赵柯洋,张衡,贺波,等.基于改进第二代非支配遗传算法的甲醇/柴油双燃料船舶发动机参数多目标优化[J].内燃机工程,2025,46(1):17-26.
基于改进第二代非支配遗传算法的甲醇/柴油双燃料船舶发动机参数多目标优化
Multi-Objective Optimization of Methanol/Diesel Dual-Fuel Marine Engine Parameters Based on Improved Non-Dominated Sorting Genetic Algorithm-Ⅱ
DOI：
关键词:甲醇/柴油  第二代非支配遗传算法  灰熵并行  性能优化
Key Words:methanol/diesel  non-dominated sorting genetic algorithm-Ⅱ（NSGA-Ⅱ）  grey entropy parallel  performance optimization
基金项目:舟山市科技计划项目（2022C41009）
作者单位E-mail
赵柯洋* 浙江海洋大学 海洋工程装备学院，舟山 316022
中创海洋科技股份有限公司，舟山316022
中国科学院宁波材料技术与工程研究所，宁波315201 		zhaoky@zjou.edu.cn 
张衡 浙江海洋大学 海洋工程装备学院，舟山 316022 zhangheng7812@163.com 
贺波 中创海洋科技股份有限公司，舟山316022 hebo@jessn.com 
庞轶星 浙江海洋大学 海洋工程装备学院，舟山 316022 pyx2652445711@163.com 
程星鑫 浙江海洋大学 海洋工程装备学院，舟山 316022 1012189774@qq.com 
苏玉香 浙江海洋大学 海洋工程装备学院，舟山 316022 514457673@qq.com 
李又一 浙江海洋大学 海洋工程装备学院，舟山 316022 youyili@zjou.edu.cn 
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摘要:利用一维发动机仿真软件GT-Power搭建了甲醇/柴油M15（即甲醇燃料所占体积比例为15%）混合燃料缸内直喷模型，选取转矩、比燃油消耗率、NOx排放量和CO排放量为优化目标，以发动机的进排气门正时角度、压缩比及空燃比为优化参数开展多目标优化。为实现多目标优化问题的有效求解，采用灰熵并行分析方法改进第二代非支配遗传算法（non-dominated sorting genetic algorithm-Ⅱ， NSGA-Ⅱ），并利用建立的响应面模型对仿真模型计算结果进行了仿真验证。优化结果显示：转矩提升了6.96%，比燃油消耗率降低了1.19%，NOx和CO排放量分别降低了12.37%和3.77%。
Abstract:The 1D engine simulation software GT-Power was used to build a direct injection model of methanol/diesel M15 （15% volume of methanol fuel） mixed fuel cylinder， and the torque， specific fuel consumption rate， NOx emissions and CO emissions were selected as the optimization targets， and the intake and exhaust valve timing angles， compression ratio and air-fuel ratio of the engine were used as the optimization parameters to carry out multi-objective optimization. In order to solve the multi-objective optimization problem effectively， the second-generation non-dominated sorting genetic algorithm-Ⅱ （NSGA-Ⅱ） was improved by using the gray entropy parallel analysis method， and the simulation results were verified by using the established response surface model. The optimization results show that the torque is increased by 6.96%， the specific fuel consumption rate is reduced by 1.19%， and the NOx and CO emissions are reduced by 12.37% and 3.77%， respectively.
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