Analysis and Optimization of the Electromagnetic Performance of a Novel Stator Modular Ring Drive Thruster Motor

Abstract

Abstract:A rim driven thruster (RDT) is an integrated deep-sea motor thruster that has been widely studied. In order to improve the performance of RDT, a novel RDT motor with a modular stator is proposed in this paper. The electromagnetic performance of the new RDT motor is analyzed by the finite element method (FEM). The influence of structure parameters on the electromagnetic performance of the new RDT motor are analyzed in detail. It is shown that the effect of additional tooth width and pole arc coefficient on the electromagnetic performance of the stator modular RDT motor is significant. To obtain the optimal design with a maximum average electromagnetic torque and minimum torque fluctuation ratio, a multi-objective optimization design method combining the non-dominated sorting genetic algorithm II (NSGA-II), Kriging method and FEM is presented in this paper. A set of Pareto optimal solutions is obtained, and the optimal design point is selected from the Pareto fronts. Compared with the initial design, the average electromagnetic torque of the optimized model is improved by 16.591% and the fluctuation ratio is reduced to 3.18%.

Multi-Objective Optimization of Permanent Magnet Synchronous Motor for Electric Vehicle Considering Demagnetization

“…Metamodeling is the process of approximating the DOE results in the form of a function. Although different metamodeling techniques could be used for different design problems, in previous studies, metamodels of objective functions and constraints were established by applying a single metamodeling technique [10][11][12][13][14][15][16]. In this study, we established metamodels by applying 13 techniques for each multi-objective function and constraint, and the most accurate technique was selected through accuracy evaluation.…”

“…Since there are various types of metamodel techniques for optimal design, it is important to apply a proper metamodel technique with high accuracy according to each design problem [9]. However, in most of the previous studies, optimization was usually performed using only one metamodel technique, e.g., the response surface method or Kriging [10][11][12][13][14][15][16].…”

Optimal Design of PMSM Based on Automated Finite Element Analysis and Metamodeling

“…This is because the predictive performance of the metamodel affects the reliability of the optimal design. Most of the existing studies have been metamodeled by a single method such as Kriging and RSM, and the accuracy evaluation has not been performed [10][11][12][13][14][15][16][17][18]. In this study, however, metamodels for the objective function and constraints are generated in 11 ways provided by PIAnO, and the best metamodels are selected, respectively, by comparing the RMSE test results to evaluate the metamodel accuracy.…”

“…To obtain a reliable optimal design result, a large number of DOE have to be carried out. However, the conventional method of manually performing the process was complicated and time consuming, and thus the number of DOE was limited [10][11][12][13][14][15][16][17][18]. However, using the automated DOE process proposed in this study, not only can the DOE be easier but also the number of DOE can be dramatically increased, resulting in high reliability of the optimal design result.…”

“…Most of the previous studies have been applied to optimal design using metamodel generated in one way. There have been a lot of optimizations recently using a single metamodeling technique such as Kriging and response surface method [10][11][12][13][14][15][16][17][18]. However, since a suitable metamodel is different according to each design problem and condition, it is necessary to select the best metamodel through accuracy evaluation after generating several metamodels.…”