Parameter determination for ice material model based on a bidirectional long short-term memory neural network

被引：8

作者：

Li, Dacheng ^{[1
]}

Jiang, Xiongwen ^{[1
]}

Zhang, Wei ^{[1
]}

Guo, Licheng ^{[2
]}

机构：

[1] Harbin Inst Technol, High Veloc Impact Dynam Lab, Harbin 150080, Peoples R China

[2] Harbin Inst Technol, Ctr Composite Mat, Harbin 150001, Peoples R China

来源：

INTERNATIONAL JOURNAL OF IMPACT ENGINEERING | 2022年 / 161卷

基金：

中国国家自然科学基金;

关键词：

Ice material; Impact test; Finite element method; Inverse method; LSTM neural network; IMPACT; STRENGTH; BEHAVIOR; FRACTURE;

D O I：

10.1016/j.ijimpeng.2021.104110

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Parameter determination is a common problem in engineering activities. For impact problems subjected to ice projectiles, however, very few researches have addressed the inverse method to determine the material pa-rameters of ice for finite element simulations. The present study introduced a novel method based on a sequence-to-sequence bidirectional long short-term memory (LSTM) neural network to learn the relationship between the input impact force histories and output material parameters of the finite element model, which was built to reproduce the ice impact test using a hollow tube sensor. After the trained network was evaluated by testing data set, the experimental data was used to predict the parameters for the numerical model to precisely match the test results.

引用

页数：16

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