Modeling Sprint Cycling Using Field-Derived Parameters and Forward Integration


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Abstract

We previously reported that a mathematical model could accurately predict steady-state road-cycling power when all the model parameters were known. Application of that model to competitive cycling has been limited by the need to obtain accurate parameter values, the non-steady-state nature of many cycling events, and because the validity of the model at maximal power has not been established.Purpose:We determined whether modeling parameters could be accurately determined during field trials and whether the model could accurately predict cycling speed during maximal acceleration using forward integration.Methods:First, we quantified aerodynamic drag area of six cyclists using both wind tunnel and field trials allowing for these two techniques to be compared. Next, we determined the aerodynamic drag area of three world-class sprint cyclists using the field-test protocol. Track cyclists also performed maximal standing-start time trials, during which we recorded power and speed. Finally, we used forward integration to predict cycling speed from power-time data recorded during the maximal trials allowing us to compare predicted speed with measured speed.Results:Field-based values of aerodynamic drag area (0.258 ± 0.006 m2) did not differ (P = 0.53) from those measured in a wind tunnel (0.261 ± 0.006 m2). Forward integration modeling accurately predicted cycling speed (y = x, r2 = 0.989) over the duration of the standing-start sprints.Conclusions:Field-derived values for aerodynamic drag area can be equivalent to values derived from wind tunnel testing, and these values can be used to accurately predict speed even during maximal-power acceleration by world-class sprint cyclists. This model could be useful for assessing aerodynamic issues and for predicting how subtle changes in riding position, mass, or power output will influence cycling speed.

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