船用中速柴油机预测模型的自动标定与优化  

Automatic Calibration and Optimization of Predictive Model for Marine Medium Speed Diesel Engine

作　　者：刘岱 李玉超 刘龙[1] 马修真[1] 桂勇 夏倩 LIU Dai;LI Yuchao;LIU Long;MA Xiuzhen;GUI Yong;XIA Qian(College of Power and Energy Engineering,Harbin Engineering University,Harbin 150001,China;China Shipbuilding Industry Power Engineering Institute Co.,Ltd.,Shanghai 200120,China)

机构地区：[1]哈尔滨工程大学动力与能源工程学院,哈尔滨150001 [2]中船动力研究院有限公司,上海200120

出　　处：《柴油机》2023年第3期14-19,共6页Diesel Engine

基　　金：科技部国家重点研发计划“船用柴油机可控燃烧技术研究”(2017YFE0116400)。

摘　　要：为解决柴油机预测模型参数过多导致的标定困难问题,采用遗传基因算法对搭建的船用中速机DIjet预测燃烧模型进行智能标定。借助最大缸内压力以及对应的曲轴转角快速完成缸压曲线标定,相较于传统手动标定,标定时间可缩短到几十分之一。在进行柴油机整体功率验证后,对NO_(x)排放进行标定,形成一套简便可行的智能标定流程。在完成标定的模型上进行NO_(x)排放及有效燃油消耗率(brake specific fuel consumption,BSFC)优化,在考虑两者不同权重系数的情况下得到更优的帕累托前沿面。In order to solve the calibration difficulties caused by excessive parameters in the diesel engine prediction model,a genetic algorithm was used to intelligently calibrate the combustion prediction model DIjet for a marine medium speed engine.By utilizing the maximum cylinder pressure and corresponding crankshaft angle,the cylinder pressure curve calibration was quickly completed,which reduced the calibration time to the several tenth of its original time compared to traditional manual calibration.After verifying the overall power of the diesel engine,the NO_(x) emissions was calibrated to form a simple and feasible intelligent calibration process.The NO_(x) and brake specific fuel consumption(BSFC)was optimized on the calibrated model,and a better Pareto front surface considering the different weight coefficients of the two was obtained.

关 键 词：中速柴油机 预测模型 遗传基因算法 自动标定 智能优化 

分 类 号：TK422[动力工程及工程热物理—动力机械及工程]