Tomopterids are mesmerizing holopelagic swimmers. They use two modes of locomotion simultaneously: drag-based metachronal paddling and bodily undulation.Tomopterishas two rows of flexible legs (parapodia) positioned on opposite sides of its body. Each row performs metachronal paddling out of phase to the other. Both paddling behaviors occur in concert with a lateral bodily undulation. However, when looked at independently, each mode appears in tension with the other. The direction of the undulatory wave is opposite of what one may expect for forward swimming and appears to actively work act against the direction of swimming initiated by metachronal paddling. To investigate how these two modes of locomotion synergize to generate effective swimming, we created an self-propelled, fluid-structure interaction model of an idealizedTomopteris. We holistically explored swimming performance over a 3D mechanospace comprising parapodia length, paddling amplitude, and undulatory amplitude using a machine learning framework based on polynomial chaos expansions. Although undulatory amplitude minimally affected forward swimming speeds, it helped mitigate larger costs of transport which arise from either using more mechanically expensive (larger) paddling amplitudes and/or having longer parapodia.
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