Flow-signal correlation in seal whisker array sensing

Abstract

Phocid seals detect and track artificial or biogenic hydrodynamic trails based on mechanical signals of their whisker arrays. In this paper, we investigated the correlations between flow structures and whisker array signals using a simplified numerical model of fluid–structure interaction (FSI). Three-dimensional (3D) wakes of moving paddles in three different shapes (rectangular plate, undulated plate, and circular cylinder) were simulated using an in-house immersed-boundary-method-based computational fluid dynamics solver. One-way FSI was then simulated to obtain the dynamic behavior and root signal of each whisker in the two whisker arrays on a seal head in each wake. The position, geometry, and material properties of each whisker were modeled based on the measurements reported in literatures. The correlations between the wake structures and whisker array signals were analyzed. It was found that the patterns of the signals on the whisker arrays can reflect the strength, timing, and moving trajectories of the jets induced by the vortices in the wakes. Specifically, the rectangular plate generates the strongest starting vortex ring as well as the strongest jets, while the undulated plate generates the weakest ones. These flow features are fully reflected by the largest whisker signal magnitude in the rectangular plate sensing and the smallest one in the undulated plate sensing. Moreover, the timing of the signal initiation and the maximum signal agree well with the timing of the jet reaching the arrays and the maximum flow speed, respectively. The correlation coefficient between the moving trajectories of the jet and the movement of the high signal level region in the array was found to be higher than 0.9 in the rectangular plate case. The results provide a physical insight into the mechanisms of seal whisker flow sensing.

Spatial arrangement of the whiskers of harbor seals (Phoca vitulina) compared to whisker arrangements of house mice (Mus musculus) and brown rats (Rattus norvegicus)

“…We anticipate that the present work will be useful for developing underwater seal-like robots for wake detection and tracking, and for studying the biomechanics and neurobiology of these behaviors. For example, the model of the seal array developed here has already been used in fluid mechanics simulations (Liu, 2022) to explore how the pattern of signals received by the whiskers reflects flow structure. ACKNOWLEDGMENTS We thank Dr. Christine Scholtyßeck for help handling the seals.…”

Effects of antagonistic muscle actuation on the bilaminar structure of ray-finned fish in propulsion

“…Following a second-order accuracy in the discretization of the flow field, this computation has a local and global second-order accuracy. This method has been successfully applied to different biological applications (Geng et al 2017;Bodaghi et al 2021a,b;Liu et al 2021). More details of this method can be found in Mittal et al (2008).…”

Deciphering the connection between upstream obstacles, wake structures, and root signals in seal whisker array sensing using interpretable neural networks

“…To treat the immersed boundaries, a multidirectional ghost-cell methodology is employed, resulting in both local and global second-order accuracy. The solver used in this study has been successfully applied to various biological applications, including human phonation, insect flight, fish swimming, and seal whiskers inspired flow sensing ( Geng et al, 2017 ; Liu et al, 2018 ; Liu et al, 2019b ; Bodaghi et al, 2021a ; Bodaghi et al, 2021b ; Liu et al, 2021 ). Details of the solver can be found in Mittal et al (2008) .…”

“…An in-house finite element method code is utilized to solve the equation for based on the hydrodynamic loads obtained from the flow solver. This solver was fully validated by Liu et al (2019a) and was successfully employed for different biological simulations, including fish swimming ( Liu et al, 2018 ) and seal whiskers ( Liu et al, 2021 ).…”

“…Artificial whisker array experiments have also indicated that cross-correlation of bending signals can be used to detect vortices ( Glick et al, 2021 ). Our previous computational studies have shown that whisker array signals can reflect the strength, timing, and trajectories of upstream vortex-induced jets ( Liu et al, 2021 ). Machine-learning models have been used to infer the position of upstream objects based on whisker tip displacements ( Elshalakani et al, 2020 ).…”