Numerical simulation and prediction model development of multiple flexible filaments in viscous shear flow using immersed boundary method and artificial neural network techniques

Many chemical and biological systems have applications involving fluid-structure interaction (FSI) of flexible filaments in viscous fluid. The dynamics of single- and multiple-filament interaction are of interest to engineers and biologists working in the area of DNA fragmentation, protein synthesis...

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Bibliographic Details
Published in:Fluid dynamics research 2020-08, Vol.52 (4), p.45507
Main Authors: Kanchan, Mithun, Maniyeri, Ranjith
Format: Article
Language:English
Subjects:
artificial neural networks
finite volume method
flexible filaments
immersed boundary method
low Reynolds number
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Summary:Many chemical and biological systems have applications involving fluid-structure interaction (FSI) of flexible filaments in viscous fluid. The dynamics of single- and multiple-filament interaction are of interest to engineers and biologists working in the area of DNA fragmentation, protein synthesis, polymer segmentation, folding-unfolding analysis of natural and synthetic fibers, etc. To perform numerical simulation of the above-mentioned FSI applications is challenging. In this direction, methods like the immersed boundary method (IBM) have been quite successful. We simulate the dynamics of multiple flexible filaments subjected to planar shear flow at low Reynolds number using the finite volume method-based IBM. The governing continuity and Navier-Stokes equations are solved by the SIMPLE algorithm on a staggered Cartesian grid system. The validation of the developed model is done using previous works. The length of the filament, its bending rigidity and fluid shear rate are taken as parametric variables and numerical simulations are carried out. Viscous flow forcing and fractional contraction terms are incorporated so as to effectively categorize filament motion into various deformation regimes. The effects of tumbling motion on the filament migration and recuperative aspects are studied. The mutual interaction of two filaments placed side by side is thus observed. Finally, an artificial neural network model is developed from the IBM simulation results to predict tumbling counts for different filament parameters.
ISSN:0169-5983
1873-7005
1873-7005
DOI:10.1088/1873-7005/aba9b8