Optimisation of off-road motorcycle suspensions

Abstract

Off-road motorcycles are built for commuting, recreation and racing on roads with significant irregularities and elevation changes. The suspension system absorbs large impulses from the road making transit on these terrains comfortable and handleable. Development of calculation and assessment procedures of suspension characteristics have the potential to improve performance of modern suspensions designed by empirical methods, leading to safer, more enjoyable and faster motorcycles. With this motivation, in this thesis we determine calculation and assessment procedures for the optimisation of off-road motorcycles suspensions. We begin by determining appropriate performance metrics. To this end we examine the literature for performance indices, we pre-select seven from the literature and propose two for comparison. We compare them with a three degree-of-freedom model under impulsive and continuous excitations representative of off-road. We find that the best suited metrics for performance assessment are the fourth order generalised mean for the accelerations and eighth order generalised mean for the contact forces. Second, we develop a procedure to estimate contact forces for experimental assessment, motivated by the absence of such a method in the literature. To this end, we derive an overdetermined system of inverse dynamics equations of the motorcycle in the plane. We add a set of constraints to include knowledge of the solution in particular situations. We arrange the problem to solve it efficiently with a non-negative least squares solver and verify the estimations using data from a virtual experiment. We test it with experimental data measured on a motocross track. We find that the estimated contact forces vary consistently with the track, and conclude that is an appropriate tool for performance comparisons. Third, we develop a suitable rider model for the estimator. We consider a passive model, as the literature suggests and we compare it against a basic model derived assuming the fundamental motion of the rider. When tested with experimental data the basic rider yields more realistic than the passive one, providing evidence that passiveness is not a valid assumption for rider modelling and we provided an alternative model for estimation of forces. Fourth, we calculate an optimal suspension to understand its characteristics. To this end, we investigate methods to calculate optimal suspensions to select a suitable one, we apply it to a cross motorcycle, where we optimised stiffness and damping in whoops, braking and acceleration using a sophisticated motorcycle model. We use a multiobjective genetic algorithm to obtain the Pareto fronts. We analyse the correlations between suspension parameters and performance finding that performance in whoops significantly correlated to eight of the ten design variables established, while braking and acceleration are correlated to four each. Also we find that there is conflict between performance in whoops, and braking and accelerating, while not among these two.

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ORCID for Emiliano Rustighi: orcid.org/0000-0001-9871-7795

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