Hopscotching jellyfish: combining different duty cycle kinematics can lead to enhanced swimming performance

Abstract

Jellyfish (Medusozoa) have been deemed the most energy-efficient animals in the world. Their bell morphology and relatively simple nervous systems make them attractive to robotocists. Although, the science community has devoted much attention to understanding their swimming performance, there is still much to be learned about the jet propulsive locomotive gait displayed by prolate jellyfish. Traditionally, computational scientists have assumed uniform duty cycle kinematics when computationally modeling jellyfish locomotion. In this study we used fluid-structure interaction modeling to determine possible enhancements in performance from shuffling different duty cycles together across multiple Reynolds numbers and contraction frequencies. Increases in speed and reductions in cost of transport were observed as high as 80% and 50%, respectively. Generally, the net effects were greater for cases involving lower contraction frequencies. Overall, robust duty cycle combinations were determined that led to enhanced or impeded performance.

A two-dimensional hydrodynamics prediction framework for mantle-undulated propulsion robot using multiple proper orthogonal decomposition and long short term memory neural network

“…In contrast, when the propulsion force reaches the second most prominent peak, the mantle's end is furthest from the wall. This occurrence aligns with existing experimental research on jellyfish-inspired robots [20,70]. It is noteworthy that during the sub-stroke, the hydrodynamic force acting on the MUPRo still provides propulsion.…”

“…The exploration and discussion of jellyfish propulsion can provide guidance in the control of propulsion and optimisation of parameters in a jellyfish-inspired robot [20]. The hydrodynamic mechanisms of jellyfish, as well as the jellyfishinspired robots, have garnered significant attention from the scientific community through experimental measurements [12,15,[21][22][23], numerical simulations [24][25][26][27][28], and reduced-order models [29].…”