Fatigue Reliability Analysis Of Rotor Blade

Rotor blade is a key component of aero engine. The working environment and mechanical behavior of blades are very complicated. The various uncertain factors in external load, geometry and material properties etc. affect the structural reliability. Because the uncertainty of exists in blade parameters, the fatigue reliability analysis in design and practical application is significant research work.The research object of this thesis is the rotor blade of aero engine compressor. Considering the randomness of the two parameters, elastic modulus and structural dimension, based on a quasi-static model and the local stress-strain method, the fatigue reliability of blades is studied by the perturbation stochastic finite element method (PSFEM) and JC-First order second moment method.This thesis consists of five parts: Part I is a brief introduction, to the background of the research, the development of the stochastic finite element method (SFEM) and reliability analysis. The definition and the calculation method of reliability are reviewed in Part II. Part III is about the theory of the SFEM and its application in the stochastic analysis of blades. In Part IV a method for the fatigue reliability analysis of rotor blades is presented. Part V is the conclusion. It is involved in detail that1. The discretion of random structureBased on the local average theory of random field, the computation formulas for discretized 3-D random field is used to deal with random field of blade, discreted by isoparametric elements. Though the discretion the randomness of variables at all point can be approximately described by some average values of randomness in limit areas.2. The probabilistic analysis of elastic stress fieldThe randomness of the parameters, such as elastic modulus (E), thickness of the blade body (T) and dimension of root in the narrowest section and its vicinity (expressed by Y coordinate) lead to changes of stiffness matrix and load vector, so their derivatives to the stochastic parameters are deduced. The orthogonal transformation of covariance matrix and...