Multi-objective free-form shape optimization of a synchronous reluctance machine
For electric machine designers, this work demonstrates a computationally efficient gradient-based optimization method that avoids geometric parametrization, though it is an incremental improvement over existing parametric optimization.
This paper applies gradient-based free-form shape optimization to maximize torque in a synchronous reluctance machine for an X-ray tube, achieving designs in minutes versus hours for stochastic optimization, and extends the method to multi-objective optimization to approximate a Pareto front.
This paper deals with the design optimization of a synchronous reluctance machine to be used in an X-ray tube, where the goal is to maximize the torque, by means of gradient-based free-form shape optimization. The presented approach is based on the mathematical concept of shape derivatives and allows to obtain new motor designs without the need to introduce a geometric parametrization. We validate our results by comparing them to a parametric geometry optimization in JMAG by means of a stochastic optimization algorithm. While the obtained designs are of similar shape, the computational time used by the gradient-based algorithm is in the order of minutes, compared to several hours taken by the stochastic optimization algorithm. Finally, we show an extension of the free-form shape optimization algorithm to the case of multiple objective functions and illustrate a way to obtain an approximate Pareto front.