numerical analysis of two side-by-side flexible filaments in shear flow and orbit class prediction using immersed boundary method and machine learning techniques

This study utilizes a second-order immersed boundary method (ibm) for investigating the orbital dynamics of two flexible, inextensible filaments placed side-by-side in low-reynolds-number shear flows. a parametric analysis explores the effects of filament length, bending rigidity, and shear-strain rate on filament deformation and interaction. the dynamic behavior of the filaments is characterized by a flow-filament offset ratio (φ). results reveal that at high shear rates and filament lengths, the first filament (f1) minimally influences the hydrodynamic forces acting on the second filament (f2), leading to independent deformation patterns. however, at moderate lengths (l ≈ 0.2) and high shear rates (g ≈ 32), f1 significantly impacts f2 deformation. spectrogram analyses show that f1 absorbs fluid forces, generating multiple energy bands, while f2 experiences reduced forces, resulting in retarded motion and larger band gaps. a predictive model based on simulation results is developed using five machine learning classifiers: naïve bayes, support vector machine (svm), k-nearest neighbor (knn), decision tree, and artificial neural network (ann). among these, knn prediction for f2 outperforms, achieving an accuracy of 0.889, precision of 0.878, recall of 0.875, f1 score of 0.875, and roc area of 0.929, making it the most effective method for classifying filament orbital regimes. the findings of this study will pave the way for the next generation of microfluidic devices, unlocking unprecedented possibilities in particle sorting through the innovative use of flexible filaments.