Sweep and Taper Analysis of Surfboard Fins Using Computational Fluid Dynamics

<p>The research presented here provides a basis for understanding the hydrodynamics of surfboard fin geometries. While there have been select studies on fins there has been little correlation to the shape of the fin and its corresponding hydrodynamic performance. This research analyzes how changing the planform shape of a surfboard fin effects its performance and flow field. This was done by isolating the taper and sweep distribution of a baseline geometry and varying each parameter individually whilst maintaining a constant span and surface area. The baseline surfboard fin was used as a template in Matlab to generate a set of x and y coordinates that defined the outline of the fin shape. These coordinates were then altered by changing either the sweep or taper distribution and resulted in new, unique planform shapes. The new shapes were used to generate 3D models with the NACA 0006 foil as the cross-section hydrofoil.</p> <p>After the geometry was modeled, each fin was meshed and simulated in CFD for incidence angles ranging from 0^o to 20^o and a fin Reynolds Number of 3.51x10⁵. When the sweep distribution was changed, there was a direct correlation to vortex formation off the leading edge. Increasing the sweep generated a stronger vortex that persisted for higher angles of attack and resulted in higher moments but increased drag. Changing the taper distribution was not as influential. The tapered fin set showed similar flow fields and body forces to each other. Making a fin more rectangular had slight decreases in drag but made the shape more prone to separation.</p>

Name thesis:degree_name

MS in Aerospace Engineering

Discipline thesis:degree_discipline

Aerospace Engineering

Year dc:date.available

2019

Author dc:creator

• Baldovin, Brandon James

Contributors dc:contributor

• David Marshall

• CFD
• Surfboard
• Aerodynamics
• Hydrodynamics
• Aerospace
• Aerodynamics and Fluid Mechanics

Identifier

10.15368/theses.2019.8

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-3374