Aerodynamic interaction between in-line runners: new insights on the drafting strategy in running

This study aims at modelling the aerodynamic interaction between a world-class runner and several pacers running in line, the objective being to determine the best drafting position in terms of potential speed gain and running time. Computational Fluid Dynamics calculations were performed to determine the aerodynamic drag forces exerted on the runners. Secondly, we estimated the metabolic savings for each of the runner's positions in the pack of pacers. Finally, we estimated a potential equivalent running speed and a corresponding running time gain for each of the runner's positions. Running second in a group of 5 runners would be the most effective drafting strategy, with a reduction of aerodynamic drag reaching 63.3%, corresponding to an improvement of 4.4% of the running economy. Furthermore, decreasing the drag forces acting on the runner would results in greater running speed. For example, a 63.3% reduction in the aerodynamic forces yields a 2.9% faster running speed (20.7 to 21.31 km/h) for an equal metabolic cost. Running in the wake of a leading runner (in a pack of five runners) over a marathon is estimated to provide a significant time saving of 3 min 28 s.

Aerodynamic analysis of uphill drafting in cycling

Some teams aiming for victory in a mountain stage in cycling take control in the uphill sections of the stage. While drafting, the team imposes a high speed at the front of the peloton defending their team leader from opponent’s attacks. Drafting is a well-known strategy on flat or descending sections and has been studied before in this context. However, there are no systematic and extensive studies in the scientific literature on the aerodynamic effect of uphill drafting. Some studies even suggested that for gradients above 7.2% the speeds drop to 17 km/h and the air resistance can be neglected. In this paper, uphill drafting is analyzed and quantified by means of drag reductions and power reductions obtained by computational fluid dynamics simulations validated with wind tunnel measurements. It is shown that even for gradients above 7.2%, drafting can yield substantial benefits. Drafting allows cyclists to save over 7% of power on a slope of 7.5% at a speed of 6 m/s. At a speed of 8 m/s, this reduction can exceed 16%. Sensitivity analyses indicate that significant power savings can be achieved, also with varying bicycle, cyclist, road and environmental characteristics.

Aerodynamics of Cycling Skinsuits Focused on the Surface Shape of the Arms

In cycling, air resistance corresponds to 90% of the resistance on the bicycle and cyclist and 70% of this is applied to the body of the cyclist. Despite research on postures that could reduce air resistance, few studies have been conducted on full-body cycling suits. As the aerodynamics of the surface shape of clothing fabric are still unclear, the airflow around cyclists and air resistance were examined using a computational fluid dynamics (CFD) method and wind tunnel experiment. Specifically, in this study, we focused on how different surface shapes of cycling suit fabrics affect air resistance. CFD results indicate that air resistance during a race was high at the head, arms and legs of the cyclist. In the wind tunnel experiment, a cylinder model resembling the arms was used to compare the aerodynamic forces of various fabrics and the results showed that air resistance changed according to the fabric surface shape. Moreover, by changing the fabric shape of the arms of the cycling suits, reduction of air resistance by up to 8% is achievable. These results suggest that offering the most appropriate suit type to each cyclist, considering race conditions, can contribute to further improvement in their performance.

Evaluation of strategy and tactics in cycling: a systematic review of evaluation methods and possible performance implications

INTRODUCTION

Cycling performance is affected by many factors and is the expression of a multitude of variables. Different studies aiming to describe variables determining cycling performance are focused mainly on metabolic efficiency optimization and mechanical efficiency optimization. Strategy and tactics analysis in cycling represent a key additional performance variable, however, the knowledge of methods to assess these parameters and the possible performance implications is low. The main purposes of the study were to systematically review the state of the art related to strategy and tactics analysis in cycling and describe and analyze the possible implications and possible evaluation methods of tactics and strategy in cycling.

EVIDENCE ACQUISITION

MEDLINE®/PubMed and Scopus databases were searched with additional integration from external sources, between March and April 2020. To meet the inclusion criteria, studies published from 2000 to 2020 that evaluated the impact of strategies and/or tactics on cycling performance or aimed to study and develop strategy and/or tactic models to improve cycling performance were selected.

