Development of a neural network model of an advanced, turbocharged diesel engine for use in vehicle-level optimization studies

Abstract

The following work documents the development of an artificial neural network (ANN) model of an advanced, turbocharged diesel engine, and its implementation within an overall vehicle simulation. The purpose of this research was to create a model that would help drastically reduce in-vehicle engine design cycle time, improve numerical robustness and maintain the predictive capability of its physically based counterpart. Ease of integration within an optimization framework for preliminary investigations of engine designs was also a significant need. A design of experiments (DOE) was first used to sample the engine' feasible design domain. The high-fidelity model calculated engine responses at these DOE designs; the DOE inputs and engine model responses were subsequently used to train a radial basis function ANN. A typical, vehicle-level optimization study is presented which compares results and time requirements of the ANN and high-fidelity models. Critical issues in the development of an ANN engine model are detailed and generic guidelines are presented to expedite the implementation of this surrogate modelling technique to other complex subsystems and components.