摘要
提出一种基于仿真数据库及数字孪生技术的深潜球壳应力场预报方法,通过建立不同尺度及载荷下耐压球壳的应力场分布仿真数据库,在潜水器上实现了通过单个耐压壳有限传感器布点虚拟传感监测其他关键部位的应力状态。基于数字孪生技术构建三级虚拟结构层,Level-1 DT层实现从有限元仿真模型到数字模型的空间映射及云图展示,球壳的极限承载力实验与数值结果对比误差小于9.4%。Level-2 DT层通过创建数据库实现数字模型的数据样本推演,仿真数据库中未获得尺寸及载荷条件下的球壳应力场分布通过局部拉格朗日插值方法获得,插值应力结果相对于仿真结果的相对误差为4.8%。Level-3 DT层开发了深潜球壳数字模型危险区域应力场分布的机器学习预报功能,通过粒子群算法优化后的BP神经网络保证预测结果与仿真结果的误差小于1%。该方法综合考虑材料性能、结构尺寸和环境载荷,可以为耐压壳结构的实时安全评估提供参考,实现对潜水器单个或多个球壳动应力场分布的动态感知、智能诊断和科学预测。
This paper presents a method for predicting the stress field of deep diving spherical shells based on simulation databases and digital twin technology.By establishing simulation databases of stress field distribution of pressure-resistant spherical shells under different scales and loads,virtual sensing monitoring of stress states in other parts of the vessel is realized through finite sensor layout of pressureresistant shells on the submersible.Based on the DT(digital twin)technology,a three-level virtual structure layer is constructed.The Level-1 DT layer realizes the spatial mapping and cloud image display from the finite element simulation model to the digital model.The error between the experimental and numerical results of the ultimate bearing capacity of the spherical shell is less than 9.4%.The Level-2 DT layer realizes the data sample deduction of the digital model by create database.The stress field distribution of the spherical shell under the condition that the size and load are not obtained in the simulation database is obtained by the local Lagrange interpolation method.The relative error of the stress interpolation result is 4.8%.The Level-3 DT layer develops a machine learning prediction function for the stress field distribution in the dangerous area of the deep-submersible spherical shell digital model.The BP neural network optimized by the particle swarm optimization algorithm ensures that the error between the prediction result and the simulation result is less than 1%.This method comprehensively considers the material properties,structural dimensions and environmental loads,which can provide a reference for the real-time safety assessment of the pressure hull structure,and realize the dynamic perception,intelligent diagnosis and scientific prediction of the dynamic stress field distribution of all deep-submersible spherical shells on the hull.
作者
曹宇
李杰
王芳
刘智翔
汪雪良
Cao Yu;Li Jie;Wang Fang;Liu Zhixiang;Wang Xueliang(Shanghai Ocean University,Shanghai 201306,China;State Key Laboratory of Industrial Equipment Structure Analysis of Dalian University of Technology,Dalian 116081,China;China Ship Scientific Research Center,Wuxi 214082,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2024年第8期1764-1779,共16页
Journal of System Simulation
基金
国家自然科学基金(52371282)
大连理工大学工业装备结构分析国家重点实验室开放课题基金(GZ22113)。
关键词
仿真数据库
数字孪生
深潜球壳
应力场分布
优化算法
simulation database
digital twin
deep submerged spherical shell
stress field distribution
optimization algorithm
