Automated model complexity monitoring and adjustment for simulation-based design of dynamic systems

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Simulation-based design is needlessly expensive or inaccurate when models of inappropriate complexity are used.  At present, no method exists to check and adjust model complexity throughout a simulation maneuver.  Over the course of a simulation, the required complexity is different during transient as opposed to steady state conditions.  The objectives of the proposed research are thus to 1) develop methods of on-line model monitoring to predict when the model should be updated, 2) predict which model elements must be removed or reinstated, and 3) switch to the updated model without numerical instability.    Such "Active Modelling" is important to the vehicle, robotic, and mechatronic system design industries.  A significant compromise results from using a single model for simulation-based design exercises such as optimization of vehicle suspension parameters over varying terrain.  If the model is too complex (e.g. highly nonlinear multibody model of a vehicle on flat terrain), then computation time will be needlessly high and optimization may be cost-prohibitive.  If the model is too simple to capture complex transients (point mass vehicle model on rough terrain), then predictive ability will be inadequate.    The proposed Active Modelling research will build on the applicant's doctoral and post-doctoral work wherein intra-system power flow was used to decouple dynamic system models and create smaller, more computationally efficient submodels for design.    Metrics based on power flow among system elements will be monitored and used to predict required model changes.  In addition, the model performance, type and location of model inputs, the sensitivity of model responses to the inputs, and variation in the responses themselves will be used to initiate model complexity adjustment.  The work will be facilitated, and the physical insight into the process maximized, by use of graphical model formalisms such as bond graphs.  The work has immediate applications in vehicle design and robotics, but new methodologies will be applicable to any lumped-parameter dynamic system model.