Strength Reliability Analysis of Turbine Blade Using Surrogate Models

Abstract

There are many stochastic parameters that have an effect on the reliability of steam turbine blades performance in practical operation. In order to improve the reliability of blade design, it is necessary to take these stochastic parameters into account. In this study, a variable cross-section twisted blade is investigated and geometrical parameters, material parameters and load parameters are considered as random variables. A reliability analysis method as a combination of a Finite Element Method (FEM), a surrogate model and Monte Carlo Simulation (MCS), is applied to solve the blade reliability analysis. Based on the blade finite element parametrical model and the experimental design, two kinds of surrogate models, Polynomial Response Surface (PRS) and Artificial Neural Network (ANN), are applied to construct the approximation analytical expressions between the blade responses (including maximum stress and deflection) and random input variables, which act as a surrogate of finite element solver to drastically reduce the number of simulations required. Then the surrogate is used for most of the samples needed in the Monte Carlo method and the statistical parameters and cumulative distribution functions of the maximum stress and deflection are obtained by Monte Carlo simulation. Finally, the probabilistic sensitivities analysis, which combines the magnitude of the gradient and the width of the scatter range of the random input variables, is applied to evaluate how much the maximum stress and deflection of the blade are influenced by the random nature of input parameters.