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A cylinder model was developed using artificial neural networks (ANN). The cylinder model utilized the trained ANN models to predict engine parameters including cylinder pressures, cylinder temperatures, cylinder wall heat transfer, NOx and soot emissions. The ANN models were trained to approximate CFD simulation results of an engine. The ANN cylinder model was then applied to predict engine performance and emissions over the standard heavy-duty FTP transient cycle. The engine responses varying over the engine speed and torque range were simulated in the course of the transient test cycle. It was demonstrated that the ANN cylinder model is capable of simulating the characteristics of the engine operating under transient conditions reasonably well.

The KIVA 3V code has been applied to predict combustion chamber heat flux in an air-cooled utility engine. The KIVA heat flux predictions were compared with experimentally measured data in the same engine over a wide range of operating conditions. The measured data were found to be approximately two times larger than the predicted results, which is attributed to the omission of chemical heat release in the near-wall region for the heat transfer model applied. Modifying the model with a simple scaling factor provided a good comparison with the measured data for the full range of engine load, heat flux sensor location, air-fuel ratio and spark timings tested. The detailed spatially resolved results of the KIVA predictions were then used to develop a simplified model of the combustion chamber temporally integrated heat flux using an artificial neural network (ANN).

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.

An overall diesel engine and aftertreatment system model has been created that integrates diesel engine, exhaust system, engine emissions, and diesel particulate filter (DPF) models using MATLAB Simulink. The 1-D engine and exhaust system models were developed using WAVE. The engine emissions model combines a phenomenological soot model with artificial neural networks to predict engine out soot emissions. Experimental data from a light-duty diesel engine was used to calibrate both the engine and engine emissions models. The DPF model predicts the behavior of a clean and particulate-loaded catalyzed wall-flow filter. Experimental data was used to validate this sub-model individually. Several model integration issues were identified and addressed. These included time-step selection, continuous vs. limited triggering of sub-models, and code structuring for simulation speed. Required time-steps for different sub models varied by orders of magnitude.