Reduced Order Single Parameter Tuning Using Dynamic Mode Decomposition: An Application in Vehicle Dynamics 2024-26-0274
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.
