Structural Optimization of Cylindrical Pressure Hulls via NSGA-II.

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Bibliographic Details
Title:	Structural Optimization of Cylindrical Pressure Hulls via NSGA-II.
Authors:	Zhang, Ya¹ zhangya_0426@163.com, Tao, Zhicong² tzc201129@163.com, Wang, Hao² 1204157459@qq.com, Li, Huixin² 2339261662@qq.com, Zeng, Zihao² 627571028@qq.com
Source:	IAENG International Journal of Applied Mathematics. Jul2026, Vol. 56 Issue 7, p2657-2666. 10p.
Subjects:	Structural optimization, Multi-objective optimization, Response surfaces (Statistics), Genetic algorithms, Latin hypercube sampling, Hulls (Naval architecture), Mechanical buckling
Abstract:	To enhance the stability and safety of submersible pressure hull structures in deep-sea environments, this study first conducts a parametric analysis of an initial ring-stiffened cylindrical pressure hull design. The optimal Latin hypercube sampling method is employed to generate sample points and to evaluate the response of design variables to the optimization objectives. A high-fidelity fourth-order response surface model is then constructed, followed by the formulation of a multi-objective optimization model that considers structural mass, strength, and buckling stability as competing objectives. The NSGA-II genetic algorithm is subsequently applied to perform non-dominated sorting and crowding distance calculation. This yields four distinct Pareto-optimal schemes, each exhibiting varying degrees of improvement across the defined objectives. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

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Abstract:	To enhance the stability and safety of submersible pressure hull structures in deep-sea environments, this study first conducts a parametric analysis of an initial ring-stiffened cylindrical pressure hull design. The optimal Latin hypercube sampling method is employed to generate sample points and to evaluate the response of design variables to the optimization objectives. A high-fidelity fourth-order response surface model is then constructed, followed by the formulation of a multi-objective optimization model that considers structural mass, strength, and buckling stability as competing objectives. The NSGA-II genetic algorithm is subsequently applied to perform non-dominated sorting and crowding distance calculation. This yields four distinct Pareto-optimal schemes, each exhibiting varying degrees of improvement across the defined objectives. [ABSTRACT FROM AUTHOR]
ISSN:	19929978