Reduced Order Model for Nonlinear Multi-Field-Coupled Spinning Shaft During Transient Operation Based on Coupled Modes Extracted by POD

Processing the numerical solution for the nonlinear spinning shaft using the Time- (Proper Orthogonal Decomposition) transform identifies the coupling between the rigid body motion and deformation as well as the coupling between the deformation modes. Laying on the fact that the POD characterizes the motion into set of optimum coupled modes, it is convenient to relay on them to derive nonlinear reduced order models. In this work, the discrete dynamics of nonlinear spinning shaft are processed using the POD method to produce optimum modes that are used to furnish bases to derive nonlinear coupled reduced model. The derived reduced model is tested at several operational conditions and compared to the full model characteristics. The reduced model produces back the dynamics; captures the natural frequencies and whirling.

Magnetic Bearing Rotordynamic System Optimization Using Multi-Objective Genetic Algorithms

Multiple objective genetic algorithms (MOGAs) simultaneously optimize a control law and geometrical features of a set of homopolar magnetic bearings (HOMB) supporting a generic flexible, spinning shaft. The minimization objectives include shaft dynamic response (vibration), actuator mass and total actuator power losses. Levitation of the spinning rotor and dynamic stability are constraint conditions for the control law search. Nonlinearities include magnetic flux saturation, and current and voltage limits. Pareto frontiers were applied to identify the best-compromised solution. Mass and vibration reductions improve with a two control law approach.