Simulation Techniques for Dynamic Systems (ME5339)
This course develops competence in the modeling of dynamical systems and in the use of dynamic system simulation software for conducting system analysis and control design.
The target audience for this course is formed by students and practical engineers with a basic (undergraduate) background in system dynamics who are seeking a deeper understanding of the process of modeling and simulation of multi-physics dynamical systems.
The course integrates knowledge from multiple disciplines (statics and dynamics, thermo-fluid sciences, numerical methods, optimization, linear systems and control theory) to develop a comprehensive methodology for model development, verification, implementation, calibration, verification and application.
Undergraduate background in mechanical engineering, or equivalent experience, and working knowledge of Matlab/Simulink are required.
Upon completion of this course participants will be able to:
Design models of complex, multi-domain physical systems from governing equations using a systematic method
Implement models in simulation environment (Matlab Simulink), conduct calibration and verification
Utilize models to conduct analysis, optimization and design on dynamic systems, including development of simple control algorithms
Marcello Canova is an associate professor of Mechanical and Aerospace Engineering at The Ohio State University, and associate director for Graduate and Continuing Education at the Center for Automotive Research. His research focuses on the optimization and control of propulsion systems, including internal combustion engines, hybrid-electric drivetrains, energy storage systems and thermal management.
Canova’s work in energy optimization of advanced powertrains has led to significant fuel economy benefits and has been implemented in production programs by major OEMs. In addition, he has published over 110 articles in refereed journals and conference proceedings and received, among others, the SAE Vincent Bendix Automotive Electronics Engineering Award (2009), the SAE Ralph E. Teetor Educational Award (2016), the NSF CAREER Award (2016), the Lumley Research Award (2016) and the Michael J. Moran Award for Excellence in Teaching (2017).