However, studies have also shown that peak power output is not the only important factor to success. 2 Indeed, a cyclist’s velocity is likely to be a much more important factor in the outcome of road cycling sprints. Cycling velocity is the result of power output, aerodynamic drag (CdA), road
Paul F.J. Merkes, Paolo Menaspà and Chris R. Abbiss
Caroline Barelle, Anne Ruby and Michel Tavernier
Aerodynamic properties are one of the factors that determine speed performance in Alpine skiing. Many studies have examined the consequences of this factor in downhill skiing, and the impact of postural modifications on speed is now well established. To date, only wind tunnel tests have enabled one to measure aerodynamic drag values (a major component of the aerodynamic force in Alpine skiing). Yet such tests are incompatible with the constraints of a regular and accurate follow-up of training programs. The present study proposes an experimental model that permits one to determine a skier's aerodynamic drag coefficient (SCx) based on posture. Experimental SCx measurements made in a wind tunnel are matched with the skier's postural parameters. The accuracy of the model was determined by comparing calculated drag values with measurements observed in a wind tunnel for different postures. For postures corresponding to an optimal aerodynamic penetration (speed position), the uncertainty was 13%. Although this model does not permit an accurate comparison between two skiers, it does satisfactorily account for variations observed in the aerodynamic drag of the same skier in different postures. During Alpine ski training sessions and races, this model may help coaches assess the gain or loss in time induced by modifications in aerodynamic drag corresponding to different postures. It may also be used in other sports to help determine whether the aerodynamic force has a significant impact on performance.
A computer model was developed of the aerodynamic drag forces acting to slow down a wheelchair. The model calculated drag forces over a range of wheeling speeds between 2 and 20 m/sec, and for wind conditions over the same range of speeds with wind direction varied between 0° (headwind) and 180° (tailwind). The computer model suggests that the large lateral area of a wheelchair adds considerably to the retarding drag forces at relative wind angles between 0 and 90°. It further suggests that three-wheeled wheelchairs have a considerable aerodynamic advantage over four-wheeled wheelchairs for a wide range of wind speeds and directions. In straight line races, the four-wheeled wheelchair has a slight aerodynamic advantage when the relative wind angle exceeds 90°, but under other speed and wind conditions in this study the three-wheeled wheelchair was more efficient.
Theo Ouvrard, Alain Groslambert, Gilles Ravier, Sidney Grosprêtre, Philippe Gimenez and Frederic Grappe
front of the main group, with their leader directly behind them. Although this strategy is now well established and used automatically by all teams seeking final victory during a Grand Tour, its impact on the main determinants of performance has not been studied yet. The reduction in aerodynamic drag
Levi Heimans, Wouter R. Dijkshoorn, Marco J.M. Hoozemans and Jos J. de Koning
, mechanical resistance of the drive system of the bike, and aerodynamic drag. At 10 m/s, more than 90% of the power loss in track cycling is due to aerodynamic drag. 1 , 2 Consequently, reduction of aerodynamic drag can lead to a significant decrease of the cyclist’s power requirements. With a given maximal
James C. Martin, Douglas L. Milliken, John E. Cobb, Kevin L. McFadden and Andrew R. Coggan
This investigation sought to determine if cycling power could be accurately modeled. A mathematical model of cycling power was derived, and values for each model parameter were determined. A bicycle-mounted power measurement system was validated by comparison with a laboratory ergometer. Power was measured during road cycling, and the measured values were compared with the values predicted by the model. The measured values for power were highly correlated (R 2 = .97) with, and were not different than, the modeled values. The standard error between the modeled and measured power (2.7 W) was very small. The model was also used to estimate the effects of changes in several model parameters on cycling velocity. Over the range of parameter values evaluated, velocity varied linearly (R 2 > .99). The results demonstrated that cycling power can be accurately predicted by a mathematical model.
