摘要
提出了基于神经网络的零维预测燃烧模型,适用于发动机稳态和动态过程的燃烧计算.以建立高原增压柴油机燃烧模型为例,介绍了基于神经网络的零维预测燃烧模型的建模步骤,主要包括放热率计算、放热率参数化、构建和训练神经网络.介绍了缸压处理和放热率计算方法;以三韦伯函数形式拟合放热率曲线,增加一系列约束解决拟合参数的多解问题;构建并训练了神经网络,完成建模.通过训练误差分析、预测结果与试验数据的对比,对模型的预测精度进行了验证.
Zero-D predictable combustion model on the basis of neural network was put forward,which is appropriate to the combustion prediction for both steady and dynamic engine simulation. Main procedures for building a predictable model were introduced,including calculation for the rate of heat release(Ro HR),parameterization for Ro HR,establishing and training the neural network. Firstly,the in-cylinder pressure curve was smoothed using average method and the Ro HR was obtained by thermodynamics. Then,mathematical algorithms were adopted to fit the Ro HR in tri-Wiebe function. To solve the multiple solutions in fitting,some constraints were put forward by analysis of parameters meanings. Lastly the radial basis function(RBF)neutral network was established and trained to complete the zero-D predictable combustion model. The accuracy of the model was validated by the training error analysis and comparison between predicted results and experimental data.
出处
《内燃机学报》
EI
CAS
CSCD
北大核心
2015年第2期163-170,共8页
Transactions of Csice
基金
省部级资助项目(D2220112901)
关键词
神经网络
零维燃烧模型
放热率
参数拟合
neural network
zero-D combustion model
rate of heat release
parameter fitting
