagan G. Balasubramanian, Ricardo Vinuesa, Outi Tammisola

Institutions: Swedish e-Science Research Centre, KTH Royal Institute of Technology

Language: English

Topic: Rheology and Fluid Dynamics Studies

Subfield: Fluid Flow and Transfer Processes

Field: Chemical Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,550

Funders: Vetenskapsrådet, European Research Council