Hydrodynamic Drag In Steller Sea Lions (Eumetopias Jubatus)

Abstract

Drag forces acting on Steller sea lions (Eumetopias jubatus) were investigated from ‘deceleration during glide’ measurements. A total of 66 glides from six juvenile sea lions yielded a mean drag coefficient (referenced to total wetted surface area) of 0.0056 at a mean Reynolds number of 5.5×10(6). The drag values indicate that the boundary layer is largely turbulent for Steller sea lions swimming at these Reynolds numbers, which are past the point of expected transition from laminar to turbulent flow. The position of maximum thickness (at 34 % of the body length measured from the tip of the nose) was more anterior than for a ‘laminar’ profile, supporting the idea that there is little laminar flow. The Steller sea lions in our study were characterized by a mean fineness ratio of 5.55. Their streamlined shape helps to delay flow separation, reducing total drag. In addition, turbulent boundary layers are more stable than laminar ones. Thus, separation should occur further back on the animal. Steller sea lions are the largest of the otariids and swam faster than the smaller California sea lions (Zalophus californianus). The mean glide velocity of the individual Steller sea lions ranged from 2.9 to 3.4 m s(−)(1) or 1.2-1.5 body lengths s(−)(1). These length-specific speeds are close to the optimum swim velocity of 1.4 body lengths s(−)(1) based on the minimum cost of transport for California sea lions.

Dynamics and Energetics of Bottlenose Dolphins (Tursiops truncatus) Fluke-and-Glide Gait

“…However, estimates of gliding drag over a range of speeds for bottlenose dolphins ( Tursiops truncatus ) are currently lacking. In this work, we estimate gliding drag coefficients for dolphins experimentally by fitting a drag model to speed measurements during gliding (Lang (1975); Bilo and Nachtigall (1980); Stelle et al (2000); McHenry and Lauder (2005); Zhang et al (2020)). In contrast to studies that use video data from structured trials to derive gliding speed profiles, we took a data-driven approach to extract the information from self-selected swimming data collected using biologging tags.…”