计及结构实际应力状态的模型降阶技术研究
Research on model reduction technique considering the actual stress state of structures
昝一鸣 1孔祥韶 2王卓 1王一雯 2吴卫国2
作者信息
- 1. 武汉理工大学绿色智能江海直达船舶与邮轮游艇研究中心,湖北武汉 430063;武汉理工大学船海与能源动力工程学院,湖北武汉 430063
- 2. 武汉理工大学绿色智能江海直达船舶与邮轮游艇研究中心,湖北武汉 430063
- 折叠
摘要
为解决目前降阶模型缺乏精确性及可交互性问题,提出一种计及结构实际应力状态的模型降阶及修正技术.基于本征正交分解方法创建由载荷-模态系数映射及基底向量组成的变截面梁结构降阶模型,通过试验测点响应数据求解结构实际应力状态下的模态系数进行映射更新,迭代选取最优映射以实现降阶模型的修正,并经由静载及动载试验对修正后的降阶模型进行对比验证.相较传统模型降阶方法,计及实际应力状态的降阶模型在垂弯静载试验中精度优化52.13％,在波浪载荷动载试验中精度优化86.98％,且在保证精度同时提升了 84.81％计算效率,为数字孪生中物理和虚拟空间的交互提出一种新的解决思路,进一步满足孪生体结构状态实时性,精确性及可交互性的需求,为实际船舶结构数字孪生体的构建提供技术支撑.
Abstract
To mitigate the challenges associated with inaccuracy and lack of interactivity in existing model order reduc-tion techniques,a model reduction and correction technique is proposed that incorporates the actual stress state of the struc-ture.Utilizing the proper orthogonal decomposition method,a reduction model for a variable cross section beam structure is developed,which comprises a mapping of a load-modal coefficients and basis vectors.The modal coefficients undergo map-ping and updating procedures,which are based on the actual stress state of the structure as determined by response data ob-tained from testing points.An iterative process is employed to select the most suitable mapping technique and achieve cor-rection for model reduction.The corrected reduced model is validated through static and dynamic testing.Compared to con-ventional model reduction methods,the model reduction technique that considers the actual stress state achieved an accuracy improvement of 52.13％in bending static tests and 86.98％in wave loading dynamic tests,while increasing computational efficiency by 84.81％.This presents a novel approach to address the interaction of physical and virtual spaces in digital twin-ning,further meeting the requirements of real-time,accurate,and interactive digital twin structures for ships,thereby offer-ing technical assistance for the development of authentic ship structure digital twins.
关键词
数字孪生/降阶模型/本征正交分解Key words
digital twin/reduced order model/proper orthogonal decomposition引用本文复制引用
出版年
2024
NETL
NSTL
万方数据