1. 5/10/00 Integration of Advanced Automotive Simulation Methods Using ANN 1 Integration of Advanced Automotive Simulation Methods Using Artificial Neural Network ECE/CS/ME 539 Final Project Yongsheng He Acknowledgement: Prof. Yu Hen Hu

2. 5/10/00 Integration of Advanced Automotive Simulation Methods Using ANN 2 Objective To develop a high fidelity automotive engine simulation tool for automotive engine design – Difficulty: build technology links between different advanced simulation methods Powertrain system simulations (MATLAB/SIMULINK) –Unsatisfactory prediction accuracy for fuel burning processes Computational fluid dynamics engine modeling (KIVA) –High fidelity, but high CPU cost

3. 5/10/00 Integration of Advanced Automotive Simulation Methods Using ANN 3 Powertrain System Simulation Dynamic modular powertrain model – Engine model (cylinders, intake/exhaust manifolds) Cylinder model (heat release, heat transfer, valve flow, etc) –Simple combustion correlations to predict fuel burning processes Unsatisfactory accuracyUnsatisfactory accuracy – Transmission model (torque converter, transmission) – Driveline model (axles, shafts, differentials) – Vehicle dynamics model Cylinder Model Engine Model Vehicle Dynamics Model Driveline Model CFD Engine Models (KIVA-3V ERC) Detailed CFD Results ANN Cylinder Model ANN Cylinder Model Transmission Model

4. 5/10/00 Integration of Advanced Automotive Simulation Methods Using ANN 4 CFD Simulation Computational Grid Main:20x30x21; Bowl:13x30x9; Computational fluid dynamics (CFD) engine modeling – Combustion, heat transfer, turbulence,atomization & drop drag, wall film/impingement, vaporization, etc – Its CPU cost is too large for direct incorporation into system simulation models Pros High fidelity Cons High CPU cost

5. 5/10/00 Integration of Advanced Automotive Simulation Methods Using ANN 5 Training and Testing ANN Multilayer Peceptron (MLP) – Training algorithm: Levenberg-Marquardt – Training and testing criteria: mean squared error (MSE) Error: difference between the non-dimensional cylinder pressure predicated by the MLP and that predicated by CFD modeling. – Hidden neurons Sigmoid transfer function Nguyen-Widrow initialization method

6. 5/10/00 Integration of Advanced Automotive Simulation Methods Using ANN 6 Input Feature Space Engine Operating Conditions (25 for training, 10 for testing) Speed (rpm)1600 Load (%)75 SOI (CA)-2 ATDC Fuel Flow Rate (kg/min)0.1297 Equivalence Ratio0.496 Intake Temp ( ° C)36 Intake Pressure (kPa)183 Exhaust Temp (° C)523 Exhaust Pressure (kPa)159 Injection Pressure (MPa)90 Injection SchemeSingle injection Injection Duration (CA)21.5 Nozzle Orifice Dia (mm)0.26 No. of Nozzle Holes6 Swirl Ratio (nominal)1.0 FuelAmoco Premier #2 EGR (%)0

7. 5/10/00 Integration of Advanced Automotive Simulation Methods Using ANN 7 Number of Hidden Neurons To achieve best testing results – 10 is optimal – Evenly distributed training dataset

8. 5/10/00 Integration of Advanced Automotive Simulation Methods Using ANN 8 Training Results MSE:

9. 5/10/00 Integration of Advanced Automotive Simulation Methods Using ANN 9 Testing Results MSE:

10. 5/10/00 Integration of Advanced Automotive Simulation Methods Using ANN 10 ANN Cylinder Model Trained MLP is then integrated into the original cylinder model to build ANN cylinder model – Predict cylinder pressure and indicated torque

11. 5/10/00 Integration of Advanced Automotive Simulation Methods Using ANN 11 Model Validation ANN cylinder model is validated using experimental results – Torque used to overcome friction should be taken into account – Satisfactory predication accuracy M f (g/cycle) SOI (degree, ATDC) Measured Brake Torque (Nm) Predicated Net Indicated Torque by ANN Cylinder Model (Nm) 0.1062 -6131.36145.50 -3127.52139.17 0122.92129.36 3119.35125.55 0.1621 -6226.17232.54 -3201.89226.71 0193.97216.79 3185.03202.05

12. 5/10/00 Integration of Advanced Automotive Simulation Methods Using ANN 12 Conclusions ANN is successfully employed to integrate advanced automotive simulation methods – Trained MLP has shown a very good predicative capability for cylinder pressure – Trained MLP is integrated into the original cylinder model to build ANN cylinder model – ANN cylinder model agrees pretty well with the experimental results