The effectiveness of modelling and simulation, which has become a routine practice in science and engineering, is heavily dependent on the availability of comprehensive dynamic system models of appropriate fidelity. A simple model can generate results in short time but with poor accuracy; conversely, a complex model can generate accurate predictions but typically requires long computation times that cannot be tolerated for elaborate optimization studies. An accurate yet sufficiently complex dynamic model can generate responses in a short time by including only the important underlying physics based on the variables of interest. Such a model is termed a "proper model" and is defined as the minimal complexity model of a dynamic system, with physically meaningful parameters, which accurately predicts dynamic system outputs.Proper models can be generated by an energy-based model reduction methodology that removes unnecessary complexity from models (linear or nonlinear) without altering the physical meaning of the remaining parameters and variables. Our previous work on such methodology developed the Model Order Reduction Algorithm (MORA), and introduced the Element Activity metric. MORA was originally implemented on automotive systems and produced very promising results in reducing the model complexity and computational cost. However, the methodology requires further developments to address the importance of complexity related with kinematic constraints and output/input-weighted simplifications and to increase computational efficiency. We propose to extend the methodology to overcome these shortcomings. The focus of this project is to aplly the methodology along with the developed extensions to other areas such as mechatronic systems, haptic devices, and fuel cells. At the completion of this work, the available reduction algorithm will be able to generate proper models for complicated systems, which can reduce the analysis and design time of new products.
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