The establishment of ski jumping model based on particle swarm optimization algorithm and ODE solver

Abstract

In ski jumping, studying to determine a reasonable run-up posture can effectively reduce air resistance during the run-up phase, thereby increasing sliding speed. However, most current research focuses on kinematic analysis from video perspectives, with fewer studies examining dynamics. Therefore, this paper proposes a maximum speed take-off model based on particle swarm optimization and ODE solver from a dynamic perspective. First, a dynamic model is established using Newtonian mechanics principles, incorporating dynamic factors for attitude control and determining relevant initial parameters. Then, the sliding process is simulated using an ODE solver, and the optimal attitude control parameters are determined using particle swarm optimization. The final model concludes that squatting during the run-up significantly reduces resistance and increases take-off speed. By further considering aerodynamic effects and refining the model by introducing real-time dynamic data, this model can become a core tool for training in winter sports research.