1. Development of mean value engine model using ANN
Soo-Youl Park (Mechanical Engineering)

2. Problem Definition Engine simulation model
Physical model (Gas dynamics, Combustion, thermodynamics)
Generally, More complex more accurate and more time consuming in calculation
Need for fast running model guaranteeing high accuracy  hopefully, running at real time
Minimizing the use of physical model by incorporating ANN  Mean value engine model

3. How to make it Build a fully physical based model
Using DOE (Design of Experiment) with above model, generate training and test data.
Replace some parts of physical model with ANN model

4. Training Neural Network
Self Organizing Local Linear (Unsupervised Learning)
RMS training error
RMS testing error

5. Results & Conclusion
With Core2 CPU of 2.4GHz, calculation time for acceleration cycle from zero to 80 km/h is
140s in case of fully physical model
21s in case of ANN supporting model  close to real time (real time is about 7.36s
Accuracy
ANN supporting vs. fully physical model
Results for acceleration cycle