Bidirectional biomimetic flow sensing with antiparallel and curved artificial hair sensors
Source abstract
Abstract
Background:Flow stimuli in the natural world are varied and contain a wide variety of directional information. Nature has developed morphological polarity and bidirectional arrangements for flow sensing to filter the incoming stimuli. Inspired by the neuromasts found in the lateral line of fish, we present a novel flow sensor design based on two curved cantilevers with bending orientation antiparallel to each other. Antiparallel cantilever pairs were designed, fabricated and compared to a single cantilever based hair sensor in terms of sensitivity to temperature changes and their response to changes in relative air flow direction. Results:In bidirectional air flow, antiparallel cantilever pairs exhibit an axially symmetrical sensitivity between 40 μV/(m s−1) for the lower air flow velocity range (between ±10–20 m s−1) and 80 μV/(m s−1) for a higher air flow velocity range (between ±20–32 m s−1). The antiparallel cantilever design improves directional sensitivity and provides a sinusoidal response to flow angle. In forward flow, the single sensor reaches its saturation limitation, flattening at 67% of the ideal sinusoidal curve which is earlier than the antiparallel cantilevers at 75%. The antiparallel artificial hair sensor better compensates for temperature changes than the single sensor. Conclusion:This work demonstrated the successive improvement of the bidirectional sensitivity, that is, improved temperature compensation, decreased noise generation and symmetrical response behaviour. In the antiparallel configuration, one of the two cantilevers always extends out into the free stream flow, remaining sensitive to directional flow and preserving a sensitivity to further flow stimuli.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Biomimetic flight and propulsion mechanisms · Marine animal studies overview · Neurobiology and Insect Physiology Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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