The limits of the possible: models of power supply and demand in cycling.

T Olds, K Norton, N Craig, S Olive, E Lowe
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Abstract

This paper outlines a general strategy for mathematical modeling of cycling performance. This strategy involves formulating one expression describing the power available for external work from physiological sources. The variables used in this expression include maximal aerobic power (VO2max), fractional utilisation of VO2max, mechanical efficiency, maximal accumulated oxygen deficit, and the time constants relating to the expression of aerobic and anaerobic capacities. A second expression describing the power demand of cycling is then constructed. The variables used in this expression include the mass, projected frontal area and drag characteristics of the system, the coefficient of rolling resistance, environmental variables such as temperature, barometric pressure, relative humidity, wind speed and direction and the slope of the course. The two expressions are equated and solved using an iterative procedure. Two series of trials were used to assess the predictive accuracy of the model, one using track endurance performances and the other a 26 km road time-trial. The correlations between actual and predicted times have been excellent (0.92-0.95, p < or = 0.0001), with small mean differences (0-1.83% of mean performance time) and mean absolute differences (1.07-3.24%). The model allows us to make predictions about the effect of equipment changes and environmental factors, to compare performances under very different conditions, and to predict the limits of the possible in cycling performance. A range of options designed to improve cycling performance is described.

可能性的限制:循环中的电力供应和需求模型。
本文概述了循环性能数学建模的一般策略。这一策略包括制定一种表达,描述从生理来源获得的外部工作的能力。该表达式中使用的变量包括最大有氧能力(VO2max), VO2max的分数利用率,机械效率,最大累积氧亏,以及与有氧和无氧能力表达相关的时间常数。然后构造了描述循环动力需求的第二个表达式。在这个表达式中使用的变量包括系统的质量、投射锋面面积和阻力特性、滚动阻力系数、环境变量,如温度、气压、相对湿度、风速和风向以及赛道的坡度。这两个表达式是等价的,并使用迭代过程求解。研究人员通过两组试验来评估模型的预测准确性,一组是赛道耐力试验,另一组是26公里公路计时赛。实际时间和预测时间之间的相关性很好(0.92-0.95,p < or = 0.0001),平均差异很小(0-1.83%的平均表现时间),平均绝对差异(1.07-3.24%)。该模型使我们能够预测设备变化和环境因素的影响,比较非常不同条件下的性能,并预测循环性能的可能极限。描述了一系列旨在改善循环性能的选项。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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