Dynamic coupled vibration analysis of a large wind turbine gearbox transmission system

A lumped-parameter coupled nonlinear dynamic model for one large multi-stage wind turbine gearbox transmission system is established comprehensively including wind varying load, mesh stiffness, dynamic transmission error, gravity, and bearing nonlinear characteristics to obtain the gearbox dynamic response. The vibration differential equations of the drive-train are deduced through the Lagrange’s equation. On the basis of that, the dynamics of wind turbine gearbox is investigated by a Runge-Kutta numerical method that includes simultaneous internal and external excitations. The results show that the dynamic response of the partial component is mainly superposed by high-frequency component caused by the internal excitation and low-frequency component caused by the external excitation. In medium-speed stage and high-speed stage, the vibration amplitude has obvious fluctuation, and the multiple frequency and random frequency components become increasingly obvious with increasing rotational speed and eccentricity at gear and bearing positions. Axial vibrations of the system also have some fluctuation. The bearing has self-variable stiffness frequency, which should be avoided in engineering design stage. The study results provide a theoretical foundation for dynamical characteristics evaluation and dynamic optimization of a large wind turbine gearbox transmission system

The Quantitative Diagnosis Method of Rubbing Rotor System

The dynamics of the rubbing rotor system is analyzed by applying harmonic balance method. The relationship between harmonic components in the response of the rubbing rotor system and the dynamic stiffness matrix of the fault free rotor system is revealed, based on which a new model based method for rubbing identification is presented. By applying this method, the fault location and rubbing forces of the single rubbing rotor system can be identified by using vibration data of only two nodes, the rubbing locations and rubbing forces of the double rubbing rotor system can be identified by using vibration data of three nodes. The numerical simulations and experiments on the rotor test-rig are carried out to verify the efficiency of the present method

Nonlinear dynamic analysis for high speed gear-rotor-bearing system of the large scale wind turbine

In this paper, an eight-degree-of-freedom (8-DOF) lumped parameter dynamic model considering the coupled lateral-torsional vibration is proposed and the coupled multi-body dynamics of the spur gear rotor bearing system is studied containing backlash, transmission error, eccentricity, gravity and time-variant mesh stiffness. Based on the dynamical equations, the coupled dynamic response of the system is investigated using the Runge-Kutta method and the effects of error fluctuation and load fluctuation on the dynamic responses are demonstrated by 3-D frequency spectrum bifurcation diagram, etc. The results show that a diverse range of nonlinear dynamic characteristics such as periodic, chaotic behaviors and impacts exhibited in the system are strongly attributed to the interaction between internal and external excitations. For gear system, the dynamic behaviors are analyzed in light, middle and high rotational speed conditions. With the increase rotational speed, the vibration amplitude increase markedly and the region of the chaotic motion become narrow gradually. At the low rotational speed, the chaos behavior turns out more easily, and the vibration intensity relatively weak. With the increase rotational speed, the vibration amplitude obvious increase, and the characteristics of the chaos strengthen and turns backward. This study may contribute to a further understanding about the spur gear bearing system with the coupled internal and external excitation

catena-Poly[[[tetra­quazinc(II)]-μ-2,5-dihydroxy­benzene-1,4-diacetato-κ2 O 1:O 4] dihydrate]

The title compound, {[Zn(C10H8O6)(H2O)4]·2H2O}n, is a one-dimensional coordination polymer with 2,5-dihydroxy­benzene-1,4-diacetate acting as bridging ligand. The zigzag chains, extending parallel to [011], are further packed into a three-dimensional network by hydrogen bonds

Enhancing Performance of Machine Learning-Based Modeling of Electromagnetic Structures

The machine learning (ML)-based modeling of electromagnetic (EM) structures involves the development of a surrogate model that approximates the relationship between EM geometries and responses, such as S 11 , gain, etc. The performance of the surrogate model is mainly affected by the simulation data for training. Normally, the training data is collected by uniformly sweeping the geometric parameters. Restricted by the computation power, only a limited parameter space can be sampled. The trained surrogate model behaves well within the sampling range but deteriorates as the parameter range extends. In this paper, we expand the predictable parameter range of an ML model with the same simulation expense by optimizing the data acquisition strategy. This approach leads to the proposed model demonstrating higher accuracy within an extended parameter space than conventional models, while the simulation consumption remains the same. We present an application example to validate its effectiveness. The proposed modified ML-based design method can potentially improve the performance of surrogate models in real-world applications