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The challenge

Tracking a swimmer's movement in water is difficult

Traditionally, modelling movement of water relies on a method that divides the water into a 3D grid, modelling movement at each point.

Swimming modelling poses challenges that are difficult for traditional grid methods including large deformations where the swimmer's body interacts with the water, and the splashing of water and bubble entrainment on the water surface.

Our response

A model tailored to individual swimmers

Computational modelling of elite level platform diving and swimming is used to improve athlete performance and to reduce injury risk.

We've worked with the Australian Institute of Sport (AIS) to create a computer model to improve the performance of their Olympic-bound swimmers.

Our model overcomes the challenges associated with traditional movement modelling by using a technique called Smoothed Particle Hydrodynamics (SPH), which describes the flow of fluid as the motion of individual particles.

This means we're able to more accurately simulate the interactions of water with a swimmer by including individual details of each swimmer such as their body shape and the surface of their skin.

The results

Improved lap times and reduced injuries

The detailed information provided by the model allows swimmers to experiment with different stroke techniques to find what works the best.

By making slight changes to the swimming stroke and by re-running our simulations, we're able to find out whether a swimmer swims faster or slower.

We're also able to compare swimming styles between different swimmers to gain scientific insight into how each swimmer is moving through the water, and even look into the effects of superimposing the techniques from different swimmers onto one another.

Our models have also been applied to other water sports such as platform diving, where the modelling is used to improve performance and to reduce injury. In the same way as the model of a swimmer is constructed, a virtual diver model is made from a laser scan of the athlete and video footage of a particular dive.

The performance benefit of small changes to technique can be modelled and improvements to judging score can be estimated. Also, as impact with the water from a 10 metre dive can often lead to injury, we're using the model to determine how small errors in technique can lead to higher forces on joints.

This modelling can also inform injury mitigation strategies for coaches and athletes.

Our technology was used by athletes and coaches at the 2016 Olympic Games in Rio de Janeiro.

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