A Quantitative Approach for Tuning a Mountain Bike Suspension
A method for tuning the spring rate and damping rate of a mountain bike suspension based on a data-driven procedure is presented. The design and development of a custom data acquisition system, known as the MTB~DAQ, capable of measuring acceleration data at the front and rear axles of a bike are discussed. These data are input into a model that is used to calculate the vertical acceleration and pitching angular acceleration response of the bike and rider. All geometric and dynamic properties of the bike and rider system are measured and built into the model. The model is tested and validated using image processing techniques. A genetic algorithm is implemented with the model and used to calculate the best spring rate and damping rate of the mountain bike suspension such that the vertical and pitching accelerations of the bike and rider are minimized for a given trail. Testing is done on a variety of different courses and the performance of the bike when tuned to the results of the genetic algorithm is discussed. While more fine tuning of the model is possible, the results show that the genetic algorithm and model accurately predict the best suspension settings for each course necessary to minimize the vertical and pitching accelerations of the bike and rider.