Joint Estimation of Bulk Flow Velocity and Angle Using a Lateral Line Probe

Measurement of complex natural flows, especially those occurring in rivers due to man-made structures, is often hampered by the limitations of existing flow measurement methods. Furthermore, there is a growing need for new measurement devices that are capable of measuring the hydrodynamic characteristics of complex natural flows required in environmental studies that often use fish as an indicator of ecological health. In this paper, we take the first step toward in situ natural flow measurements with a new biologically inspired probe design in conjunction with signal processing methods. The device presented in this paper is a dedicated hydrodynamically sensitive sensor array following the fish lateral line sensor modality. Low-level multidimensional sensor signals are transformed to the two key hydrodynamic primitives, bulk flow velocity and bulk flow angle. We show that this can be achieved via canonical signal transformation and kernel ridge regression, allowing velocity estimates with a less than 10 cm/s error. The approach provides robust velocity estimates not only when the sensor is ideally oriented parallel to the bulk flow, but also across the full range of angular deviations up to a completely orthogonal orientation by correcting the pressure field asymmetry for large angular deviations. Furthermore, we show that their joint estimation becomes feasible above a threshold current velocity of 0.45 m/s. The method demonstrated an error of 14 cm/s in velocity estimation in a river environment after training in laboratory conditions.

I. Introduction

Quantitative evaluation of complex natural flows, especially in rivers, has become an important issue as poorly functioning hydraulic structures can be detrimental to development of an ecosystem. New measurement devices and data processing workflows are required that are capable of measuring the complex in situ hydrodynamic conditions in order to catalog natural flow characteristics, which can then be used to improve existing structures and optimize current design standards.