Parameter identification theory of a complex model based on global optimization method  

作　　者：QU Jie1, JIN QuanLin2 & XU BingYe3 1 College of Automotive Engineering, South China University of Technology, Guangzhou 510640, China 2 Beijing Research Institute of Mechanical and Electrical Technology, Beijing 100083, China 3 Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China 

出　　处：《Science China(Physics,Mechanics & Astronomy)》2008年第11期1722-1732,共11页中国科学：物理学、力学、天文学（英文版）

基　　金：the National Key Basic Research Development Program of China (973 Program)(Grant No. G2006CB605208-3);the National Natural Science Foundation of China (Grant No. 10602018);the Natural Science Foundation of Guangdong Province of China (Grant No. 05300252)

摘　　要：With the development of computer technology and numerical simulation technology, computer aided engineering (CAE) technology has been widely applied to many fields. One of the main obstacles, which hinder the further application of CAE technology, is how to successfully identify the parameters of the selected model. An elementary framework for parameter identification of a complex model is provided in this paper. The framework includes the construction of objective function, the design of the optimization method and the evaluation of the identified results, etc. The parameter identification process is described in this framework, taking the parameter identification of the superplastic constitutive model considering grain growth for Ti-6Al-4V at 927°C as an example. The objective function is the weighted quadratic sums of the difference between the experimental and computational data for the stress-strain relationship and the grain growth relationship; the designed optimization method is a hybrid global optimization method, which is based on the feature of the objective function and incorporates the strengths of genetic algorithm (GA), the Levenberg-Marquardt algorithm and the augmented Gauss-Newton algorithm. The reliability evaluation of parameter identification result is made through the comparison between the calculated and experimental results and between the theoretical values of the parameters and the identified ones.With the development of computer technology and numerical simulation technol- ogy, computer aided engineering (CAE) technology has been widely applied to many fields. One of the main obstacles, which hinder the further application of CAE technology, is how to successfully identify the parameters of the selected model. An elementary framework for parameter identification of a complex model is pro-vided in this paper. The framework includes the construction of objective function, the design of the optimization method and the evaluation of the identified results, etc. The parameter identification process is described in this framework, taking the parameter identification of the superplastic constitutive model considering grain growth for Ti-6Al-4V at 927℃ as an example. The objective function is the weighted quadratic sums of the difference between the experimental and computational data for the stress-strain relationship and the grain growth relationship; the designed optimization method is a hybrid global optimization method, which is based on the feature of the objective function and incorporates the strengths of genetic algo-rithm (GA), the Levenberg-Marquardt algorithm and the augmented Gauss-Newton algorithm. The reliability evaluation of parameter identification result is made through the comparison between the calculated and experimental results and be-tween the theoretical values of the parameters and the identified ones.

关 键 词：CONSTITUTIVE model PARAMETER IDENTIFICATION THEORY INVERSE analysis GLOBAL optimization 

分 类 号：O224[理学—运筹学与控制论]