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

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

3. 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

4. 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

5. 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

6. 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

7. 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
Trained model (with NN)
0.02 2

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