Published March 8, 2018 | Version v1
Journal article Open

Propulsive efficiency in drag-based locomotion of a reduced-size swimmer with various types of appendages

  • 1. Department of Biomedical Engineering, University of Strathclyde
  • 2. Institute of Computer Science, Foundation for Research & Technology-Hellas
  • 3. Department of Aerospace Engineering, Embry-Riddle Aeronautical University

Description

The propulsive efficiencies of multi-functional appendage configurations in a small drag-based swimmer are investigated computationally. Due to the lack of actual actuators to measure input power, efficiency is evaluated indirectly and may be instinctively associated to higher production of forward thrust. However, the relation is not intuitively self-evident, since the shape of the propulsive system is known to influence the generation of hydrodynamic forces, along with the particular kinematics used, which in turn affect the power consumption. The current article investigates this topic in the case of a reduced-size appendage-based swimmer producing small values of thrust, and discusses the role of design in the relation between propulsive efficiency and thrust production under a “sculling” kinematic motion profile. The study implements seven different shapes of appendages, inspired by both the biology and engineering, which perform a drag-based swimming pattern while being attached, in pairs, at the dorsal side of a common body. The work utilises an immersed boundary approach to solve numerically the fluid equations and capture the flow patterns around the swimmer. The results contribute to our understanding of drag-based propulsive systems and may influence the development of novel underwater robotic systems and limb prosthetic devices for underwater rehabilitation.

Files

Kazakidi_etal_CAF_2018_Propulsive_efficiency_in_drag_based_locomotion_of_a_reduced_size_swimmer.pdf

Additional details

Funding

European Commission
CALLIRHEO – Multi-scale computational hemodynamics in obese children and adolescents: enabling personalised prediction of cardiovascular disease 749185