摘要
正庚烷-乙醇混合燃料的自燃温度对研究反应控制压燃（RCCI）具有重要的参考意义。采用BP神经网络预测正庚烷-乙醇混合燃料自燃温度，该神经网络模型以正庚烷掺混比、当量比和进气压力为输入，自燃温度为输出，单层隐含层有16个节点时迭代过程均方误差和训练状态梯度均最小。研究结果表明：对神经网络模型训练、验证、测试的线性系数和全局线性系数R分别为0.997 78，0.997 9，0.994 92和0.997 33，预测精度较高；验证了该神经网络模型对正庚烷掺混比、当量比和进气压力变化的泛化能力，预测值与试验值的误差均在允许范围内，因此本模型得到的预测值与试验值具有良好的一致性。
Abstract
The autoignition temperature of nheptane and ethanol blended fuel has an important reference significance for studying the reaction controlled compression ignition. The autoignition temperature of nheptane and ethanol blended fuel was predicted by BP neural network, which used nheptane mixing ratio, equivalence ratio and inlet pressure as input and autoignition temperature as output. When there were 16 nodes in a single hidden layer, the mean square error in the iterative process and the training state gradient were the minimum. The results show that the prediction accuracy is higher when the training, verification, test and global linear coefficients R of neural network model are 0.997 78, 0.997 9, 0.994 92 and 0.997 33 respectively. The generalization ability of neural network model for the variation of n heptane mixing ratio, equivalence ratio and inlet pressure is verified and the error between the predicted and experimental values is within the allowable range. Therefore, the predicted values obtained by this model are in good agreement with the experimental values.
关键词
BP神经网络 /
正庚烷 /
乙醇 /
混合燃料 /
自燃温度 /
预测
Key words
BP neural network /
nheptane andethanol /
ethanol /
blended fuel /
auto-ignition temperature /
prediction
陈若龙，韩永强，李润钊，张一鸣，安东，孙博.
基于BP神经网络预测正庚烷-乙醇混合燃料自燃温度[J]. 车用发动机. 2018, 0(1): 10-15 https://doi.org/10.3969/j.issn.1001-2222.2018.01.002
CHEN Ruolong，HAN Yongqiang，LI Runzhao，ZHANG Yiming，AN Dong，SUN Bo.
Prediction on Auto ignition Temperature of N heptane and Ethanol Blended Fuel Based on BP Neural Network[J]. Vehicle Engine. 2018, 0(1): 10-15 https://doi.org/10.3969/j.issn.1001-2222.2018.01.002
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