Designing new suspension systems is a complex task which affects directly the tire behavior, impacting the vehicle balance and final performance. As computational resources are getting cheaper and more powerful, simulation tools are being used together with optimization techniques that allow the exploitation of such resources. This work presents a framework for simulation and optimizations of the kinematics of a suspension system. An evolutionary multi-objective optimization algorithm is implemented along with a suspension kinematics simulation software that evaluates the solutions and retrieves the final best compromise between the given objectives inside the design space given to the algorithm. The dependence of the kinematic objectives is modeled through a set of weight functions and scaling factors. Considering the multi-objective nature of this problem and the complex relation between the input and output variables along with the conflicting objective responses, a genetic algorithm was chosen as the optimization approach. The developed framework can potentially reduce the design time by returning a single final sub-optimal solution based on the set of objectives and their respective weights and scaling factors.


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    Titel :

    Multi-objective Optimization of Suspension Kinematics of a Race Car


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Orlova, Anna (Herausgeber:in) / Cole, David (Herausgeber:in) / Avi, Ariel Gustavo (Autor:in) / Carboni, Andrea Piga (Autor:in) / Rouelle, Claude (Autor:in)

    Kongress:

    The IAVSD International Symposium on Dynamics of Vehicles on Roads and Tracks ; 2021 August 17, 2021 - August 19, 2021



    Erscheinungsdatum :

    06.08.2022


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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