| dc.description.abstract | This thesis aims to explore the key factors that contribute to the performance of an axial flux machine. The axial flux machine offers distinct advantages such as higher power and torque density, as well as the ability to be constructed with a slim profile, which has led to its popular nickname, the "pancake motor." To investigate the factors that contribute to a well-performing axial flux machine, an optimization study has been done using COMSOL Multiphysics.
The primary objective of the optimization study was to study which parameter being important in optimizing the axial flux machine. Given that a good axial flux machine is characterized by high efficiency and power density when the diameter is big enough the optimization aimed to achieve these goals. The specific machine chosen for optimization was an axial flux machine with a power output of 60 kW, intended to deliver a torque of 1150 Nm at a rated speed of 500 RPM.
Through the optimization process, various parameters and design elements were adjusted and analyzed to find the optimal combination that would result in the desired efficiency while minimizing the axial length. The aim was to producing a highly efficient axial flux machine capable of meeting the specified power and torque requirements.
To enhance the optimization process and facilitate learning about what constitutes a good axial flux machine, an application has been developed using COMSOL Multiphysics. This app enables students to gain practical experience and learn from trial and error. By manipulating various parameters within the app, students can explore the significance of each parameter in achieving optimal performance and design of an axial flux machine. Importantly, deep knowledge of axial flux permanent magnet (AFPM) machines is not required to use the app.
The idea for the app emerged during the development of an underperforming axial flux machine, prompting the question of how to improve its performance. The app serves as a platform to experiment with different parameters and investigate which specific parameter modifications could lead to an enhanced axial flux machine.
In this paper, an optimization study is done using the app, and the resulting optimizations are rigorously verified and cross-validated. By leveraging the app's capabilities, this study provides valuable insights into the optimization of axial flux machines, ensuring reliable and robust results. | |
