仿生锥形弹簧传感器实现水下流场实时感知与湍流识别
Bioinspired Tapered-Spring Turbulence Sensor for Underwater Flow Detection
- 采用锥形尼龙弹簧嵌入加速度计，实现振动空间分布与频率分离
- 涡旋脱落频率识别误差小于10%，湍流过渡由熵值有效捕捉
- 支持毫秒级推理，适合自主水下机器人实时导航与尾迹追踪
本文提出一种仿生水下触须传感器，基于物理储备池计算（PRC）实现鲁棒的水动力扰动检测与高效信号分析。传感器采用嵌入式加速度计的锥形尼龙弹簧，实现触须沿程的空间振动感知与频率分离。拖曳水池实验与计算流体动力学模拟表明，该触须能有效区分不同鳍角下的涡流状态，并保持斯特劳哈尔数与流速的标度关系：流速越高，振动强度越大，但主频不变。频域分析、香农熵与机器学习进一步验证了传感性能：涡旋脱落频率识别误差低于10%，熵值准确反映层流到湍流的转变，逻辑回归分类准确率达86.0%，推理时间在毫秒级。结果表明，结构编码的触须感知为水下感知、尾迹追踪与湍流自适应导航提供了可扩展的实时解决方案。
原文摘要 · Abstract (English)
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.
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