The track bound sliding sport of Skeleton was permanently added to Winter Olympics programme in 2002. This has led to increased interest in the sport. Engineering has already proved to be a vital contributor to improved performance in the related sport of Bobsleighing. We hope that engineering can do the same for Skeleton. This report describes an attempt at developing a platform to be used as a real-time training simulator for the sport of Skeleton. For a multitude of reasons athletes are, on average limited to a total of two hours of practice and competitive on-track time in any given year. When compared with time spent practising and in competition in most sports, this is extremely low. It is hypothesized that a simulator can augment track time by providing a realistic environment to practise in, even when access to a track is not available. This work is guided by simulators that have been developed for Bobsleighing. The main components are the models to describe the dynamics of the sport, an input method and visualization of the simulation. The main considerations for the dynamic model are of the track surface, the sled and contact between sled and track surface. These models lead to a system of equations which when solved provide accelerations and contact forces. The accelerations are integrated over a fixed time interval to determine changes in velocities, position and orientation. The position and orientation obtained after the integration is passed on to a game engine which provides the user with real-time visual output of the position and orientation along a digitally recreated track surface. A video game controller was chosen to serve as the input device. It has two joysticks, which can be mapped so as to mimic the forces applied by an athlete. A number of descents were performed using this platform both at real-time speed and at a slower speed to give the user, unfamiliar with the sport, a better chance to steer the sled. We were able to consistently reach the exit of curve 2 in real-time speed and curve 4 at the slower play speed before failure of the simulation. In most cases the algorithm used here proves to take lesser time for computation than the chosen integration time step, which is a great sign for future development as we did not make any attempts to optimise its omputation
time. We made an attempt at validation using time elapsed to traverse a certain distance and the sum of magnitude of Lagrangian multipliers. We had poor results with the time elapsed comparison, with simulated runs being 15% slower than competitive descents. While the sum of Lagrangian multipliers showed good relation to expected behaviour. This first attempt was reasonably successful, and we believe that the lessons learnt from this work has brought us one step closer to realizing a training simulator that can be useful to Skeleton athletes.