Jeanick Brisswalter and Christophe Hausswirth
LeRoy W. Alaways, Sean P. Mish and Mont Hubbard
Pitched-baseball trajectories were measured in three dimensions during competitions at the 1996 Summer Olympic games using two high-speed video cameras and standard DLT techniques. A dynamic model of baseball flight including aerodynamic drag and Magnus lift forces was used to simulate trajectories. This simulation together with the measured trajectory position data constituted the components of an estimation scheme to determine 8 of the 9 release conditions (3 components each of velocity, position, and angular velocity) as well as the mean drag coefficient CD and terminal conditions at home plate. The average pitch loses 5% of its initial velocity during flight. The dependence of estimated drag coefficient on Reynolds number hints at the possibility of the drag crisis occurring in pitched baseballs. Such data may be used to quantify a pitcher’s performance (including fastball speed and amount of curve-ball break) and its improvement or degradation over time. It may also be used to understand the effects of release parameters on baseball trajectories.
W. Lee Childers, Tim P. Gallagher, J. Chad Duncan and Douglas K. Taylor
The individual pursuit is a 4-km cycling time trial performed on a velodrome. Parathletes with transtibial amputation (TTA) have lost physiological systems, but this may be offset by the reduced aerodynamic drag of the prosthesis. This research was performed to understand the effect of a unilateral TTA on Olympic 4-km pursuit performance. A forward-integration model of pursuit performance explored the interplay between power loss and aerodynamic gains in parathletes with TTA. The model is calibrated to a 4-km pursuit time of 4:10.5 (baseline), then adjusted to account for a TTA. Conditions simulated were based on typical pedal asymmetry in TTA (AMP), if foot stiffness were decreased (FLEX), if pedaling asymmetries were minimized (ASYM), if the prosthesis were aerodynamically optimized (AERO), if the prosthesis had a cosmetic cover (CC), and if all variables were optimized (OPT). A random Monte Carlo analysis was performed to understand model precision. Four-kilometer pursuit performances predicted by the model were 4:10.5, 4:20.4, 4:27.7, 4:09.2, 4:19.4, 4:27.9, and 4:08.2 for the baseline, AMP, FLEX, ASYM, AERO, CC, and OPT models, respectively. Model precision was ±3.7 s. While the modeled time decreased for ASYM and OPT modeled conditions, the time reduction fell within model precision and therefore was not significant. Practical application of these results suggests that parathletes with a TTA could improve performance by minimizing pedaling asymmetry and/or optimizing aerodynamic design, but, at best, they will have performance similar to that of intact cyclists. In conclusion, parathletes with TTA do not have a net advantage in the individual pursuit.
Grégoire P. Millet, Cyrille Tronche and Frédéric Grappe
To use measurement by cycling power meters (Pmes) to evaluate the accuracy of commonly used models for estimating uphill cycling power (Pest). Experiments were designed to explore the influence of wind speed and steepness of climb on accuracy of Pest. The authors hypothesized that the random error in Pest would be largely influenced by the windy conditions, the bias would be diminished in steeper climbs, and windy conditions would induce larger bias in Pest.
Sixteen well-trained cyclists performed 15 uphill-cycling trials (range: length 1.3–6.3 km, slope 4.4–10.7%) in a random order. Trials included different riding position in a group (lead or follow) and different wind speeds. Pmes was quantified using a power meter, and Pest was calculated with a methodology used by journalists reporting on the Tour de France.
Overall, the difference between Pmes and Pest was –0.95% (95%CI: –10.4%, +8.5%) for all trials and 0.24% (–6.1%, +6.6%) in conditions without wind (>2 m/s). The relationship between percent slope and the error between Pest and Pmes were considered trivial.
Aerodynamic drag (affected by wind velocity and orientation, frontal area, drafting, and speed) is the most confounding factor. The mean estimated values are close to the power-output values measured by power meters, but the random error is between ±6% and ±10%. Moreover, at the power outputs (>400 W) produced by professional riders, this error is likely to be higher. This observation calls into question the validity of releasing individual values without reporting the range of random errors.