模型数据混合驱动的火箭贮箱结构极限承载数字孪生技术

黄佳; 童军; 郭建; 郭文婧; 杨蓉; 朱曦全

doi:10.21656/1000-0887.450210

模型数据混合驱动的火箭贮箱结构极限承载数字孪生技术

doi: 10.21656/1000-0887.450210

北京强度环境研究所，北京 100076

详细信息

作者简介:
黄佳（1988—），女，高级工程师，硕士（E-mail: hj880616@126.com）;杨蓉（1978—），女，研究员，硕士（通讯作者. E-mail: rongyang_001@126.com）.

通讯作者:
杨蓉（1978—），女，研究员，硕士（通讯作者. E-mail: rongyang_001@126.com）.

中图分类号: V19
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- 文章访问数: 16
- HTML全文浏览量: 2
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2024-07-12
- 修回日期: 2024-07-31
- 网络出版日期: 2025-09-10

The Model and Data-Driven Digital Twin Technology for Ultimate Load-Bearing Capacity of the Rocket Propellant Tank Structure

Beijing Institute of Structure and Environment Engineering, Beijing 100076, P.R.China

摘要

摘要: 针对航天运载火箭关键承载贮箱结构，提出了一种基于实测数据和模型的极限承载数字孪生构建方法.首先，根据贮箱结构及制造工艺特征建立精细化有限元模型，对强度特性进行计算分析，并根据贮箱结构强度试验测点信息提取计算结果.然后，对贮箱结构强度试验历史试验数据进行了处理分析，基于试验数据和仿真模型构建出贮箱结构承载数字孪生模型的训练数据集.进一步，结合长短期记忆网络模型对孪生算法进行训练，实现了贮箱结构极限承载的预测.最后，基于离线交互和在线交互模式，构建了贮箱结构极限承载数字孪生系统，有效提升了虚实试验能力和效率，降低了试验成本和风险.
- 贮箱结构 /
- 极限承载 /
- 数字孪生 /
- 长短期记忆
Abstract: A digital twin methodology integrating physical measurements with computational modeling was proposed for predicting the ultimate load-bearing capacity of the key propellant tank structure in aerospace launch vehicles. First, a refined finite element model (FEM) was established based on the tank structural design and manufacturing process characteristics, to compute and analyze the structure strength, with results extracted specifically in positions corresponding to physical measurement points. Historical experimental data from structural strength tests were subsequently processed and analyzed. With both the test data and the simulation outputs, a comprehensive training dataset was constructed for the tank structure ultimate load-bearing capacity digital twin model. A long short-term memory (LSTM) network was then trained with this dataset to predict the structure ultimate load-bearing capacity. Finally, a dual-mode (offline and online interactive) digital twin system was implemented to predict the tank structure ultimate load-bearing performance. This proposed method significantly enhances virtual-physical testing ability and efficiency and reduces related testing costs and risks.
- tank structure /
- ultimate load-bearing capacity /
- digital twin /
- long short-term memory

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参考文献(14)

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文章访问数: 16
HTML全文浏览量: 2
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被引次数: 0

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模型数据混合驱动的火箭贮箱结构极限承载数字孪生技术

doi: 10.21656/1000-0887.450210

作者简介:
黄佳（1988—），女，高级工程师，硕士（E-mail: hj880616@126.com）;杨蓉（1978—），女，研究员，硕士（通讯作者. E-mail: rongyang_001@126.com）.

通讯作者:
杨蓉（1978—），女，研究员，硕士（通讯作者. E-mail: rongyang_001@126.com）.

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The Model and Data-Driven Digital Twin Technology for Ultimate Load-Bearing Capacity of the Rocket Propellant Tank Structure

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留言板

模型数据混合驱动的火箭贮箱结构极限承载数字孪生技术

doi: 10.21656/1000-0887.450210

作者简介: 黄佳（1988—），女，高级工程师，硕士（E-mail: hj880616@126.com）;杨蓉（1978—），女，研究员，硕士（通讯作者. E-mail: rongyang_001@126.com）.

通讯作者: 杨蓉（1978—），女，研究员，硕士（通讯作者. E-mail: rongyang_001@126.com）.

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出版历程

The Model and Data-Driven Digital Twin Technology for Ultimate Load-Bearing Capacity of the Rocket Propellant Tank Structure

计量

出版历程

目录

作者简介:
黄佳（1988—），女，高级工程师，硕士（E-mail: hj880616@126.com）;杨蓉（1978—），女，研究员，硕士（通讯作者. E-mail: rongyang_001@126.com）.

通讯作者:
杨蓉（1978—），女，研究员，硕士（通讯作者. E-mail: rongyang_001@126.com）.