中国舰船研究2024，Vol.19Issue(6)：228-236,9.DOI:10.19693/j.issn.1673-3185.03463
基于长短时记忆网络的结构动态载荷预测方法
Structural dynamic load prediction method based on long short-term memory network
摘要
Abstract
[Objective]To address the limitations of traditional surrogate models in handling time-dependent dynamic processes and heterogeneous data,this paper proposes a dynamic load surrogate model method based on a long short-term memory(LSTM)network.[Methods]The surrogate model is comprised of two mod-ules:the load feature encoder and load response decoder.First,the LSTM in the load feature encoder per-forms feature extraction on the time series of dynamic external loads.Next,the extracted load features are combined with the structural parameter features.The LSTM in the load decoder conducts further feature ex-traction and finally generates output while comprehensively considering the heterogeneous data input of the dynamic external load time series and one-dimensional structural parameter features in order to predict the time history of internal force responses.Finally,the model's accuracy is evaluated using a finite element simu-lation dataset and compared with other surrogate model methods.[Results]The results show that the aver-age accuracy of the dynamic load surrogate model can reach 98%,which is higher than that of other methods,and its calculation speed is faster than that of the finite element method.[Conclusions]The proposed meth-od addresses the issue of heterogeneous data involving both time-series and non-time-series features,and of-fers advantages such as high accuracy and efficiency,making it effective for fast iterative computation tasks.关键词
结构优化/动态载荷/人工智能/代理模型/深度学习/长短时记忆网络Key words
structural optimization/dynamic loads/artificial intelligence/surrogate model/deep learn-ing/long short-term memory(LSTM)network分类
交通工程引用本文复制引用
樊昱玮,郭腾博,李哲,洪良友,刘超,蒋东翔..基于长短时记忆网络的结构动态载荷预测方法[J].中国舰船研究,2024,19(6):228-236,9.基金项目
航空发动机及燃气轮机基础科学中心资助项目(P2022-C-I-002-001) （P2022-C-I-002-001）
