Modeling of a Turbocharged DI Diesel Engine Using Artificial Neural Networks Engine Research Center, University of Wisconsin-Madison

Format:

Conference/Event

Author/Creator:

He, Yongsheng, author.

Conference Name:

SAE Powertrain & Fluid Systems Conference & Exhibition (2002-10-21 : San Diego, California, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2002

Summary:

Artificial neural networks (ANN) have been recognized as universal approximators for nonlinear continuous functions and actively applied in engine research in recent years [1, 2, 3, 4, 5, 6, 7 and 8]. This paper describes the methodology and results of using the ANN to model a turbocharged DI diesel engine. The engine was simulated using the CFD code (KIVA-ERC) over a wide range of operating conditions, and numerical simulation results were used to train the ANN. An efficient data collection methodology using the Design of Experiments (DOE) techniques was developed to select the most characteristic engine operating conditions and hence the most informative data to train the ANN. This approach minimizes the time and cost of collecting training data from either computational or experimental resources. The trained ANN was then used to predict engine parameters such as cylinder pressure, cylinder temperature, NOx and soot emissions, and cylinder heat transfer. The ANN model will replace the cylinder and heat release models of a system simulation with significantly improved predictive capability. The model will be important in future control development and diesel aftertreatment work

Notes:

Vendor supplied data

Publisher Number:

2002-01-2772

Access Restriction:

Restricted for use by site license