Bioinspired Tapered-Spring Turbulence Sensor for Underwater Flow Detection

2025-10-07

AI Summary

To address the challenge of real-time sensing of vortices, wakes, and flow transition by autonomous underwater vehicles (AUVs) in turbulent environments, this work proposes a biomimetic conical nylon spring “whisker” sensor that integrates structural vibration encoding with physical reservoir computing. The passive sensor leverages geometric tapering to spatially separate vibration modes and frequency components while inherently preserving Strouhal-number scaling—eliminating the need for external power. Coupled with an embedded accelerometer, frequency-domain analysis, Shannon entropy characterization, and a lightweight logistic regression classifier, it achieves <10% error in vortex shedding frequency estimation and sensitively captures streamwise turbulent transition via entropy dynamics, yielding 86.0% classification accuracy. Inference latency is sub-millisecond. Validated jointly in towing-tank experiments and CFD simulations, this study pioneers the application of physical reservoir computing to real-time underwater flow disturbance decoding—significantly enhancing sensing robustness, energy efficiency, and system scalability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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. These results demonstrate 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.

Problem

• Detects underwater hydrodynamic disturbances using bio-inspired whisker sensor
• Distinguishes vortex regimes and maintains Strouhal scaling with flow velocity
• Provides real-time turbulence sensing for autonomous marine robot navigation

Innovation

• Bioinspired tapered spring with embedded accelerometers for vibration sensing
• Physical Reservoir Computing enables efficient hydrodynamic signal analysis
• Structurally encoded whisker provides real-time turbulence detection and classification