Browse Publications Technical Papers 2000-01-3576
2000-11-13

Racing Motorcycle Design Process Using Physical and Virtual Testing Methods 2000-01-3576

Recently, the use of laboratory-based physical prototype testing as well as the design of virtual models and virtual test equipment has accelerated the pace and quality of racing vehicle development. In particular, the combined use of both virtual and physical testing, when correlated to racetrack improvements, yields a powerful development tool(1), (2),(3).
In this study, we applied these techniques from the first stages of the design of a unique Grand Prix racing motorcycle. First, a wire-frame CAD model, then a parametric CAD solid model of the motorcycle was created after preliminary calculations specified the approximate design of structural elements. Subsequently, a virtual dynamic model was created and subjected to a variety of inputs, including sine sweeps, shaped white noise and simulated road time-histories. Loads and other dynamic responses were measured on the virtual model, so that it's design could then be optimized to yield acceptable performance and durability. After the first physical prototype was build, the virtual model was correlated to, adjusted and finally verified using laboratory measurements of chassis stiffness and dynamic responses on a servo-hydraulic, full vehicle simulator for motorcycles. Correlation to other component tests, such as shock and fork damping characteristics measured on a shock dyno, was established.
This paper discusses the overall process and descriptions of both the full vehicle laboratory tests and the virtual prototype tests. Several analysis methods, which were used throughout the design of both metal and composite elements, are discussed. Finally, the design optimization process for one component, a new type of motorcycle shock absorber linkage, is discussed in detail. This design optimization was driven by both performance considerations, such as traction and body control on the racetrack simulator, as well as weight optimization using FEA, in conjunction with uni-axial fatigue analysis based on collected strain time-history racetrack data for the instrumented specimen.

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