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

Abstract

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

Lateral Migration of Variously Shaped Particles: A Computational Study

“…It is a monolithic and non-conforming mesh-type numerical method. This method was utilized for several FSI problems [11][12][13][14][15][16][17]. Shin et al [18] developed another IBM scheme with a feedback forcing scheme.…”

Modelling and Simulation Strategies for Fluid–Structure-Interactions of Highly Viscous Thermoplastic Melt and Single Fibres—A Numerical Study

“…Numerical investigations of flexible fibres in shear flows mainly focus short or long fibres. A distinction has to be made between single fibre [ 20 , 21 , 22 ] and multiple fibre modelling setups [ 18 , 23 ]. Established methods for fibre modelling are beam elements [ 21 , 23 ] or interlinked rigid spheres allowing elongation, bending, and twisting by a bear-spring chain model [ 20 ] or ball socket connection [ 24 ].…”

“…A distinction has to be made between single fibre [ 20 , 21 , 22 ] and multiple fibre modelling setups [ 18 , 23 ]. Established methods for fibre modelling are beam elements [ 21 , 23 ] or interlinked rigid spheres allowing elongation, bending, and twisting by a bear-spring chain model [ 20 ] or ball socket connection [ 24 ]. Instead of rigid spheres cylindrical segments can also be used [ 22 , 25 , 26 , 27 ].…”

Lateral migration of cylindrical particle in a constricted microchannel—A numerical study

“…This includes parachute aerodynamics, [ 20,21 ] jellyfish locomotion, [ 22–24 ] propulsion of bacterial flagellum, [ 25 ] valveless pump, [ 26 ] flapping of flexible ring, [ 27 ] elastic rod rotation, [ 28 ] bundling of bacterial flagella, [ 29 ] droplet dynamics, [ 30 ] leaflet dynamics in fluid flow, [ 31 ] and flexible filament dynamics. [ 32–35 ] Shin et al put forward a new version of IBM, [ 36 ] which has a combination of the virtual boundary method, [ 37 ] and Peskin's regularized delta function. This method was further utilized for flexible filament simulation [ 38 ] and to study the elastic capsule dynamics in confined flow.…”

“…Kanchan and Maniyeri developed an ANN model for predicting the flow rate past a flexible membrane, which took both training and testing data from the IBM simulations carried out. [ 35 ] This implies that the ANN model can be effectively coupled with IBM simulation data. Hence, a prediction model for predicting the equilibrium position and migration time can be developed using the ANN algorithm with the aid of simulation data.…”