Enhanced network learning model with intelligent operator for the motion reliability evaluation of flexible mechanism

For the nonlinear dynamic analyses of complex mechanical components, it is necessary to apply efficient modeling framework to reduce computational burden. The accurate surrogate model for approximating the nonlinear responses of several failures is a vital issue to provide robust and safe design conditions in complex engineering applications. In this paper, two different Modified multi-extremum Response Surface basis Models (MRSM) are proposed for dynamic nonlinear responses of failure capacities for turbine blisk responses. The proposed MRSM is established using two regression processes including regressed the input variables by linear or exponential basis functions in first calibrating phase and regressed the second-order polynomial basis function using inputs data provided by first stage in second calibrating procedure. A sensitivity analysis using MRSM is proposed to consider the variation of input variables on the nonlinear responses. In the sensitivity analysis procedure, the effects of input variables are evaluated using the calibrating results given from the first regressed process. To evaluate the performance of the proposed MRSM, three multi-extremum failure modes including radial deformation of compressor blisk, maximum strain, and stress of compressor blade and disk are considered. the prediction of MRSM of nonlinear responses for Thermal-fluid–structure system with dynamical nonlinear finite-element analyses is compared with response surface method (RSM) and artificial neural network (ANN). The predicted results of modeling approaches showed that the sensitivity analysis based on MRSM accurately provided the effective degree for input variables. The gas temperature has the highest effects on nonlinear responses of turbine blisk which is followed by angular speed and material density. The MRSM combined with basic exponential function performs better than other models, while the MRSM coupled with linear function is more accurate than ANN and RSM. The proposed MRSM models have illustrated the accurate and efficient framework for approximating dynamic structural analysis of complex components.

Multi-extremum-modified response basis model for nonlinear response prediction of dynamic turbine blisk

The design of complex mechanical components is a time-consuming process which involves many design variables with multiple interacted objectives and constraints. Traditionally, the design process of mechanical components is performed manually depending on the intuition and experience of the designer. In recent decades, automatic methods have been proposed to effectively search diverse and large parameter spaces. There is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. Nowadays, there is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. This review article demonstrates the applications of different metaheuristic algorithms in enhancing the design process of different mechanical systems. First, the basic concepts of common used metaheuristic algorithms are introduced. Then the applications of theses algorithms in optimization of different mechanical systems are discussed.

Liqiang An

Papers

Dynamic parametric modeling-based model updating strategy of aeroengine casings

Whole-process design and experimental validation of landing gear lower drag stay with global/local linked driven optimization strategy