Nonlinear parametric models of viscoelastic fluid flows

• Published in: Royal Society open science 2024-10, Vol.11 (10), p.240995-19
• Main Authors: Oishi, C M, Kaptanoglu, A A, Kutz, J Nathan, Brunton, S L
• Format: Article
• Language: English
• Subjects: Algorithms; Bifurcations; computational fluid dynamics; data-driven models; Elastic limit; Engineering; Fluid dynamics; Fluid flow; Fluid mechanics; Four roll mills; Fourier transforms; Geometry; Machine learning; Newtonian fluids; Non Newtonian flow; Nonlinear dynamics; Nonlinear systems; Ordinary differential equations; Oscillations; Partial differential equations; Physics; Polymers; Reduced order models; Reynolds number; sparse identification of nonlinear dynamics; Spatial data; Viscoelastic fluids; Viscoelasticity; Viscosity
• ISSN: 2054-5703; 2054-5703
• DOI: 10.1098/rsos.240995

Reduced-order models (ROMs) have been widely adopted in fluid mechanics, particularly in the context of Newtonian fluid flows. These models offer the ability to predict complex dynamics, such as instabilities and oscillations, at a considerably reduced computational cost. In contrast, the reduced-order modelling of non-Newtonian viscoelastic fluid flows remains relatively unexplored. This work leverages the (SINDy) algorithm to develop interpretable ROMs for viscoelastic flows. In particular, we explore a benchmark oscillatory viscoelastic flow on the four-roll mill geometry using the classical Oldroyd-B fluid. This flow exemplifies many canonical challenges associated with non-Newtonian flows, including transitions, asymmetries, instabilities, and bifurcations arising from the interplay of viscous and elastic forces, all of which require expensive computations in order to resolve the fast timescales and long transients characteristic of such flows. First, we demonstrate the effectiveness of our data-driven surrogate model to predict the transient evolution and accurately reconstruct the spatial flow field for fixed flow parameters. We then develop a fully parametric, nonlinear model capable of capturing the dynamic variations as a function of the Weissenberg number. While the training data are predominantly concentrated on a limit cycle regime for moderate , we show that the parametrized model can be used to extrapolate, accurately predicting the dominant dynamics in the case of high Weissenberg numbers. The proposed methodology represents an initial step in applying machine learning and reduced-order modelling techniques to viscoelastic flows.