Dynamic leg-motion and its effect on the aerodynamic performance of cyclists

Crouch, Timothy; Burton, David; Thompson, Mark C.; Brown, Nicholas; Sheridan, John

doi:10.1016/j.jfluidstructs.2016.05.007

Cited by 55 publications

(39 citation statements)

References 18 publications

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“…A region of strong downwash behind the curved back of the mannequin (y ~ 120 cm) is observed in Fig. 6-right, with a peak vertical velocity of v/U ∞ ~ − 0.17 that agrees well with literature (Crouch et al 2016;Griffith et al 2014, among others). Two distinct counter-rotating vortices (marked T1/T2 in Fig.…”

Section: Wake Flow Topologysupporting

confidence: 88%

“…The minimum value of the streamwise velocity is comparatively higher than that measured by Crouch et al (2014, u A second region of high velocity deficit is observed downstream of the wheel axis and the drivetrain configuration (y ~ 40 cm). In this region the minimum velocity of u/U ∞ ~ 0.45 matches well the work of Crouch et al (2016) in terms of location and magnitude, despite the differences between the respective models.…”

Section: Wake Flow Topologysupporting

confidence: 60%

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Aerodynamic drag determination of a full-scale cyclist mannequin from large-scale PTV measurements

2019

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The aerodynamic drag of a human-scale wind tunnel model is obtained from large-scale particle tracking velocimetry measurements invoking the conservation of momentum in a control volume surrounding the model. Lagrangian particle tracking is employed to obtain the velocity and static pressure statistics in a thin volume in the wake of a cyclist mannequin at freestream velocities between 12.5 and 15 m/s, corresponding to Reynolds numbers from 5 × 10 5 to 6 × 10 5 based on the torso length. The spatial distributions of the time-average streamwise velocity and pressure coefficient match well with previous works reported in literature. The streamwise velocity fluctuations in the wake of the cyclist's model are presented, clearly demonstrating the unsteady nature of the main wake flow structures. Furthermore, the obtained aerodynamic drag follows the expected quadratic increase with increasing freestream velocity. The accuracy of this drag estimation is evaluated by comparison to force balance data and corresponds to 30 drag counts. The three terms composing the overall drag force, ascribed to the mean and fluctuating streamwise velocity and the mean pressure, are also evaluated separately, demonstrating that the resistive force is dominated by the contribution of the mean streamwise momentum deficit, whereas the contribution of the pressure term is negligible. Graphical abstractThe data presented in the figures in this work is available online (https ://doi.org/10.4121/uuid:7f42e 060-766d-4bf1-86c4-c648c 79431 7c)

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Section: Wake Flow Topologysupporting

confidence: 88%

Section: Wake Flow Topologysupporting

confidence: 60%

Aerodynamic drag determination of a full-scale cyclist mannequin from large-scale PTV measurements

2019

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“…Over 60% of the variation in drag with leg position could be accounted for solely by the large change in the pressure distribution on the back and hips throughout the crank cycle. These findings have also been supported by recent studies that include the dynamic motion of the legs for realistic racing cadences [13,16,17]. These studies show that the large-scale wake structures identified in quasi-steady experiments are still the dominant flow features at elite-level time-trial speeds and cadences.…”

Section: Cyclist Wake Structure and Major Flow Regimessupporting

confidence: 78%

“…Experimental studies by Crouch et al [12,13] and numerical investigations by Griffith et al [14] have recently provided insight into the nature of the flow and the origin of the aerodynamic forces at play for cyclists. These studies utilised a full-scale mannequin in a time-trial position, and a numerical cyclist model of a similar geometry to visualise and quantify the development of the large-scale flow structures that develop in the wake over the course of a pedal stroke.…”

Section: Cyclist Wake Structure and Major Flow Regimesmentioning

confidence: 99%

Riding against the wind: a review of competition cycling aerodynamics

et al. 2017

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Aerodynamics has such a profound impact on cycling performance at the elite level that it has infiltrated almost every aspect of the sport from riding position and styles, equipment design and selection, race tactics and training regimes, governing rules and regulations to even the design of new velodromes. This paper presents a review of the aspects of aerodynamics that are critical to understanding flows around cyclists under racing conditions, and the methods used to evaluate and improve aerodynamic performance at the elite level. The fundamental flow physics of bluff body aerodynamics and the mechanisms by which the aerodynamic forces are imparted on cyclists are described. Both experimental and numerical techniques used to investigate cycling aerodynamic performance and the constraints on implementing aerodynamic saving measures at the elite level are also discussed. The review reveals that the nature of cycling flow fields are complex and multi-faceted as a result of the highly three-dimensional and variable geometry of the human form, the unsteady racing environment flow field, and the non-linear interactions that are inherent to all cycling flows. Current findings in this field have and will continue to evolve the sport of elite cycling while also posing a multitude of potentially fruitful areas of research for further gains in cycling performance.

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“…The static mannequin obeys a fixed crank angle of 75° as detailed in Table 2 which summarizes the characteristic model dimensions. As discussed in Crouch et al (2016), the use of a static model yields a wake flow that is highly representative of the phase-averaged flow field of a pedalling athlete, while greatly simplifying the experimental setup. The cyclist wears a long-sleeve Etxeondo time trial suit along with a Giant time trial helmet, as used by Team Giant Alpecin during the 2016 season.…”

Section: Cyclist Modelmentioning

confidence: 99%