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Session: Poster Session 1 (Even numbers)
Session starts: Thursday 26 January, 16:00
Presentation starts: 16:00

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Jan Thomas Heinen (BioMechanical Engineerin, TU Delft)

Abstract:
Skateboarding involves a human controlling a four wheeled vehicle that is steered by tilting the standing surface. The riding mechanics of skateboarding have been well reported [1][2]. The sport also includes aerial maneuvers such as jumping of stairs, flying off ramps and flipping and rotating the skateboard. The most basic aerial trick is called the ollie. The athlete jumps up while pushing down on the back end of the skateboard’s tail, causing a rotation about the back axle. The upward acceleration due to the rotation together with the tail-ground impact cause the skateboard to go airborne. Midair the athlete pulls the skateboard up through frictional contact and levels it out to land the trick. The most absolute performance measure of the trick is height [3]. To reach maximum height the dynamics such as impact, dynamic response, and torque production are dependent on shape, inertia and mass, which gives reason to assume an optimal shape exists. This leads to the research question: How do geometric parameters influence the maximum jumping height during the ollie. A 9-piece two- dimensional model is made with a theoretical inertia values. The inertia values are tested and scaled appropriately to verify the model. With a multi-phase direct collocation scheme the model is optimized for maximum height subject to the dynamics of the skater and human to find the optimal trajectory and parameters. The ollie height is improved by changing either one of these parameters relative to a standardized skateboard; longer wheelbase, smaller tail angle, shorter flat part, and lower truck height. The results could be beneficial for Olympic performers to score higher points and push the sport to a new level. The method does not only apply to skateboarding, but any maneuver involving a human used artefact which has performance related dimensions. Such as, golf, honk ball, or cycling could be optimized similarly; model the artefact, simplify the input, set a performance metric, and optimize for optimal control and dimensions.