针对柴油发动机推进特性下的中高负荷工况出现的NO_(x)排放峰值现象,以及燃油价格日益上涨带来降低油耗率的迫切需求,本研究通过调节柴油/甲醇组合燃烧(diesel/methanol compound combustion,DMCC)发动机多种控制参数,在保证动力性前提...针对柴油发动机推进特性下的中高负荷工况出现的NO_(x)排放峰值现象,以及燃油价格日益上涨带来降低油耗率的迫切需求,本研究通过调节柴油/甲醇组合燃烧(diesel/methanol compound combustion,DMCC)发动机多种控制参数,在保证动力性前提下,实现NO_(x)排放和有效燃油消耗率(brake specific fuel consumption,BSFC)的同步下降。为避免大规模试验带来的成本增加,首先基于高斯过程回归建立DMCC发动机排放的NO_(x)体积分数、BSFC和指示功率预测模型;然后将所建模型与第二代非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)结合,对NO_(x)的体积分数和BSFC进行优化,并将Pareto前沿解集代入逼近理想解排序法(the technique for order preference by similarity to an ideal solution,TOPSIS)寻找最优控制参数组合;最后将最优控制参数组合标定至电子控制单元,与原机数据进行对比分析。结果表明:基于高斯过程回归建立的预测模型的拟合优度大于0.95,均方根误差小于1,具有良好的一致性和准确性;使用NSGA-Ⅱ获取的最佳控制参数与优化前(原机工况)的相比,NO_(x)的排放量下降74.5%,仅为3.47 g/(kW·h),BSFC平均下降6.7%,仅为203.5 g/(kW·h)。展开更多
Misfire is a common fault in compression ignition engines,characterized by the absence or flame loss due to insufficient fuel in the cylinders.This fault is difficult to diagnose and resolve due to its multiple potent...Misfire is a common fault in compression ignition engines,characterized by the absence or flame loss due to insufficient fuel in the cylinders.This fault is difficult to diagnose and resolve due to its multiple potential causes.This study focuses on identifying misfires in a 12-cylinder V-type marine diesel engine by analyzing vibration data collected from 15 accelerometers mounted on the engine block.Three machine learning algorithms—K-Nearest Neighbors(K-NNs),support vector machines(SVMs),and random forests(RFs)—were employed to classify engine conditions using 18 time-domain features.Results showed that the K-NN,SVM and RF algorithms achieved F1 scores of 99.87%,100%,and 99.87%,respectively,when using 18 time-domain features and all 15 accelerometers mounted on the engine block.Additionally,the study evaluated classification performance while reducing the number of accelerometers and features using two methods:Relief-F and general combinatory analysis(GCA).Although the GCA method yields better results when using only two accelerometers and nine features for misfire classification,its overall process required substantially more computational time compared to Relief-F.The best result obtained with Relief-F was achieved using 3 accelerometers and 18 features.Therefore,Relief-F proved to be more practical and take less overall computational time within the proposed framework.展开更多
文摘针对柴油发动机推进特性下的中高负荷工况出现的NO_(x)排放峰值现象,以及燃油价格日益上涨带来降低油耗率的迫切需求,本研究通过调节柴油/甲醇组合燃烧(diesel/methanol compound combustion,DMCC)发动机多种控制参数,在保证动力性前提下,实现NO_(x)排放和有效燃油消耗率(brake specific fuel consumption,BSFC)的同步下降。为避免大规模试验带来的成本增加,首先基于高斯过程回归建立DMCC发动机排放的NO_(x)体积分数、BSFC和指示功率预测模型;然后将所建模型与第二代非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)结合,对NO_(x)的体积分数和BSFC进行优化,并将Pareto前沿解集代入逼近理想解排序法(the technique for order preference by similarity to an ideal solution,TOPSIS)寻找最优控制参数组合;最后将最优控制参数组合标定至电子控制单元,与原机数据进行对比分析。结果表明:基于高斯过程回归建立的预测模型的拟合优度大于0.95,均方根误差小于1,具有良好的一致性和准确性;使用NSGA-Ⅱ获取的最佳控制参数与优化前(原机工况)的相比,NO_(x)的排放量下降74.5%,仅为3.47 g/(kW·h),BSFC平均下降6.7%,仅为203.5 g/(kW·h)。
文摘Misfire is a common fault in compression ignition engines,characterized by the absence or flame loss due to insufficient fuel in the cylinders.This fault is difficult to diagnose and resolve due to its multiple potential causes.This study focuses on identifying misfires in a 12-cylinder V-type marine diesel engine by analyzing vibration data collected from 15 accelerometers mounted on the engine block.Three machine learning algorithms—K-Nearest Neighbors(K-NNs),support vector machines(SVMs),and random forests(RFs)—were employed to classify engine conditions using 18 time-domain features.Results showed that the K-NN,SVM and RF algorithms achieved F1 scores of 99.87%,100%,and 99.87%,respectively,when using 18 time-domain features and all 15 accelerometers mounted on the engine block.Additionally,the study evaluated classification performance while reducing the number of accelerometers and features using two methods:Relief-F and general combinatory analysis(GCA).Although the GCA method yields better results when using only two accelerometers and nine features for misfire classification,its overall process required substantially more computational time compared to Relief-F.The best result obtained with Relief-F was achieved using 3 accelerometers and 18 features.Therefore,Relief-F proved to be more practical and take less overall computational time within the proposed framework.
