The combinatorial optimization strategy for large-sized magnetic suspension rotor in CMG system

To improve the efficiency and to get the best design parameters of the large-sized magnetic suspension CMG (Control Moment Gyroscope) system being used in space station, a optimization design and analysis method about the rotor with high speed (1200r/min) and big angular momentum (1000 N·m·s) has been proceeded. A combinatorial optimization strategy based on Genetic Algorithm (GA) and Sequential Quadratic Programming (NLPQL) is proposed to optimize the rotor system. ANSYS is used to build a parameterization model, INIGHT and ANSYS are integrated to finish the optimization motor mass and maximal equivalent stress taken to be minimum respectively are chose as the design objects. The wheel disk dimensions are taken as design variables. Size, strength and effectiveness are taken as constraints according to the rotor working condition. Compared with GA and NLPQL in operating efficiency and optimization result, it is found that the combinatorial optimization strategy is better than a global or a local optimization algorithm to be used only. The combinatorial optimization strategy's result indicates that the mass decreases 3.92 percent, the safety factor improves 2.69 percent. The experimental result demonstrates the combinatorial optimization strategy's design result.