基于试验设计-遗传算法的船用柴油机冷却系统多目标优化

doi:10.13675/j.cnki.tjjs.200317

推进技术 ›› 2020, Vol. 41 ›› Issue (11): 2518-2529.DOI: 10.13675/j.cnki.tjjs.200317

基于试验设计-遗传算法的船用柴油机冷却系统多目标优化

张博¹，张萍¹，郭旭²，曾凡明¹

1.海军工程大学动力工程学院，湖北武汉 430000;2.中国人民解放军驻芜湖军代室，安徽芜湖 241000

出版日期:2020-11-15 发布日期:2020-11-15
作者简介:张　博，博士生，研究领域为舰船动力装置自动化与仿真技术。E-mail：821021174@qq.com
基金资助:
“十三五”预研项目（3020401030403）；湖北省自然科学基金（2016CFB62）。

Multi-Objective Optimization of Marine Diesel Engine Cooling System Based on DOE-GA

1.College of Power Engineering，Naval University of Engineering，Wuhan 430000，China;2.Navy Representative Office of PLA in Wuhu，Wuhu 241000，China

Online:2020-11-15 Published:2020-11-15

摘要/Abstract

摘要： 为了获取船用柴油机冷却系统的最佳运行参数，在柴油机可靠运行的前提下，尽量提升动力性和经济性。建立了柴油机工作过程-燃烧室-冷却系统耦合仿真模型，提出试验设计-遗传算法优化算法，该算法具备节省试验成本和多目标全局寻优的优势。选取柴油机的6个典型推进工况点进行研究，采用该算法对冷却系统运行参数开展多目标优化设计，优化设计以海、淡水泵转速为变量因子，以提升动力性参数和经济性参数为目标，以涡前排温、淡水出机温度和峰值缸压为约束条件，优化后，在低负荷点，淡、海水泵节省功耗79.4%和71.73%，扭矩提升7.2%，有效功率提升7.6%，热效率提升8%，耗油率降低6.73%，摩擦功降低9.71%，峰值缸压降低5%。研究结果表明：耦合仿真模型与实机更加贴近；试验设计-遗传算法在解决强耦合、非线性系统的多目标优化问题具备成本低、效率高、精度高的优点。

关键词: 遗传算法;冷却系统;船用柴油机;耗油率;有效功率

Abstract: In order to obtain the best operation parameters of the cooling system of a marine diesel engine， achieving the goal of improving the power and economy under the premise of reliable operation of diesel engine， diesel engine working process-combustion chamber-cooling system coupling simulation model was established， and DOE-GA optimization algorithm was proposed which had the advantages of saving test costs and multi-objective global optimization. Six typical propulsion conditions were selected for research， DOE-GA optimization algorithm was used to carry out multi-objective optimization design on the operating parameters of the cooling system. The optimal design took the speed of sea and fresh water pumps as variable factors， and the promotion of dynamic and economic parameters as optimization goal， with turbine inlet exhaust temperature， fresh water outlet temperature， and peak cylinder pressure as constraints. After optimization， at the low load point， the fresh water and sea water pumps saved power consumption by 79.4% and 71.73%， torque increased by 7.2%， effective power increased by 7.6%， thermal efficiency increased by 8%， fuel consumption rate decreased by 6.73%， and friction work decreased by 9.71%， peak cylinder pressure decreased by 5%.The research results show that the coupled simulation model is closer to the real machine. The DOE-GA has the advantages of low cost， high efficiency， and high accuracy in solving multi-objective optimization problems of strongly coupled nonlinear systems.

Key words: Genetic algorithms;Cooling system;Marine diesel engine;Fuel consumption rate;Effective power

张博，张萍，郭旭，曾凡明. 基于试验设计-遗传算法的船用柴油机冷却系统多目标优化[J]. 推进技术, 2020, 41(11): 2518-2529.

