基于粒子群算法的搁浅船舶舱室调压脱浅方案优化

    Optimization of ballast adjustment for refloating a grounded ship with PSO algorithm

    • 摘要: 由于大吨位遇险船舶舱室较多,在脱浅救助工程中如果通过人工调整每个舱室的压载量以“试错”方式得出最小搁坐力,显然不能满足应急救助工作快速反应的要求。为解决这一问题,建立搁坐力理论模型,以GHS软件为船舶稳性求解器,通过粒子群算法将舱室的压载量作为优化因子,再采用Python编程将各个粒子信息传递给GHS软件,最后以最小搁坐力为目标函数求解出各个舱室的最优压载。在救助工程中通过该方法可快速得到优化的舱室压载方案,迅速确定关键舱室,再根据实际情况微调得出最优的脱浅方案。“帝祥”轮的顺利脱浅验证了方法的有效性,该方法可为大吨位遇险船舶救助工程提供理论支撑。

       

      Abstract: The particle swarm optimization algorithm(PSO) is introduced into the ballast adjustment calculation for refloating a grounded ship, which allows the design of the adjustment plan to get rid of trial-and-error experiments. A theoretic model of grounding force on ship is built. The GHS software is used as the stability resolver. The optimal ballast in every hold(cargo tank) is decided by PSO algorithm for minimum grounding force on the ship. The volumes of ballast in holds(cargo tanks), as the optimized factors, are fed to GHS software for stability checking via a Python program. Through iteration of the series of calculation, the ballast scheme for minimum grounding force is generated. This method was used for refloating the oil tanker “Dixiang” successfully.

       

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