Comparison and evaluation of advanced machine learning methods for performance and emissions prediction of a gasoline Wankel rotary engine
Comparison and evaluation of advanced machine learning methods for performance and emissions prediction of a gasoline Wankel rotary engine
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DOI：
10.1016/j.energy.2022.123611
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发表时间：
2022-02
期刊：
影响因子：
9
通讯作者：
Huaiyu Wang;C. Ji;Cheng Shi;Yunshan Ge;Hao Meng;Jinxin Yang;Ke Chang;Shuofeng Wang
中科院分区：
文献类型：
--
作者：
Huaiyu Wang;C. Ji;Cheng Shi;Yunshan Ge;Hao Meng;Jinxin Yang;Ke Chang;Shuofeng Wang
为了提高汽油汪克尔转子发动机（WRE）的性能、减少排放并提高标定效率，应用人工神经网络（ANN）、支持向量机（SVM）和高斯过程回归（GPR）三种先进的机器学习（ML）方法开发了扭矩、燃油流量、氮氧化物、一氧化碳和碳氢化合物的预测模型。使用 ANN、SVM 和 GPR 模型的推荐参数检查特征数的影响。结论是，使用速度、歧管绝对压力和空燃比作为输入参数来建立预测模型效果最好。使用扩展推荐参数在内插和外推数据集上比较了三种机器学习模型的泛化能力。结果表明，GPR模型在稀缺数据集上表现出最好的泛化能力，并且与ANN和SVM相比训练起来更简单。利用探地雷达模型构建的响应面非常光滑、准确，所有预测参数的决定系数均大于0.99。强烈建议探地雷达方法是一种通用方法，它将成为 WRE 系统控制和代理模型建模的重要方向。
In order to improve the performance, reduce the emissions and enhance the calibration efficiency of a gasoline Wankel rotary engine (WRE), three advanced machine learning (ML) methods, including artificial neural network (ANN), support vector machine (SVM), and Gaussian process regression (GPR), were applied to develop the prediction model of the torque, fuel flow, nitrogen oxide, carbon monoxide, and hydrocarbon. The effect of feature numbers was examined using the recommended parameters of the ANN, SVM, and GPR models. It was concluded that using speed, manifold absolute pressure, and air fuel ratio as input parameters to build the prediction model performed best. The generalization ability of the three ML models was compared on the interpolative and extrapolative data sets using the extended recommendation parameters. The results showed that the GPR model performed the best generalization ability in scarce data sets and was simpler to train compared with ANN and SVM. The response surfaces constructed using the GPR model were very smooth and accurate, in which the coefficient of determination for all the predicted parameters was more than 0.99. It is strongly proposed that the GPR approach is a universal approach which will be an essential direction for WRE system control and surrogate model modeling.