Abstract:
Master the automated design optimization of the IPM motor by applying powerful classical and intelligent algorithms to solve both single- and multi-objective challenges. This hands-on course teaches you to build a complete optimization workflow by implementing algorithms in MATLAB and interfacing with finite element analysis for high-fidelity simulations (prerequisite: link).
Course Contents:
- Understand the theoretical concepts behind design optimization for IPM motor, define key terminology, and outline the course workflow.
- Learn how to translate an engineering design challenge into a mathematical problem by defining design variables, constraints, and objective functions for the IPM case study.
- Develop a robust script that creates a seamless link between MATLAB and finite element analysis, enabling automated simulation and data retrieval.
- Learn the core theory of single-objective algorithms and understand their operational flowchart.
- Translate the algorithm theory into functional MATLAB code, building the core components of the algorithm step-by-step.
- Execute a complete single-objective optimization for the IPM and learn to analyze convergence plots and validate the final, optimal design.
- Understand the fundamental differences in multi-objective optimization, including the concepts of dominance and the goal of finding the Pareto optimal front.
- Learn the theory behind the powerful multi-objective algorithms.
- Implement the core mechanisms of the multi-objective algorithms in MATLAB.
- Apply the implemented multi-objective algorithm to the IPM problem to find the trade-offs between two or more competing objectives.
- Master the techniques for plotting the Pareto front and learn how to make an informed engineering decision by selecting the best trade-off design.