Taken a gas turbine rotor system as the research object, this paper has established dynamical model based on transfer matrix method. Natural vibration characteristics are solved with different transfer matrix models, and are compared with that gotten by finite element method. Then unbalanced response is researched. The effects of eccentric factors are discussed on unbalanced vibration. Furthermore, the application is explored in rotor dynamic balancing. The results show there is much minor deviation between natural frequencies of transfer matrix method and finite element method, and vibration modes of two methods coincide quite well. It illustrates the model established in the research is reliable. For input side of blade-disk rotor, unbalanced vibration occurs more easily in lower frequency. While unbalanced vibration of output side is more sensitive at higher frequency. With the increase of eccentric mass, unbalanced vibration of blade-disk rotor is more heavily, and the displacement rotates around a fixed node. The application of opposite unbalanced mass can decrease the amplitude of vibration in lower frequency. Hence, it can be used to achieve the balance of rotor dynamics. This research provides the theoretical basis and technological fundament for gas turbine rotor dynamics design.

This article introduces a method of mistuned parameter identification which consists of static frequency testing of blades, dichotomy and finite element analysis. A lumped parameter model of an engine bladed-disc system is then set up. A bladed arrangement optimization method, namely the genetic particle swarm optimization algorithm, is presented. It consists of a discrete particle swarm optimization and a genetic algorithm. From this, the local and global search ability is introduced. CUDA-based co-evolution particle swarm optimization, using a graphics processing unit, is presented and its performance is analysed. The results show that using optimization results can reduce the amplitude and localization of the forced vibration response of a bladed-disc system, while optimization based on the CUDA framework can improve the computing speed. This method could provide support for engineering applications in terms of effectiveness and efficiency.