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Aerodynamic characteristics and trajectory analysis of badminton shuttlecocks

Zhou Lin

Acta of Bioengineering and Biomechanics

2024

Vol. 26, no. 4

29--37

This study aimed to investigate the aerodynamic characteristics and trajectory behavior of badminton shuttlecocks, focusing on the effects of design factors such as porosity, flexibility, and feather geometry on flight performance. The main research question was how shuttlecock design influences aerodynamic forces and resulting trajectories. Methods: Wind tunnel tests were conducted on two feather and two synthetic shuttlecocks to measure drag, lift, and pitching forces across speeds of 10–50 m/s and angles of 0–20°. Empirical correlations for drag and lift coefficients were derived via regression analysis. The effects of gaps and rotation were evaluated by modifying shuttlecocks. Trajectories were simulated by numerically integrating the equations of motion using the empirical force correlations and validated against high-speed video of players hitting shuttlecocks. Results: Premium shuttlecocks displayed lower drag and higher lift than budget models. Feather shuttlecocks maintained higher rotation rates at high speeds compared to synthetic ones. Sealing gaps reduced drag by up to 10% for 75% sealed gaps. Stiffening synthetic skirts improved performance closer to feather shuttlecocks. Simulations matched experimental trajectories within 5% deviation for key metrics across different shots and shuttlecock types. Conclusions: Shuttlecock design significantly impacts aerodynamic forces and flight trajectories. Factors such as porosity, skirt flexibility and feather shape play crucial roles in performance. The developed simulation methodology can aid players in optimizing shots and manufacturers in designing better shuttlecocks. This research enhances understanding of shuttlecock aerodynamics and provides a foundation for future equipment innovations in badminton.

Finding playing styles of badminton players using firefly algorithm-based clustering algorithms

Ilankoon I.M.T.P.K., Samarasinghe U.S., Ariyaratne M.K.A., Silva R. M.

Computer Science

2023

T. 24 (3)

427--450

Cluster analysis can be defined as applying clustering algorithms with the goal of finding any hidden patterns or groupings in a data set. Different clustering methods may provide different solutions for the same data set. Traditional clustering algorithms are popular, but handling big data sets is beyond the abilities of such methods. We propose three big data clustering methods basedon the firefly algorithm (FA). Three different fitness functions were definedon FA using inter-cluster distance, intra-cluster distance, silhouette value, and the Calinski-Harabasz index. The algorithms find the most appropriate cluster centers for a given data set. The algorithms were tested with nine popular synthetic data sets and one medical data set and are later applied on two badminton data sets with the intention of identifying the different playing styles of players based on their physical characteristics. The results specify that the firefly algorithm could generate better clustering results with high accuracy. The algorithms cluster the players to find the most suitable playing strategy for a given player where expert knowledge is needed in labeling the clusters. Comparisons with a PSO-based clustering algorithm (APSO) and traditional algorithms point out that the proposed firefly variants work in a similar fashion as the APSO method, and they surpass the performance of traditional algorithms.