Effective and efficient simulation-based design is facilitated by models of appropriate complexity. A single model will not have the most appropriate level of complexity throughout all phases of a simulation maneuver if inputs or parameters vary. Ideally, a model for which complexity can be varied as necessary will achieve the best possible trade-off between accuracy and computational efficiency. A method is presented for switching system model elements on and off as their importance changes, and predicting the response of the resulting variable-complexity model. A modified transformer element removes the dynamic output of a model element to the rest of the system when the moving average of its absolute power falls below a user-defined threshold. When the element is “off,” the input from the system to the element is still passed through the transformer so that an estimate of element power and importance can continue to be calculated and the element switched back “on” if necessary. The bond graph formalism is used to facilitate implementation. Switch configurations are defined for both causally weak and causally strong energy storage and dissipative elements. The method is applicable to linear or nonlinear systems that can be modeled with lumped parameter elements. The approach is demonstrated for quarter- and half-car vehicle models subject to a road profile of varying frequency. The appropriate model complexity at all stages is determined and implemented continuously without prior knowledge of input or parameter changes.

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