EVIDENCE SYNTHESIS

Starting from the 12972 identified records, totally 22 studies met the inclusion criteria and were included in the current systematic review. Studies emerged from the selection focused mainly on time trials strategies analysis (54.55%), track cycling strategy analysis (22.73%) and other cycling disciplines strategy evaluation (road cycling, mountain bike, cyclocross; 22.73%). According to the studies' objectives, four main topics of investigation emerged from the research: evaluation of the impact of different starting strategies on time-trial performance; evaluation of different pacing strategies on performance; evaluation of aerodynamics and drag coefficients according to racing strategy in team pursuit; application of video analysis or strategy/tactics effect on performance.

CONCLUSIONS

Strategy and tactics analysis in cycling represent a key additional performance variable to add to the traditionally more studied and analyzed parameters. However, few studies deeply analyzed these variables. Future works may focus on these aspects to investigate strategy and tactics insights and application of evaluation methods in cycling.

Aerodynamic benefits for a cyclist by drafting behind a motorcycle

Motorcycles are present in cycling races for reasons including television broadcasting. During parts of the race, these motorcycles ride in front of individual or groups of cyclists. Concerns have been expressed in the professional cycling community that these motorcycles can provide aerodynamic benefits in terms of drag reduction for the cyclists drafting behind them. However, to the best of our knowledge, no information about the extent of these benefits is present in the scientific literature. Therefore, this paper analyses the potential drag reduction for a cyclist by drafting behind a motorcycle. Wind tunnel measurements and numerical simulations with computational fluid dynamics were performed. It was shown that drafting at separation distances d = 2.64, 10, 30 and 50 m can reduce the drag of the cyclist down to 52, 77, 88 and 93% of that of an isolated cyclist, respectively. A cyclist power model is used to convert these drag reductions into potential time gains. For a non-drafting cyclist at a speed of 54 km/h on level road in calm weather, the time gains by drafting at d = 2.64, 10, 30 and 50 m are 12.7, 5.4, 2.7 and 1.6 s per km, respectively. These time differences can influence the outcome of cycling races. The current rules of the International Cycling Union do not prevent these aerodynamic benefits from occurring in races.

Brachistochrone on a velodrome

The Brachistochrone problem, which describes the curve that carries a particle under gravity in a vertical plane from one height to another in the shortest time, is one of the most famous studies in classical physics. There is a similar problem in track cycling, where a cyclist aims to find the trajectory on the curved sloping surface of a velodrome that results in the minimum lap time. In this paper, we extend the classical Brachistochrone problem to find the optimum cycling trajectory in a velodrome, treating the cyclist as an active particle. Starting with two canonical cases of cycling on a sloping plane and a cone, where analytical solutions are found, we then solve the problem numerically on the reconstructed surface of the velodrome in Montigny le Bretonneux, France. Finally, we discuss the parameters of the problem and the effects of fatigue.

On the aerodynamic flow around a cyclist model at the hoods position

Abstract

Aerodynamic flow around an 1/5 scale cyclist model was studied experimentally and numerically. First, measurements of drag force were performed for the model in a low-speed wind tunnel at Reynolds numbers from $$5.5 \times 10^{4}$$5.5×104 to $$1.8 \times 10^{5}$$1.8×105. Meanwhile, numerical computation using a large eddy simulation method was performed at three Reynolds numbers of $$1.1 \times 10^{4}$$1.1×104, $$6.5 \times 10^{4}$$6.5×104 and $$1.5 \times 10^{5}$$1.5×105 to obtain the drag coefficients for comparison. Second, flow visualization was made in a water channel and the wind tunnel mentioned to examine the three-dimensional flow separation pattern on the model surface, which could also be realized from the numerical results. Finally, a wake flow survey based on the hot-wire measurements in the wind tunnel showed that in the near-wake region, the flow was featured with the formation of multiple streamwise vortices. The numerical results further indicated that these vortices were evolved from the separated flows occurred on the model surface.

