Decoding Propulsive Wakes using Biomimetic Seal Whiskers

Detecting and tracking swimming animals without disturbing them is a central need in fisheries protection and aquatic biological monitoring, yet optical and acoustic methods remain limited underwater. Passive hydrodynamic sensing offers an alternative: harbour seals can follow hydrodynamic wakes over long distances, and biomimetic whiskers respond measurably to the wakes of upstream bodies. Here we quantify how well four independently varied properties of a propulsive source can be discriminated simultaneously from a single force-instrumented whisker, using towingtank measurements of a 30× scaled biomimetic seal whisker instrumented with a six-axis force/torque transducer. First, we separate the sensor's intrinsic response from flowinduced forcing in quiescent flow over Reynolds numbers 4,770-20,670. The quiescentflow spectra show that the whisker's self-shedding response is weak and separated from the wake-relevant band. We then expose the sensor to wakes from a flapping NACA 0012 airfoil across 1,050 conditions in which the source frequency, pitching amplitude, angle of attack, and downstream distance are prescribed independently at the source. The measured force response locks to the source frequency, scales with pitching amplitude, varies systematically in the drag-to-lift fluctuation ratio with angle of attack, and decays approximately exponentially with distance, giving four distinct force signatures of the prescribed source state. A one-dimensional convolutional neural network trained on the raw six-channel time series recovers all four attributes simultaneously with 94.1% average test accuracy (mean over five random-seed runs; per-task 84-100%) and millisecondscale CPU inference latency. Frequency and source angle of attack are recovered essentially perfectly, whereas the remaining errors are concentrated in adjacent pitchingamplitude and downstream-distance classes, indicating that the model's failures reflect physically weak separability rather than missing spectral cues. Within the experimentally investigated parameter space, these results establish the force signature of a biomimetic whisker as a compact basis for reading the state of a propulsive wake source.

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Publication Details

Journal
Bioinspiration & Biomimetics
Published
2026-08-14
DOI
https://doi.org/10.1088/1748-3190/ae9a20
Primary Topic
Biomimetic flight and propulsion mechanisms
Type
article
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article

Decoding Propulsive Wakes using Biomimetic Seal Whiskers

Leixin Ma, Sanjay Giridharan
Bioinspiration & Biomimetics
Biomimetic flight and propulsion mechanisms
article

Decoding Propulsive Wakes using Biomimetic Seal Whiskers

Leixin Ma, Sanjay Giridharan
article en

Abstract

Detecting and tracking swimming animals without disturbing them is a central need in fisheries protection and aquatic biological monitoring, yet optical and acoustic methods remain limited underwater. Passive hydrodynamic sensing offers an alternative: harbour seals can follow hydrodynamic wakes over long distances, and biomimetic whiskers respond measurably to the wakes of upstream bodies. Here we quantify how well four independently varied properties of a propulsive source can be discriminated simultaneously from a single force-instrumented whisker, using towingtank measurements of a 30× scaled biomimetic seal whisker instrumented with a six-axis force/torque transducer. First, we separate the sensor's intrinsic response from flowinduced forcing in quiescent flow over Reynolds numbers 4,770-20,670. The quiescentflow spectra show that the whisker's self-shedding response is weak and separated from the wake-relevant band. We then expose the sensor to wakes from a flapping NACA 0012 airfoil across 1,050 conditions in which the source frequency, pitching amplitude, angle of attack, and downstream distance are prescribed independently at the source. The measured force response locks to the source frequency, scales with pitching amplitude, varies systematically in the drag-to-lift fluctuation ratio with angle of attack, and decays approximately exponentially with distance, giving four distinct force signatures of the prescribed source state. A one-dimensional convolutional neural network trained on the raw six-channel time series recovers all four attributes simultaneously with 94.1% average test accuracy (mean over five random-seed runs; per-task 84-100%) and millisecondscale CPU inference latency. Frequency and source angle of attack are recovered essentially perfectly, whereas the remaining errors are concentrated in adjacent pitchingamplitude and downstream-distance classes, indicating that the model's failures reflect physically weak separability rather than missing spectral cues. Within the experimentally investigated parameter space, these results establish the force signature of a biomimetic whisker as a compact basis for reading the state of a propulsive wake source.

Bioinspiration & Biomimetics
Arizona State University (US)
Reduced inequalities
Openalex Percentile: Top 6%
Biomimetic flight and propulsion mechanisms
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