Issah Ibrahim

The application of surrogate models in engineering design is becoming popular due to their mimicking capabilities. In the design of electric motor drives for example, where a control-drive circuit is connected directly to the electric motor, the finite element simulations of the integrated motor drive system can be time-consuming. A complete motor simulation can run into several hours, if not days. Therefore, the main contribution of this article resides in the effort to address the computational burden associated with the design process by replacing the finite element models with surrogate models to achieve a faster design process. Briefly, the procedure involves filling the design space with multiple motor geometries created from an 8-pole 12-slot surface-mounted permanent magnet (SPM) synchronous motor. Then, a multiphysics performance evaluation is performed over the design space for different switching frequencies over a range of dc bus voltages. The design variables of the motor, the inverter, and the calculated performance objectives are used to train a collection of surrogate models to obtain the objective functions that could potentially be used as possible replacements for the finite element models in order to reduce the solution time.

Design and optimization problems typically require running thousands of motor simulations which can take several hours, if not days. To overcome this bottleneck, surrogate models are often used in engineering design to expedite the process. When inverter-fed motor drives are involved, the cost of generating a finite element (FE) database from a pulsewidth modulated (PWM) current simulations to fit such models can be prohibitive. This article compares an ensemble of surrogate models of synchronous motors generated with sinusoidal and PWM stator excitations, in terms of computational burden and performance. The main contribution resides in showing whether computational resources can be saved with sinusoidal excitation models without compromising the optimization results of a real inverter-fed permanent magnet synchronous motor design.

Design and optimization problems typically require running thousands of motor simulations, which could take several hours if not days. Finding alternative means of reducing the solution time has recently gained research interest. Surrogate models can emulate the outputs of computer simulations with less computational effort. This paper proposes the use of surrogate models to predict the acoustic noise, applied to an interior permanent-magnet synchronous motor (IPMSM). The simulation procedure involves using finite element analysis to evaluate the acoustic performance across a design space of stator and rotor geometric variations. Then, four different classes of surrogate models are used to learn a portion of the design space before attempting to generalize and make predictions in a much larger space with relatively less computational burden. It is demonstrated that the trained models can be considered as appropriate replacements of the time-consuming FEA for future design and optimization problems of the same motor case study.