基于模拟退火算法的船舶航速优化研究
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摘要
为了降低船舶单航次的主机总燃油消耗量,从而减少船舶的能源消耗。首先,计算了船舶的阻力,并基于大量船舶历史数据,构建了主机油耗模型,模型相对平均误差为3.6%;在船舶阻力计算的基础上,以船舶失速值和航向转向点为依据,将船舶的航线划分为不同分段;采用模拟退火智能算法,在航行时间、船舶航速、主机转速等约束条件下,以单航次总的燃油消耗量为优化目标,规划并得出了不同分段上的最佳航速和主机转速。计算结果显示,优化后船舶主机的燃油消耗量可降低3.1%,节油效果显著。
To reduce the total fuel consumption of the single voyage of main engine, so as to reduce the fuel consumption of ships. First, the resistance of the ship was calculated, and the fuel consumption model of the main engine was built based on a large number of ship historical data. The relative average error of the model is 3.6%. The ship's route was divided into different segments based on ship alteration point and stall value, which was on the basis of ship resistance calculation. Under the constraints of navigation time, ship speed and engine rotation rate, the optimal speed and rotation rate on different segments were planned and obtained by using the total fuel consumption as the optimization target, which was based on simulated annealing intelligent algorithm. The calculation result shows that the fuel consumption of the main engine can be reduced by 3.1% after optimization, and the fuel saving effect is remarkable.
To reduce the total fuel consumption of the single voyage of main engine, so as to reduce the fuel consumption of ships. First, the resistance of the ship was calculated, and the fuel consumption model of the main engine was built based on a large number of ship historical data. The relative average error of the model is 3.6%. The ship's route was divided into different segments based on ship alteration point and stall value, which was on the basis of ship resistance calculation. Under the constraints of navigation time, ship speed and engine rotation rate, the optimal speed and rotation rate on different segments were planned and obtained by using the total fuel consumption as the optimization target, which was based on simulated annealing intelligent algorithm. The calculation result shows that the fuel consumption of the main engine can be reduced by 3.1% after optimization, and the fuel saving effect is remarkable.
引文
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[2]徐延军,王敏.基于能效管理的船舶经济航速决策系统[J].中国航海,2013(4):135-138.
[3]陈前昆,严新平,尹奇志,等.基于EEOI的内河船舶航速优化研究[J].交通信息与安全,2014(185):87-91.
[4]刘玉霞,熊娟,金升平.基于模糊动态规划算法的船舶航速最优调度模型研究[J].新型工业化,2014(1):33-37.
[5]Norstad I,Fagerholt K,Laporte G.Tramp ship routing and scheduling with speed optimization[J].Transportation Research Part C Emerging Technologies,2011(5):853-865.
[6]Kim H J,Chang Y T,Kim K T,et al.An epsilon-optimal algorithm considering greenhouse gas emissions for the management of a ship’s bunker fuel[J].Transportation Research Part D,2012(2):97-103.
[7]Norlund E K,Gribkovskaia I.Reducing emissions through speed optimization in supply vessel operations[J].Transportation Research Part D Transport&Environment,2013(3):105-113.
[8]包子阳,余继周.智能优化算法及其MATLAB实例[M].北京:电子工业出版社,2016:127-128.
[9]盛振邦,刘应中.船舶原理[M].上海:上海交通大学出版社,2004:217-218
[10]洪碧光.船舶风压系数计算方法[J].大连海运学院学报,1991(2):113-121.
[11]Alvarez J,Arellano M.The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators[J].Econometrica,2003(4):1121-1159.
[12]李深洛.基于特征的时间序列聚类[D].广西师范大学,2014.