ZHANG Bo¹, ZHANG Ping¹, GUO Xu², ZENG Fan-ming¹. Multi-Objective Optimization of Marine Diesel Engine Cooling System Based on DOE-GA[J]. Journal of Propulsion Technology, 2020, 41(11): 2518-2529.

参考文献

[1] 孙培廷. 船舶柴油机［M］. 大连：大连海事大学出版社， 2002.
[2] 杨艳玲. 浅析柴油机的冷却系统［J］. 黑龙江交通科技， 2005，（4）： 67-69.
[3] 任林. 船用大功率中高速柴油机冷却系统数值分析与试验研究［D］. 北京：中国舰船研究院， 2012.
[4] Caglar D， Cengiz D. Load Optimization of Central Cooling System Pumps of a Container Ship for the Slow Steaming Conditions to Enhance the Energy Efficiency［J］. Journal of Cleaner Production， 2019， 222（2）： 151-157.
[5] 李文耀. 船舶主机冷却水系统的温度智能控制系统研究［J］. 舰船科学技术， 2018， 40（8）： 64-66.
[6] Arya K H， Soheil R， Navid M， et al. An Intelligent Cooling System and Control Model for Improved Engine Thermal Management［J］. Applied Thermal Engineering， 2018， 128（4）： 57-64.
[7] 唐强. 船舶中央冷却系统仿真与智能控制研究［D］. 上海：上海交通大学， 2015.
[8] Masatoshi N， Jin K， Hisafumi D， et a1. Prediction Method of Cooling System Performance［R］. SAE-TP-930146， 1993.
[9] Sadek R， Richard S. Robust Engineering of Engine Cooling System［R］. SAE-TP-2003-01-0149.
[10] Clive H， Chad M， Frederic J， et a1. Heavy Duty Truck Cooling System Design Using Co-Simulation［R］. SAE-TP-2001-01-1707.
[11] Kamel M， Johann K. AVL Solution Thermal Management During Engine Warm-Up［C］. Changchun： Flowmaster User Meeting， 2006.
[12] Mahmoud K G， Loibner E， Wiesler B， et al. Simulation-Based Vehicle Thermal Management System Concept and Methodology［R］. SAE-TP-2003-01-0276.
[13] 吴桂涛，孙培廷. 船舶中央冷却系统的热力计算数学模型［J］. 大连海事大学学报（自然科学版）， 2002， 28（1）： 13-15.
[14] 李学艳. 柴油机数字化冷却系统控制策略研究［D］. 北京：北京理工大学， 2008.
[15] 刘晓东，石秀勇，倪计民. 基于最小耗功的发动机冷却系统设计研究［J］. 汽车科技， 2012，（2）： 42-45.
[16] 李儒男. 乘用车智能冷却系统控制策略研究［D］. 杭州：浙江大学， 2017.
[17] National Renewable Energy Laboratory. Co-Optima Research Yields Potential Bioblendstock for Diesel Fuel［J］. NewsRx Health & Science， 2020， 20（2）： 89-93.
[18] Ali N， Shahram K， Mohammad A. Corrigendum to “Diesel Engine Optimization with Multi-Objective Performance Characteristics by Non-Evolutionary Nelder-Mead Algorithm： Sobol Sequence and Latin Hypercube Sampling Methods Comparison in DoE Process”［J］. Fuel， 2019， 243（6）： 320-328.
[19] Syed A P Q， Masood M， Tabish W， et al. DOE Applied to Multi-Response Optimisation Study on Performance， Combustion and Emission Characteristics of a VCR Diesel Engine［J］. International Journal of Ambient Energy， 2017， 38（8）： 201-208.
[20] Milad F， Hadi G， Towhid P， et al. Exergoeconomic Analysis and Optimization of a New Combined Power and Freshwater System Driven by Waste Heat of a Marine Diesel Engine［J］. Thermal Science and Engineering Progress， 2020， 18（2）： 116-124.

基于试验设计-遗传算法的船用柴油机冷却系统多目标优化

Multi-Objective Optimization of Marine Diesel Engine Cooling System Based on DOE-GA

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