PREDICTION METHOD FOR RESPONSE OF REFINED FINITE ELEMENT MODELS OF COMPLEX STRUCTURE
개요
출원인
JIANGXI UNIVERSITY OF SCIENCE AND TECHNOLOGY; HOHAI UNIVERSITY
발명자
Maosen CAO; Yifei LI; Tongfa DENG; Li CHEN; Drahomír Novak; Dragoslav SUMARAC; Qingyang WEI; Zeyu WANG
IPC 분류
CPC 분류
The invention provides a prediction method for the response of a refined finite element model of a complex structure. It includes establishing a refined finite element model and a rough mirror information model with different mesh densities; using Latin Hypercube Sampling (LHS) for random sampling to construct input parameter sample sets of sizes m and n; performing probabilistic finite element analysis and extracting output response sample sets; constructing a Kriging model based on the first m sets of data in the output response sample sets, and using validation error to evaluate predictive accuracy; predicting the output response of the refined finite element model corresponding to the remaining n−m sets of data in the response sample sets of the rough mirror information model according to the Kriging model. This method reduces surrogate model's dependence on the forward calculation model's fineness and significantly reduces calculation time for system response of complex structures.
원문 (중국어)
The invention provides a prediction method for the response of a refined finite element model of a complex structure. It includes establishing a refined finite element model and a rough mirror information model with different mesh densities; using Latin Hypercube Sampling (LHS) for random sampling to construct input parameter sample sets of sizes m and n; performing probabilistic finite element analysis and extracting output response sample sets; constructing a Kriging model based on the first m sets of data in the output response sample sets, and using validation error to evaluate predictive accuracy; predicting the output response of the refined finite element model corresponding to the remaining n−m sets of data in the response sample sets of the rough mirror information model according to the Kriging model. This method reduces surrogate model's dependence on the forward calculation model's fineness and significantly reduces calculation time for system response of complex structures.