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The University of Southampton


Computational Biomechanics Based Optimization.

Case study: bicycle chainring optimization

The drive train of a bicycle is remarkably efficient (~98%), however, its interface with the rider is the source of considerable losses. The pseudo-reciprocating forces from the legs and feet are transferred to rotational motion via the cranks and chain ring. Inefficiency is due to out of plane forces and in-plane forces which are non-tangential to the circular path of the pedal. These result in large variations in torque throughout the pedal revolution.

A PSEL study on athlete specific chain ring shape optimization has been funded by the Royal Society . Torque data from instrumented cranks is used to calibrate a musculoskeletal simulation of an athlete. The local velocity of the crank, i.e. the local radius of the chain ring, is varied to minimize muscle loads for a given power output. An 8.9% reduction in muscle activity for 200 W at 90rpm has been predicted for our test athlete.

The methodology can also be applied to rehabilitation (we have developed a possible treatment for patello-femoral pain syndrome).

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