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Abstract Tomopterids are mesmerizing holopelagic swimmers. They use two modes of locomotion simultaneously: drag-based metachronal paddling and bodily undulation.Tomopterishas two rows of flexible, leg-like parapodia positioned on opposite sides of its body. Each row metachronally paddles 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 (FWD) 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 a 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 FWD swimming speeds, it helped mitigate the larger costs of transport that arise from either using more mechanically expensive (larger) paddling amplitudes and/or having longer parapodia. 

A large diversity of fluid pumps is found throughout nature. The study of these pumps has provided insights into fundamental fluid dynamic processes and inspiration for the development of micro-fluid devices. Recent work by Thiria and Zhang [Appl. Phys. Lett. 106, 054106 (2015)] demonstrated how a reciprocal, valveless pump with a geometric asymmetry could drive net fluid flow due to an impedance mismatch when the fluid moves in different directions. Their pump's geometry is reminiscent of the asymmetries seen in the chains of contractile chambers that form the insect heart and mammalian lymphangions. Inspired by these similarities, we further explored the role of such geometric asymmetry in driving bulk flow in a preferred direction. We used an open-source implementation of the immersed boundary method to solve the fluid-structure interaction problem of a viscous fluid moving through a sawtooth channel whose walls move up and down with a reciprocal motion. Using a machine learning approach based on generalized polynomial chaos expansions, we fully described the model's behavior over the target 3-dimensional design space, composed of input Reynolds numbers (Rein), pumping frequencies, and duty cycles. Scaling studies showed that the pump is more effective at higher intermediate Rein. Moreover, greater volumetric flow rates were observed for near extremal duty cycles, with higher duty cycles (longer contraction and shorter expansion phases) resulting in the highest bulk flow rates.