Hydrodynamic pressure sensing with an artificial lateral line in steady and unsteady flows

Abstract

With the overall goal being a better understanding of the sensing environment from the local perspective of a situated agent, we studied uniform flows and Kármán vortex streets in a frame of reference relevant to a fish or swimming robot. We visualized each flow regime with digital particle image velocimetry and then took local measurements using a rigid body with laterally distributed parallel pressure sensor arrays. Time and frequency domain methods were used to characterize hydrodynamically relevant scenarios in steady and unsteady flows for control applications. Here we report that a distributed pressure sensing mechanism has the capability to discriminate Kármán vortex streets from uniform flows, and determine the orientation and position of the platform with respect to the incoming flow and the centre axis of the Kármán vortex street. It also enables the computation of hydrodynamic features which may be relevant for a robot while interacting with the flow, such as vortex shedding frequency, vortex travelling speed and downstream distance between vortices. A Kármán vortex street was distinguished in this study from uniform flows by analysing the magnitude of fluctuations present in the sensor measurements and the number of sensors detecting the same dominant frequency. In the Kármán vortex street the turbulence intensity was 30% higher than that in the uniform flow and the sensors collectively sensed the vortex shedding frequency as the dominant frequency. The position and orientation of the sensor platform were determined via a comparative analysis between laterally distributed sensor arrays; the vortex travelling speed was estimated via a cross-correlation analysis among the sensors.

A fish perspective: detecting flow features while moving using an artificial lateral line in steady and unsteady flow

“…Further wake navigation algorithms have been previously developed based on the predicted pressure signal [14]. Research by Venturelli et al [15] sampled a KVS using two symmetric linear lateral lines running on either side of a three-dimensional 'boat-shaped' craft, as an analogue of the posterior lateral line. Characterization of the KVS was performed using calculations of turbulence intensity and vortex shedding frequency (VSF) and by measuring the magnitude of the fluctuations present in the sensor readings.…”

Flow-relative control of an underwater robot

“…Our results showed that periodic turbulence (KVS) can be detected by simply monitoring the pressure readings in the nose of the robot. As opposed to the spectral and turbulence analyses proposed in [21], this method works in real time owing to the very fast response and the very distinctive change in pressure readings at the nose when entering the KVS. If the change is caused solely by the changing speed of the uniform flow, then there will also be difference in the pressure on the sides of the robot, making the identification of those two different events possible.…”

“…Several types of artificial superficial MEMS-based neuromasts have been developed [18,19] and demonstrated to be capable of detecting hydrodynamic events, such as a dipole source [20]. Pressure sensors [21] or optical flow sensors placed in artificial canals [22] have been also used, and it is demonstrated that the presence of KVS can be detected and the position of the cylinder generating the street can be estimated from those sensor readings [21].…”

Micro-Machined Flow Sensors Mimicking Lateral Line Canal Neuromasts

“…Previous studies investigated this issue [51,82]. However, experiments were done mainly in open frame flow tanks.…”