A general framework for fatigue reliability analysis of a high temperature component

Abstract

Fatigue reliability analysis for a high temperature component is a complex task due to the difficulties of doing experiments and uncertainties occurring in practical engineering. Researchers usually focus on the fatigue life scatter of standard specimens since the life data are easily available from fatigue tests. An alternative method is proposed in this paper which can replace the real tests of high temperature components with finite element (FE) simulation. A constitutive model coupled damage rule is utilized in this work to describe the cyclic stress‐strain response. Then, the FE simulations is incorporated into the general framework for reliability assessment. An adaptive least squares support vector machines with Latin hypercube sampling and learning function is employed to construct limit state function. To verify the accuracy and robustness of proposed method, a numerical example is utilized. In the end of this paper, a three‐layer structure extrusion container is applied for whole flowchart of fatigue reliability analysis.

A New Approach for Fatigue Reliability Analysis of Thin-Walled Structures with DC-ILSSVR

“…Assuming that there are n points of for the input variables, the corresponding outputs y can be written according to the LS-SVR theory [ 33 ]: where are the Lagrange multipliers, is the kernel function, and b is the bias term. and b are obtained by the following equations [ 34 ]: where K is the kernel matrix, v is a n × 1 vector in which the value of each element is equal to 1, and is the regularization parameter affecting the balance between the minimization of training error and the smoothness of the regression curve.…”

A multiaxial probabilistic fatigue life prediction method for nickel‐based single crystal turbine blade considering mean stress correction

“…Moreover, due to the randomness of manufacturing errors, operation loads, environmental conditions, and dispersion of material properties, the multiaxial fatigue performance normally presents unavoidable stochastic behavior. [49][50][51] Until now, numerous efforts on probabilistic LCF analysis of engines have been carried out from different aspects: turbinebladed disks, [52][53][54][55][56] bearing support structures, 57 notched components, 58,59 dispersion of material properties, [60][61][62] turbine blades, [63][64][65][66] and model prediciton error. 70 However, the effects of stochastic mean stress and equivalent strain for NSCS turbine blade have not been well studied.…”