Modeling of a Bio-Inspired Canal-Type Lateral Line System

Abstract

It is of interest to exploit the insight from the lateral line system of fish for flow sensing applications. The lateral line consists of arrays of flow sensors, known as neuromasts, with hair cells encased within a gel-like structure called cupula. There are two types of neuromasts, superficial neuromasts, which reside on the surface, and canal neuromasts, which are recessed within a channel with its ends open at the body’s surface. In this work we investigate the modeling of a canal-type artificial lateral line system. The canal is filled with viscous fluid to emulate its biological counterpart. The artificial neuromast consists of an ionic polymer-metal composite (IPMC) sensor embedded within a soft molded cupula structure. The displacement of the cupula structure and the resulting short-circuit current of the IPMC sensor under an oscillatory flow are modeled and solved with finite-element methods. The Poisson-Nernst-Planck (PNP) model is used to describe the fundamental physics within the IPMC, where the bending stimulus due to the cupula displacement is coupled to the PNP model through the cation convective flux term. Comparison of the numerically computed cupula displacement with an analytical approximation is conducted. The effects of material stiffness and and device size on the device sensitivity are further explored.