.png){kind=icon}
返回
Surrogate-based structural optimization design of large-scale rectangular pressure vessel using radial point interpolation method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijpvp.2022.104638.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Large-scale rectangular pressure vessel possesses wide application due to volume efficiency and the convenience of transporting and installing. Despite there being increasing emphasis on weight reduction, the optimization design method for large-scale rectangular pressure vessel is still under development due to complicated geometry and engineering constraints that make conventional optimization approaches incapable. Therefore, a surrogate based optimization design method is proposed in this work to resolve the aforementioned difficulties. The proposed technique adopts radial point interpolation method to construct surrogate model, which is validated with classical numerical examples and a rectangular pressure vessel, i.e., the test section of a large-scale water tunnel. Fair convergence and precision have been revealed. Then, the surrogate-based optimization framework is established on the basis of the surrogate model, involving optimization scheme, sampling range and constraints, sampling method, standard optimization model and method. Subsequently, the investigated rectangular pressure vessel is optimized using the developed method. The optimized structure maintains good machinability and reduces weight by 10.06% in comparison to the results of preliminary routine structural design, proving that the developed technique possesses good capacity for the optimization design of large-scale rectangular pressure vessels.
Keyword:
Rectangular pressure vessel
Large-scale
Structural optimization design
Surrogate model
Radial point interpolation method
期刊
IF:
3.5
论文数:
4.0K
被引数:
6.6K
机构
引用论文
A comparative reliability study of corroded pipelines based on Monte Carlo Simulation and Latin Hypercube Sampling methods基于蒙特卡罗模拟和拉丁超立方抽样方法的腐蚀管道可靠性比较研究
.png){kind=icon}
PREAI
分享收藏
分享收藏
查看更多内容