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Yi Wang, M.S. Student Advisor: Professors Dan M. Ionel and Adel Nasiri

Large Scale Design Optimization of Synchronous Reluctance Machines with Steady-state Ultrafast Multi-physics Model. Yi Wang, M.S. Student Advisor: Professors Dan M. Ionel and Adel Nasiri. Objectives.

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Yi Wang, M.S. Student Advisor: Professors Dan M. Ionel and Adel Nasiri

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  1. Large Scale Design Optimization of Synchronous Reluctance Machines with Steady-state Ultrafast Multi-physics Model Yi Wang, M.S. Student Advisor: Professors Dan M. Ionel and Adel Nasiri

  2. Objectives • Accurately analyze the performance of electric machines with a coupled electromagnetic, thermal, and air-flow model. The electromagnetic power losses are generating heat, leading to a non-uniform distribution of temperature inside the machine, which in turn yields further variations of the power losses. • Automate the multi-objective design optimization of Synchronous Reluctance (SyncRel) machines by means of a ultrafast Computationally Efficient Finite Element Analysis (CE-FEA) and Differential Evolution algorithms (CMODE).

  3. Approach • CE-FEA employs only a limited number of magnetostatic solutions and fully exploits the symmetries of synchronous machines through space-time transformations reducing the computational time up to two orders of magnitude, as compared with conventional transient FEA. The method was implemented in the scripting language of the ANSYS/Maxwell2D software. • The thermal and air-flow analysis is performed using a 3D equivalent circuit network. This was implemented in the scripting language of the Motor Design/MotorCAD software. • For the automatic design optimization method with multiple objectives and constraints, a DE algorithm is implemented in Matlab.

  4. Thermal analysis

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