Exploration of optimal aerodynamic design space for discus

Abstract This paper proposes a methodology to explore the optimal design space of sports equipment, where non-linear dynamics play a key role, with the goal of gaining creative insights. In the presented example, this space encompasses both the optimal launch conditions and the optimal sizes of a discus to maximize flight distance. The optimization procedure is divided into four steps. The first step calculates the aerodynamic forces acting on a discus using computational fluid dynamics. The second step optimizes the discus flight distance for launch conditions. This step provides the maximum attainable flight distance and the optimal launch conditions for each candidate discus design, that is, the optimal skill of the athlete. The third step is Bayesian optimization of the flight distance. Bayesian optimization identifies the promising design set of design variables, that is, equipment (discus). Finally, the fourth step explores the optimal space using self-organizing maps, providing creative insights for an optimal aerodynamic design space. It was found that discus thickness is the most crucial design variable, with the thinner discus achieving longer flight distances due to the greater maximum lift coefficient. Regarding launch conditions, the initial flight path angle is the most important parameter, ideally about 40 degrees with a narrow tolerance window.

Authors

Institutions

Publication Details

Journal
Sports Engineering
Published
2025-06-30
DOI
https://doi.org/10.1007/s12283-025-00513-8
Citations
1
Primary Topic
Sports Dynamics and Biomechanics
Type
article
Field-Weighted Citation Impact
0.67
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploration of optimal aerodynamic design space for discus

Kazuya Seo, Thomas J. Allen, Ryota Kikuchi
1 citations
Sports Engineering
Sports Dynamics and Biomechanics
0.67
article

Exploration of optimal aerodynamic design space for discus

Kazuya Seo, Thomas J. Allen, Ryota Kikuchi
article en
1 citations

Abstract

Abstract This paper proposes a methodology to explore the optimal design space of sports equipment, where non-linear dynamics play a key role, with the goal of gaining creative insights. In the presented example, this space encompasses both the optimal launch conditions and the optimal sizes of a discus to maximize flight distance. The optimization procedure is divided into four steps. The first step calculates the aerodynamic forces acting on a discus using computational fluid dynamics. The second step optimizes the discus flight distance for launch conditions. This step provides the maximum attainable flight distance and the optimal launch conditions for each candidate discus design, that is, the optimal skill of the athlete. The third step is Bayesian optimization of the flight distance. Bayesian optimization identifies the promising design set of design variables, that is, equipment (discus). Finally, the fourth step explores the optimal space using self-organizing maps, providing creative insights for an optimal aerodynamic design space. It was found that discus thickness is the most crucial design variable, with the thinner discus achieving longer flight distances due to the greater maximum lift coefficient. Regarding launch conditions, the initial flight path angle is the most important parameter, ideally about 40 degrees with a narrow tolerance window.

Sports EngineeringVol. 28(2)
Yamagata University (JP), Kogakuin University (JP), Manchester Metropolitan University (GB)
Openalex Percentile: Top 30%
Sports Dynamics and Biomechanics
0.67
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.