System Level Diesel Engine Emission Modeling Using Neural Networks

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Presentation transcript:

System Level Diesel Engine Emission Modeling Using Neural Networks ME 539 Project Presentation By Jian Gong Instructor: Prof. Yu Hen Hu December 14, 2010

Outline Background Literature Review Proposed Neutral Networks Model Neutral Network Structure Training and Testing Results and Discussion Future Work

Background Diesel engine emission Object Worldwide tightening of the emission regulations Need accurate prediction of engine emissions Challenges Engine-out emission involves thermo-fluid physics and complex chemical mechanisms Object ---To build system level predictive emission models using Neutral Networks

Literature Review-Modeling Zero-dimensional phenomenological models Simple & Low accuracy: involves basic thermal-fluid physics Fast Multi-dimensional CFD (Computational Fluid Dynamics) models Complicated & More accurate: involves detailed thermal-fluid physics and large reaction mechanism Extremely high computational cost System-level models Physical phenomenological model coupled with Neutral Networks Moderate accuracy & reasonable computation cost

Proposed Neutral Networks Model Neutral Network Structure Engine operating data (scalar) CO (carbon monoxide) Output layer Crank-angle resolved in-cylinder data (vector) 1st layer Input layer

Proposed Neutral Networks Model Notes on the Network Physic laws are acted as activation functions in each layer Currently, only three weights are used in the 2nd layer. More weights could be used in the 1st layer in the future. Training and testing Training method Back propagation algorithm Training & Testing data Totally 16 data set from experimental measurement (currently) 8 data set for training and another 8 data set for testing

Results and Discussion Challenges during training Trade-off between # of weights, accuracy and computational load Additional constrains and reduced degree of freedom due to physical laws eg. PV(w*x+w0) =Const (1.2<(w*x+w0)<1.7) Model W1 W2 W3 Mean Square Error Physical model (without NN) 1 0.77552 Trained model (with NN) 0.02 2 0.0558634

Future Work Carefully integrated more weights into the physic model (like 1st layer) Test the trained model using testing data Thank You~