Viscosity estimation from elastohydrodynamic response of a magnetic flagellated microswimmer

Reliable viscosity measurement at small length scales is essential for a wide range of applications, from microfluidics to biomedical systems. The magnetic flagellated microswimmers, whose dynamics arise from the coupled effects of elasticity, hydrodynamics, and magnetic actuation, offer a promising route to estimate viscosity. In this study, we propose a multiparametric approach to exploit the dynamics of a microswimmer for sensing the fluid viscosity. A finite-element-based model of a flagellated microswimmer is developed and analyzed to identify the characteristic parameters. The extracted time- and frequency-domain descriptors are found to be highly sensitive to variations in fluid viscosity. Machine learning models are subsequently developed to capture the relationship between these extracted features and fluid viscosity, resulting in strong predictive performance and reliable estimation of viscosity. The findings of this study reveal a previously unrecognized perspective, wherein the amplitudes of harmonic components (f, 2f, 4f) among the extracted features serve as dominant indicators for accurate viscosity estimation. The validity of the proposed approach is further confirmed by a scaled-up experimental system with a millimeter-sized swimmer under low Reynolds number conditions. The experimental observations retain the underlying physical behavior and substantiate the relevance of the extracted features, with the amplitudes of harmonic components playing a pivotal role in viscosity prediction. The agreement between numerical and experimental results highlights the potential of this framework as a reliable and scalable strategy for viscosity sensing, offering a promising alternative to conventional viscosity measurement techniques.