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案件記録

PREDICTION METHOD FOR RESPONSE OF REFINED FINITE ELEMENT MODELS OF COMPLEX STRUCTURE

発明審査中
4閲覧数
8請求項 · 1 独立
§ Ⅰ

案件概要

発明者

Maosen CAO; Yifei LI; Tongfa DENG; Li CHEN; Drahomír Novak; Dragoslav SUMARAC; Qingyang WEI; Zeyu WANG

IPC分類

G6F 30/23G6F 111/8

CPC分類

G6F30/23G6F2111/8

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.

原文(中国語)

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.

外部リソース