Riassunto analitico
The present essay finds its application in the optimization studies of the Ferrari F1 wind tunnel model suspension, during my internship with thesis development.
The main purpose has been to optimize the design of the suspension, resulting in an automatic process which minimizes the differences between the wind tunnel model and the actual F1 car. This need is due to two important factors: firstly, the structural difference between the WT model and the F1 car is such that the kinematic picking points cannot be just scaled, resulting in the need of developing a different kinematic which may alter the correlation between the WT aerodynamic data and the track, secondly, one different suspension corner may need to be tested week-by-week, requiring a fast design process.
The actual methodology is manual and involves an operator which, based on his know-how, defines the model scale kinematic points such that the kinematic parameters are as similar as possible to the F1 car, in a no-steering condition.
It has been necessary to realize a tool ('Kinematic tool'), which manages the design phases, with an embedded multi-objective optimization algorithm based on evolutionary strategies which replaces the operator and optimizes the kinematic points such that the errors on the various objectives are minimized, with each point constrained into a possible design space. In this way, the kinematic lines are automatically designed, considering all the steering conditions, allowing a performance improvement and an operator time reduction. The tool is the extension of a previous thesis project where a function which solves the kinematic of the suspension starting from the picking points position has been developed.
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