初步设计阶段中型邮轮尺度多目标优化研究

李雷雷; 吴四川

doi:10.19693/j.issn.1673-3185.02475

初步设计阶段中型邮轮尺度多目标优化研究

doi: 10.19693/j.issn.1673-3185.02475

李雷雷^1, ,,
吴四川²

1.
武汉长江船舶设计院有限公司，湖北武汉 430062
2.
招商局工业集团有限公司，广东深圳 518063

基金项目: 工信部高技术船舶科研资助项目(MC-201917-C09)

详细信息

作者简介:
李雷雷，男，1985年生，硕士，高级工程师

吴四川，男，1975年生，硕士，高级工程师

通信作者:
李雷雷

中图分类号: U662.2；U674.11
计量
- 文章访问数: 243
- HTML全文浏览量: 56
- PDF下载量: 29
- 被引次数: 0
出版历程
- 收稿日期: 2021-08-04
- 修回日期: 2021-11-02
- 网络出版日期: 2023-03-16
- 刊出日期: 2023-04-28

Multi-objective optimization study on dimensions of medium-sized cruise ship in concept phase

LI Leilei^{1
, ,},
WU Sichuan²

1.
Wuhan Changjiang Ship Design Institute Co., Ltd., Wuhan 430062, China
2.
China Merchants Industry Holdings Co., Ltd., Shenzhen 518063, China

初步设计阶段中型邮轮尺度多目标优化研究由李雷雷，等创作，采用知识共享署名4.0国际许可协议进行许可。

摘要

摘要: 目的针对中型邮轮初步设计阶段确定尺度时影响因素多、参考资料少的问题，提出一种基于尺度与性能指标拟合简化的多目标优化方法。方法首先，对国际在航邮轮尺度进行分析，高精度拟合中型邮轮尺度特征参数，并结合最新公约要求，确定尺度优化范围。然后，分析邮轮尺度参数对空间、阻力、稳性、耐波性的影响程度，建立目标表达方式；并基于遗传算法原理，确定鲁棒性、工程适应性高的多目标优化算法，以某中型邮轮初步设计尺度优化为目标，建立多目标优化模型并展开研究。最后，分析多目标优化得到的Pareto解集，优选目标邮轮的初始尺度。结果基于遗传算法实现的优化程序，在某中型邮轮初步设计阶段进行了工程实践应用，为该邮轮提供了合理的初始尺度。结论简化的多目标优化模型为中型邮轮初步设计阶段主尺度决策提供了一种解决方案；多目标优化得到的Pareto集，不同解针对邮轮阻力、稳性等不同的技术性能指标有不同的侧重点，工程中可根据设计需求选择适用解。
- 中型邮轮 /
- 尺度 /
- 多目标优化 /
- Pareto解
Abstract: Objective Aiming at the problem of too many influencing factors and too little reference data for determining the dimensions of medium-sized cruise ships in the concept phase, a simplified multi-objective optimization method based on the fitting of dimensions and performance is proposed. Method First, the dimension relations of medium-sized cruise ships are analyzed and the influence of the latest SOLAS requirements used to determine the optimization range. Second, the influence of cruise ship dimensions on space, resistance, stability and seakeeping are analyzed. Next, based on the principles of genetic algorithms, a multi-objective optimization algorithm with high robustness and high engineering adaptability is determined to establish a multi-objective optimization model for the concept design of medium-sized cruise ships. Finally, the Pareto solution obtained by multi-objective optimization is analyzed to provide initial references for determining the dimensions of the target cruise ship. Results Implemented via a genetic algorithm, the optimization program proposed herein is applied in the concept design of a medium-sized cruise ship in order to optimize the initial dimensions, thereby achieving the expected outcome of providing reasonable initial dimensions for cruise ship design. Conclusion The proposed simplified multi-objective optimization model can provide feasible initial dimensions for medium-sized cruise ships in the concept phase. As the Pareto solution obtained by multi-objective optimization has different focuses such as resistance and stability, the most suitable solution needs to be selected according to the design object.
- medium-sized cruise ship /
- dimensions /
- multi-objective optimization /
- Pareto solution

HTML全文

图 1 典型二目标优化问题的Pareto解集

Figure 1. Pareto solution of a typical two-objective problem

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图 2 NSGA-III优化算法流程

Figure 2. Flow chart of NSGA-III optimization algorithm

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图 3 邮轮船宽与总吨位关系

Figure 3. Relationship between breadth and gross tonnage of cruise ships

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图 4 中型邮轮总吨位与船长拟合曲线

Figure 4. Fitting curve between length and gross tonnage of medium-sized cruise ships

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图 5 中型邮轮总吨位与船宽拟合曲线

Figure 5. Fitting curve between breadth and gross tonnage of medium-sized cruise ships

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图 6 中型邮轮总吨位与吃水关系拟合曲线

Figure 6. Fitting curve between draft and gross tonnage of medium-sized cruise ships

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图 7 中型邮轮总吨位与旅客甲板层数拟合曲线

Figure 7. Fitting curve between number of decks and gross tonnage of medium-sized cruise ships

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图 8 中型邮轮总吨位与乘客人数拟合曲线

Figure 8. Fitting curve between passenger count and gross tonnage of medium-sized cruise ships

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图 9 中型邮轮总吨位与客房数拟合曲线

Figure 9. Fitting curve between rooms count and gross tonnage of medium-sized cruise ships

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图 10 邮轮人均吨位分布

Figure 10. Distribution of gross tonnage per passenger on cruise ships

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图 11 不同得到的G_M回归分析的Pareto图

Figure 11. Pareto diagram for regression analysis with the impact of G_M

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图 12 A影响回归分析的Pareto图

Figure 12. Pareto diagram for regression analysis with the impact of A

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图 13 非支配解示意图

Figure 13. Schematic diagram of non-dominated set

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图 14 推荐解选取示意图

Figure 14. Schematic diagram of recommended solution selection

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表 1 决策变量上限和下限

Table 1. Upper and lower limit of decision variables

参数	下限值	上限值
船长L/m	210	250
型宽B/m	27	32
吃水T/m	6.2	7.0
方形系数$ {C_{\text{b}}} $	0.62	0.72

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表 2 Pareto解集特例

Table 2. Special case in Pareto solution

L	B	T	C_b	E_HP	G_M	A	$ {T_\theta } $	R₁
249.8	29.54	6.30	0.649	1.000 0	0.220 0	0.115 0	0.460 0	0.211 0
230.1	31.97	6.24	0.652	0.860 0	1.000 0	0.926 0	0	0.065 0
230.0	32.00	6.20	0.717	0	0.996 0	1.000 0	0.074 0	0.797 0
245.8	29.66	6.66	0.689	0.559 0	0.013 0	0.028 0	1.000 0	0.479 0
249.4	29.73	6.20	0.720	0.175 0	0.241 3	0.214 5	0.547 7	1.000 0

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