BREATHING AND STROKE FREQUENCY STRATEGIES FOR TOP PERFORMANCE

How do you optimise the stroke frequency in swimming? The answer to this question is important in the pursuit of higher performances in the pool. Selecting the right stroke frequency is detrimental for an optimal performance. It should be adjusted to each individual athlete’s characteristics. For running and cycling, we know much on energy consumption and stride frequency. In swimming though, which is a relatively new activity for humans, the optimum stroke frequency is less researched. In this lecture we will look briefly to running and cycling, and the models that explain the optimal human cadence. How these models can be transferred to swimming will be discussed. What we know from swimming studies, optimising stroke frequency for different age- and performance levels swimmers in different strokes will be reviewed. Finally, our research shows that certain stroke rate strategies seem to be ideal during a race. This lecture will examine what stroke rates strategies to choose during a race in order to win

Modelling Hydrodynamic Drag in Swimming using Computational Fluid Dynamics

In the sports field, numerical simulation techniques have been shown to provide useful information about performance and to play an important role as a complementary tool to physical experiments. Indeed, this methodology has produced significant improvements in equipment design and technique prescription in different sports (Kellar et al., 1999; Pallis et al., 2000; Dabnichki & Avital, 2006). In swimming, this methodology has been applied in order to better understand swimming performance. Thus, the numerical techniques have been addressed to study the propulsive forces generated by the propelling segments (Rouboa et al., 2006; Marinho et al., 2009a) and the hydrodynamic drag forces resisting forward motion (Silva et al., 2008; Marinho et al., 2009b). Although the swimmer’s performance is dependent on both drag and propulsive forces, within this chapter the focus is only on the analysis of the hydrodynamic drag. Therefore, this chapter covers topics in swimming drag simulation from a computational fluid dynamics (CFD) perspective. This perspective means emphasis on the fluid mechanics and CFD methodology applied in swimming research. One of the main aims for performance (velocity) enhancement of swimming is to minimize drag forces resisting forward motion, for a given trust. This chapter will concentrate on numerical simulation results, considering the scientific simulation point-of-view, for this practical implication in swimming. In the first part of the chapter, we introduce the issue, the main aims of the chapter and a brief explanation of the CFD methodology. Then, the contribution of different studies for swimming using CFD and some practical applications of this methodology are presented. During the chapter the authors will attempt to present the CFD data and to address some practical concerns to swimmers and coaches, comparing as well the numerical data with other experimental data available in the literature

Swimming propulsion forces are enhanced by a small finger spread

The main aim of this study was to investigate the effect of finger spread on the propulsive force production in swimming using computational fluid dynamics. Computer tomography scans of an Olympic swimmer hand were conducted. This procedure involved three models of the hand with differing finger spreads: fingers closed together (no spread), fingers with a small (0.32 cm) spread, and fingers with large (0.64 cm) spread. Steady-state computational fluid dynamics analyses were performed using the Fluent code. The measured forces on the hand models were decomposed into drag and lift coefficients. For hand models, angles of attack of 0°, 15°, 30°, 45°, 60°, 75°, and 90°, with a sweep back angle of 0°, were used for the calculations. The results showed that the model with a small spread between fingers presented higher values of drag coefficient than did the models with fingers closed and fingers with a large spread. One can note that the drag coefficient presented the highest values for an attack angle of 90° in the three hand models. The lift coefficient resembled a sinusoidal curve across the attack angle. The values for the lift coefficient presented few differences among the three models, for a given attack angle. These results suggested that fingers slightly spread could allow the hand to create more propulsive force during swimming