Graphic Abstract

The variations on the aerodynamics of a world-ranked wheelchair sprinter in the key-moments of the stroke cycle: A numerical simulation analysis

Biomechanics plays an important role helping Paralympic sprinters to excel, having the aerodynamic drag a significant impact on the athlete’s performance. The aim of this study was to assess the aerodynamics in different key-moments of the stroke cycle by Computational Fluid Dynamics. A world-ranked wheelchair sprinter was scanned on the racing wheelchair wearing his competition gear and helmet. The sprinter was scanned in three different positions: (i) catch (hands in the 12h position on the hand-rim); (ii) the release (hands in the 18h position on the hand-rim) and; (iii) recovery phase (hands do not touch the hand-rim and are hyperextended backwards). The simulations were performed at 2.0, 3.5, 5.0 and 6.5 m/s. The mean viscous and pressure drag components, total drag force and effective area were retrieved after running the numerical simulations. The viscous drag ranged from 3.35 N to 2.94 N, pressure drag from 0.38 N to 5.51 N, total drag force from 0.72 N to 8.45 N and effective area from 0.24 to 0.41 m². The results pointed out that the sprinter was submitted to less drag in the recovery phase, and higher drag in the catch. These findings suggest the importance of keeping an adequate body alignment to avoid an increase in the drag force.

Qualitative Video Analysis of Track-Cycling Team Pursuit in World-Class Athletes

Context: Track-cycling team pursuit (TP) is a highly technical effort involving 4 athletes completing 4 km from a standing start, often in less than 240 s. Transitions between athletes leading the team are obviously of utmost importance. Purpose: To perform qualitative video analyses of transitions of world-class athletes in TP competitions. Methods: Videos captured at 100 Hz were recorded for 77 races (including 96 different athletes) in 5 international track-cycling competitions (eg, UCI World Cups and World Championships) and analyzed for the 12 best teams in the UCI Track Cycling TP Olympic ranking. During TP, 1013 transitions were evaluated individually to extract quantitative (eg, average lead time, transition number, length, duration, height in the curve) and qualitative (quality of transition start, quality of return at the back of the team, distance between third and returning rider score) variables. Determination of correlation coefficients between extracted variables and end time allowed assessment of relationships between variables and relevance of the video analyses. Results: Overall quality of transitions and end time were significantly correlated (r = .35, P = .002). Similarly, transition distance (r = .26, P = .02) and duration (r = .35, P = .002) were positively correlated with end time. Conversely, no relationship was observed between transition number, average lead time, or height reached in the curve and end time. Conclusion: Video analysis of TP races highlights the importance of quality transitions between riders, with preferably swift and short relays rather than longer lead times for faster race times.

Optimizing the Team for Required Power During Track-Cycling Team Pursuit

Purpose: Since the aim of the men’s team pursuit in time-trial track cycling is to accomplish a distance of 4000 m as fast as possible, optimizing aerodynamic drag can contribute to achieving this goal. The aim of this study was to determine the drafting effect in second, third, and fourth position during the team pursuit in track cycling as a function of the team members’ individual frontal areas in order to minimize the required power. Method: Eight experienced track cyclists of the Dutch national selection performed 39 trials of 3 km in different teams of 4 cyclists at a constant velocity of 15.75 m/s. Frontal projected areas were determined, and together with field-derived drag coefficients for all 4 positions, the relationships between frontal areas of team members and drag fractions were estimated using generalized estimating equations. Results: The frontal area of both the cyclist directly in front of the drafter and the drafter himself turned out to be significant determinants of the drag fraction at the drafter’s position (P < .05) for all 3 drafting positions. Predicted required power for individuals in drafting positions differed up to 35 W depending on team composition. For a team, a maximal difference in team efficiency (1.2%) exists by selecting cyclists in a specific sequence. Conclusion: Estimating required power for a specific team composition gives insight into differences in team efficiency for the team pursuit. Furthermore, required power for individual team members ranges substantially depending on team composition.