Adaptive Local Maximum-Entropy Surrogate Model and Its Application to Turbine Disk Reliability Analysis

Abstract

The emerging Local Maximum-Entropy (LME) approximation, which combines the advantages of global and local approximations, has an unsolved issue wherein it cannot adaptively change the morphology of the basis function according to the local characteristics of the sample, which greatly limits its highly nonlinear approximation ability. In this research, a novel Adaptive Local Maximum-Entropy Surrogate Model (ALMESM) is proposed by constructing an algorithm that adaptively changes the LME basis function and introduces Particle Swarm Optimization to ensure the optimality of the adaptively changed basis function. The performance of the ALMESM is systematically investigated by comparison with the LME approximation, a Radial basis function, and the Kriging model in two explicit highly nonlinear mathematical functions. The results show that the ALMESM has the highest accuracy and stability of all the compared models. The ALMESM is further validated by a highly nonlinear engineering case, consisting of a turbine disk reliability analysis under geometrical uncertainty, and achieves a desirable result. Compared with the direct Monte Carlo method, the relative error of the ALMESM is less than 1%, which indicates that the ALMESM has considerable potential for highly nonlinear problems and structural reliability analysis.

Analysis of Meshfree Galerkin Methods Based on Moving Least Squares and Local Maximum-Entropy Approximation Schemes

“…Hence, the LME approximation has demonstrated its accuracy and stability in dealing with problems involving large deformation and phase transition when employed as shape functions. Additionally, the LME approximation is incorporated in constructing the surrogate models for structural reliability analysis, offering an accurate and robust approach for highly nonlinear problems [38,39].…”

A Study of Evaluation Method for Turbocharger Turbine Based on Joint Operation Curve

“…The performance of turbochargers in a harsh working environment of high temperature and high pressure for a long time will directly affect the performance of an engine. Fan proposed a novel Adaptive Local Maximum-Entropy Surrogate Model, carried out a turbine disk reliability analysis under geometrical uncertainty, and achieved a desirable result [38]. Meng constructed a smooth response surface of the turbine performance by the saddlepoint approximation reliability analysis method and solved a turbine blade design problem [39].…”