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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 integrated system model containing sub-models for a diesel engine, NOx and soot emissions, and a diesel particulate filter (DPF) has been used to simulate stead-state engine operating conditions. The simulation results have been used to investigate the effect of DPF loading and passive regeneration on engine performance and emissions. This work is the continuation of previous work done to create an overall diesel engine/exhaust system integrated model. As in the previous work, a diesel engine, exhaust system, engine soot emissions, and diesel particulate filter (DPF) sub-models have been integrated into an overall model using Matlab Simulink. For the current work new sub-models have been added for engine-out NOx emissions and an engine feedback controller. The integrated model is intended for use in simulating the interaction of the engine and exhaust aftertreatment components.

An approach to accurately capture overall behavior in a system level model of DI Diesel HCCI engine operation is presented. The modeling methodology is an improvement over the previous effort [36], where a multi-zone model with detailed chemical kinetics was coupled with an engine cycle simulation code. This multi-zone technique was found to be inadequate in capturing the fuel spray dynamics and its impact on mixing. An improved methodology is presented in this paper that can be used to model fully and partially premixed charge compression ignition engines. A Computational Fluid Dynamics (CFD) driven model is used where the effects of fuel injection, spray evolution, evaporation, and turbulent mixing are considered. The modeling approach is based on the premise that once the initial spray dynamics are correctly captured, the overall engine predictions during the combustion process can be captured with good accuracy.

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.

An integrated system based modeling approach has been developed to understand early Direct Injection (DI) Diesel Homogeneous Charge Compression Ignition (HCCI) process. GT-Power, a commercial one-dimensional (1-D) engine cycle code has been coupled with an external cylinder model which incorporates sub-models for fuel injection, vaporization, detailed chemistry calculations (Chemkin), heat transfer, energy conservation and species conservation. In order to improve the modeling accuracy, a multi-zone model has been implemented to account for temperature and fuel stratifications in the cylinder charge. The predictions from the coupled simulation have been compared with experimental data from a single cylinder Caterpillar truck engine modified for Diesel HCCI operation. A parametric study is conducted to examine the effect of combustion timing on four major control parameters. Overall the results show good agreement of the trends between the experiments and model predictions.

Numerical simulations are performed to investigate the fuel/air mixing preparation in a gasoline direct injection (GDI) engine. A two-valve OHV engine with wedge combustion chamber is investigated since automobiles equipped with this type of engine are readily available in the U.S. market. Modifying and retrofitting these engines for GDI operation could become a viable scenario for some engine manufactures. A pressure-swirl injector and wide spacing injection layout are adapted to enhance mixture preparation. The primary interest is on preparing the mixture with adequate equivalence ratio at the spark plug under a wide range of engine operating conditions. Two different engine operating conditions are investigated with respect to engine speed and load. A modified version of the KIVA-3V multi-dimensional CFD code is used. The modified code includes the Linearized Instability Sheet Atomization (LISA) model to simulate the development of the hollow cone spray.