Research On Dynamics Problem Of Blade-Rotor System And Parameters Identification Method

High speed rotating machinery is widely used in many fields, such as aerospace, electric power, metallurgical and so on. It is necessary to pay close attention to build an effective modelling strategy and identify the faults and parameters of high speed rotating machinery, which can reduce the mechanical faults and improve the safety and reliability of high speed rotating machinery.In this paper, we research on the rotor system. Firstly, a coupling nonlinear dynamical model of the blade-rotor-bearing system is developed to analyse the interaction of the rotor, blade and bearing. Secondly, the improved differential evolution is used to optimize the installation arrangement of mistuned blades and identify the faults and parameters of the rotor system. To improve the operation efficiency, a new identification method combined with multi-points added Kriging surrogate model and improved differential evolution algorithm is presented, which identifies the faults and parameters of linear and nonlinear rotor system effectively and exactly. The main works in this paper are as follows:1. The nonlinear coupling model of blade-rotor-bearing system is established by the Lagrange approach. Simplifying the rotor system with the lumped mass method. The baldes are modeled as a cantilever beam and simplified with the assumed mode method. The motion equations are simplified owing to cyclic symmetric property. According to this, the dimension of blade-rotor-bearing system can be reducted, especially for too many blades. Considering the bending-torsion coupling motion with nonlinear oil film, the motion differential equations are solved. The bifurcation diagrams, maximum Lyapunov exponent diagrams, phase plane portraits, Poincare maps, time domain waveform, amplitude-frequency curve are used to analyse the motion state of blade-rotor-bearing system. The nonlinear dynamic characteristics of the rotor system with or without blades and the effect of blade length on onlinear dynamic characteristics are discussed.2. A lumped parameter model of blade-disk system is established. At the beginning, we analyse the dynamic characteristics of free vibration and forced vibration and discuss the effect of mistuning on mode localization of various orders. Based on the rules, a new formula which evaluates the degree of mode localization of mistuned blade-disk is proposed. At the end, the differential evolution algorithm is used to optimize the installation arrangement of mistuned blades. Both reducing vibration and tuning amplitudes are considered which makes each blade share vibration energy evenly and bring down the material fatigue of blades. 3. An improved differential evolution algorithm is proposed. It is suitable for identifying the faults and parameters of rotor system. Because of the large range of parameter interval, the genetic algorithm based on its global searching ability is used to reduce the optimization interval firstly. Secondly, in order to prevent reaching the dilemma problem and the local optimal solution, the adaptive Cauchy mutation and adaptive Gaussian mutation are put forward to modify the original mutation strategy of the differential evolution. A linear rotor system with unbalance and a nonlinear rotor system are as the numerical examples. The results prove that the improved differential evolution algorithm is efficient and accurate on faults and parameters identification of rotor system.4. A new parameter identification method is designed based on the Kriging surrogate model and the improved differential evolution algorithm. This identification method updates the Kriging surrogate model by adding current optimal point, which improves the accuracy of the Kriging surrogate model in global approximation. A numerical example and the experiment are analysed to vertify the effectiveness and accuracy of the method on parameter identification of rotor system. After that, a new multi-points addition criterion is joined in the Kriging surrogate model. It means that not only the current optimal point, but also the correlated points are add to update the Kriging surrogate model, which can improve the precision of Kriging surrogate model in a short time. At the end, the linear and nonlinear examples are analysed. We discuss the identification results using the multi-points added Kriging-AHDE, and the applicable condition of this method.