Optimization of pacing strategies for variable wind conditions in road cycling. (September 2017)

• Record Type: Journal Article
• Authors: Sundström, David; Bäckström, Mikael
• Other Names: Goff John Eric guest-editor.
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 231:Number 3(2017:Sep.)
• Page Start: 184
• Page End: 199
• Publication Date: 2017-09
• Subjects: Pacing strategy -- pacing -- performance -- bioenergetics -- road cycling -- wind -- optimization; Sports sciences -- Periodicals; Sporting goods -- Design and construction -- Periodicals; Sports -- Technological innovations -- Periodicals; Biomechanics -- Periodicals; 613.71
• DOI: 10.1177/1754337117700550
• Languages: English
• ISSNs: 1754-3371

Abstract

It has been shown theoretically that performance can be enhanced by varying power in parallel with variable ambient conditions. However, no theoretical model has considered aerobic substrate utilization dynamics, limited carbohydrate stores, force–velocity relationships, and proper efficiency modelling. Furthermore, no study has investigated optimal pacing for courses with continuously variable ambient wind directions. Therefore, the aim of this study was to develop a model for the optimization of pacing strategies in road cycling with an updated bioenergetic model. The purpose of this model was to optimize pacing strategies for courses with continuously variable wind directions on both short (2 km) and long (100 km) courses. For this purpose, a numerical model consisting of three sub-models was programmed into the MATLAB software. This model consisted of one mechanical simulation model for cycling locomotion, one bioenergetic model based on the Margaria–Morton–Sundström model, and the method of moving asymptotes for optimization of the pacing strategy. Results showed that by optimizing the pacing strategies, time gains of 4.9% and 5.7% were attained for the 2-km courses with and without an ambient wind of 5 m·s −1, respectively. The corresponding time gains for the 100-km courses were 1.4% and 2.0% with and without ambient wind, respectively. The theoretical model in this study further resulted in all-out strategies for the flat 2-km courses with and without ambient wind. Moreover, the 100-km course without wind was met with a positive pacing strategy and the 100-km course with ambient wind was met with a compromise of positive pacing and variable power distribution in parallel with the variable ambient wind conditions. In conclusion, the model presented in this study performed more detailed bioenergetic simulations than previous pacing strategy optimization studies, and this resulted in more detailed pacing strategies for long courses.