Vehicle design necessarily involves tuning various parameters to optimize automotive performance metrics like ride and handling. The tuning process is iterative and involves a trial-and-error approach to understand the influence of the input parameters on various output metrics. We develop tuning models and run many simulations to optimize various parameters, followed by validation. This process is computationally expensive and contingent on the output metrics. Alternatively, data-driven modelling could overcome shortcomings but requires extensive data sets to train, which may not be feasible in the initial design phase.In this work, we demonstrate how we can use Dynamic Mode Decomposition, commonly used in Fluid Mechanics, to create Reduced Order Single Parameter Tuning Models, which are computationally lightweight and can provide the output metrics as a function of one tuning parameter. It reduces the tuning time and also helps to understand the system better. We developed reduced order models for various suspension parameters and compared their results with standard simulation results. The prediction accuracy of these models is well above the acceptance criteria, which deems them fit to incorporate in corresponding product development gateways.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Reduced Order Single Parameter Tuning Using Dynamic Mode Decomposition: An Application in Vehicle Dynamics


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:

    Kongress:

    Symposium on International Automotive Technology ; 2024



    Erscheinungsdatum :

    16.01.2024




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Reduced-Order Modeling for Dynamic Mode Decomposition Without an Arbitrary Sparsity Parameter

    Graff, John / Ringuette, Matthew J. / Singh, Tarunraj et al. | AIAA | 2020

    Freier Zugriff

    Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression

    Graff, John / Xu, Xianzhang / Lagor, Francis D. et al. | AIAA | 2019



    AIAA-2019-3072: Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression

    Graff, John / Xu, Xianzhang / Lagor, Francis D. et al. | British Library Conference Proceedings | 2019