基于Mars2000的船舶中剖面通用快速优化方法

General fast optimization method for midship section based on Mars2000

  • 摘要:
      目的  船舶中剖面优化具有设计变量多、约束条件复杂的特点。相关研究多采用智能优化算法直接嵌套规范校核软件(如Mars2000)的优化方法,计算量较大。为此,提出基于敏度排序的中剖面快速优化方法。
      方法  首先计算各个约束特征量关于各设计变量的敏度,根据敏度信息得到各特征量不满足约束时的设计变量调整次序,并判断各特征量是否只与局部构件相关。优化迭代时,根据当前方案的约束不满足情况,结合敏度信息做变量调整,并进行周期性的敏度更新。最后引入基于坐标轮换法的小范围调整方法进一步提升优化效果。
      结果  某油船中剖面算例优化结果表明,所提方法可实现结构减重5.195%。
      结论  与直接嵌套Mars2000的智能优化算法相比,本方法在优化效果相当的情况下,计算量仅为前者的5.58%左右,成本优势明显。

     

    Abstract:
      Objectives  The optimization of midship sections is characterized by the large amount of design variables and the complex constraints. Most relevant research applied the intelligent optimization algorithm embedded with the rule-based calculation program (e.g., Mars2000) from classification society to deal with this issue, which has a large computation cost. Therefore, a general fast optimization method based on sensitivity ranking is proposed for the optimization of midship sections.
      Methods  Firstly, the sensitivity of each constraint about each design variable was evaluated. According to the result of sensitivity, the order of design variables to be modified can be obtained when each constraint is violated. Whether the constraint is only related to local variables or not can be determined as well. During optimization iteration, based on the constraint violation of the current scheme, variable adjustment can be made with the above sensitivity information, and the sensitivity result was updated periodically. Finally, minor adjustment of optimized schemes based on coordinate alternation was employed to further improve the optimization effect.
      Results  The optimization result of an oil tanker midship section shows that the proposed method can achieve a 5.195% reduction of weight.
      Conclusions  Compared with the intelligent optimization algorithm nesting Mars2000 directly, the optimization effect of the proposed method is satisfactory, and the time cost is only 5.58% of the former. The advantage of the proposed method in time cost is quite obvious.

     

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