Bioinspired Tapered-Spring Turbulence Sensor for Underwater Flow Detection

This paper presents a bio-inspired underwater whisker sensor for robust hydrodynamic disturbance detection and efficient signal analysis based on Physical Reservoir Computing (PRC). The design uses a tapered nylon spring with embedded accelerometers to achieve spatially distributed vibration sensing and frequency separation along the whisker. Towing-tank experiments and computational fluid dynamics simulations confirmed that the whisker effectively distinguishes vortex regimes across different fin angles and maintains Strouhal scaling with flow velocity, where higher speeds increase vibration intensity without affecting the dominant frequencies. Frequency-domain analysis, Shannon entropy, and machine learning further validated the sensing performance: vortex shedding frequencies were identified with less than 10% error, entropy captured the transition from coherent vortex streets to turbulence, and logistic regression achieved 86.0% classification accuracy with millisecond-level inference. Importantly, the sensing objective of this work is to characterize repeatable and physically interpretable hydrodynamic disturbances, which provide a controlled basis for evaluating how the tapered geometry encodes flow-induced vibrations. Under this scope, the demonstrated frequency–spatial decoupling and multi-modal responses show that structurally encoded whisker sensing provides a scalable and real-time solution for underwater perception, wake tracking, and turbulence-aware navigation in autonomous marine robots.