Erwin Koninckx

Aerodynamic study of different cyclist positions: CFD analysis and full-scale wind-tunnel tests

Three different cyclist positions were evaluated with Computational Fluid Dynamics (CFD) and wind-tunnel experiments were used to provide reliable data to evaluate the accuracy of the CFD simulations. Specific features of this study are: (1) both steady Reynolds-averaged Navier-Stokes (RANS) and unsteady flow modelling, with more advanced turbulence modelling techniques (Large-Eddy Simulation - LES), were evaluated; (2) the boundary layer on the cyclists surface was resolved entirely with low-Reynolds number modelling, instead of modelling it with wall functions; (3) apart from drag measurements, also surface pressure measurements on the cyclists body were performed in the wind-tunnel experiment, which provided the basis for a more detailed evaluation of the predicted flow field by CFD. The results show that the simulated and measured drag areas differed about 11% (RANS) and 7% (LES), which is considered to be a close agreement in CFD studies. A fair agreement with wind-tunnel data was obtained for the predicted surface pressures, especially with LES. Despite the higher accuracy of LES, its much higher computational cost could make RANS more attractive for practical use in some situations. CFD is found to be a valuable tool to evaluate the drag of different cyclist positions and to investigate the influence of small adjustments in the cyclists position. A strong advantage of CFD is that detailed flow field information is obtained, which cannot easily be obtained from wind-tunnel tests. This detailed information allows more insight in the causes of the drag force and provides better guidance for position improvements.

Computational fluid dynamics analysis of cyclist aerodynamics: performance of different turbulence-modelling and boundary-layer modelling approaches.

This study aims at assessing the accuracy of computational fluid dynamics (CFD) for applications in sports aerodynamics, for example for drag predictions of swimmers, cyclists or skiers, by evaluating the applied numerical modelling techniques by means of detailed validation experiments. In this study, a wind-tunnel experiment on a scale model of a cyclist (scale 1:2) is presented. Apart from three-component forces and moments, also high-resolution surface pressure measurements on the scale models surface, i.e. at 115 locations, are performed to provide detailed information on the flow field. These data are used to compare the performance of different turbulence-modelling techniques, such as steady Reynolds-averaged Navier-Stokes (RANS), with several k-epsilon and k-omega turbulence models, and unsteady large-eddy simulation (LES), and also boundary-layer modelling techniques, namely wall functions and low-Reynolds number modelling (LRNM). The commercial CFD code Fluent 6.3 is used for the simulations. The RANS shear-stress transport (SST) k-omega model shows the best overall performance, followed by the more computationally expensive LES. Furthermore, LRNM is clearly preferred over wall functions to model the boundary layer. This study showed that there are more accurate alternatives for evaluating flow around bluff bodies with CFD than the standard k-epsilon model combined with wall functions, which is often used in CFD studies in sports.

Computational fluid dynamics analysis of drag and convective heat transfer of individual body segments for different cyclist positions.

This study aims at investigating drag and convective heat transfer for cyclists at a high spatial resolution. Such an increased spatial resolution, when combined with flow-field data, can increase insight in drag reduction mechanisms and in the thermo-physiological response of cyclists related to heat stress and hygrothermal performance of clothing. Computational fluid dynamics (steady Reynolds-averaged Navier-Stokes) is used to evaluate the drag and convective heat transfer of 19 body segments of a cyclist for three different cyclist positions. The influence of wind speed on the drag is analysed, indicating a pronounced Reynolds number dependency on the drag, where more streamlined positions show a dependency up to higher Reynolds numbers. The drag and convective heat transfer coefficient (CHTC) of the body segments and the entire cyclist are compared for all positions at racing speeds, showing high drag values for the head, legs and arms and high CHTCs for the legs, arms, hands and feet. The drag areas of individual body segments differ markedly for different cyclist positions whereas the convective heat losses of the body segments are found to be less sensitive to the position. CHTC-wind speed correlations are derived, in which the power-law exponent does not differ significantly for the individual body segments for all positions, where an average value of 0.84 is found. Similar CFD studies can be performed to assess drag and CHTCs at a higher spatial resolution for applications in other sport disciplines, bicycle equipment design or to assess convective moisture transfer.

Cyclist drag in team pursuit: influence of cyclist sequence, stature, and arm spacing.

In team pursuit, the drag of a group of cyclists riding in a pace line is dependent on several factors, such as anthropometric characteristics (stature) and position of each cyclist as well as the sequence in which they ride. To increase insight in drag reduction mechanisms, the aerodynamic drag of four cyclists riding in a pace line was investigated, using four different cyclists, and for four different sequences. In addition, each sequence was evaluated for two arm spacings. Instead of conventional field or wind tunnel experiments, a validated numerical approach (computational fluid dynamics) was used to evaluate cyclist drag, where the bicycles were not included in the model. The cyclist drag was clearly dependent on his position in the pace line, where second and subsequent positions experienced a drag reduction up to 40%, compared to an individual cyclist. Individual differences in stature and position on the bicycle led to an intercyclist variation of this drag reduction at a specific position in the sequence, but also to a variation of the total drag of the group for different sequences. A larger drag area for the group was found when riding with wider arm spacing. Such numerical studies on cyclists in a pace line are useful for determining the optimal cyclist sequence for team pursuit.

Effect of a novel pedal design on maximal power output and mechanical efficiency in well-trained cyclists

Abstract In this study, we evaluated the effects of a novel pedal design, characterized by a downward and forward shift of the cleat fixing platform relative to the pedal axle, on maximal power output and mechanical efficiency in 22 well-trained cyclists. Maximal power output was measured during a series of short (5-s) intermittent sprints on an isokinetic cycle ergometer at cadences from 40 to 120 rev · min−1. Mechanical efficiency was evaluated during a submaximal incremental exercise test on a bicycle ergometer using continuous [Vdot]O2 and [Vdot]CO2 measurement. Similar tests with conventional pedals and the novel pedals, which were mounted on the individual racing bike of the participant, were randomized. Maximal power was greater with novel pedals than with conventional pedals (between 6.0%, sx = 1.5 at 40 rev · min−1 and 1.8%, sx = 0.7 at 120 rev · min−1; P = 0.01). Torque production between crank angles of 60° and 150° was higher with novel pedals than with conventional pedals (P = 0.004). The novel pedal design did not affect whole-body [Vdot]O2 or [Vdot]CO2. Mechanical efficiency was greater with novel pedals than with conventional pedals (27.2%, sx = 0.9 and 25.1%, sx = 0.9% respectively; P = 0.047; effect size = 0.9). In conclusion, the novel pedals can increase maximal power output and mechanical efficiency in well-trained cyclists.