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Neural networks are useful tools for optimization studies since they are very fast, so that while capturing the accuracy of multi-dimensional CFD calculations or experimental data, they can be run numerous times as required by many optimization techniques. This paper describes how a set of neural networks trained on a multi-dimensional CFD code to predict pressure, temperature, heat flux, torque and emissions, have been used by a genetic algorithm in combination with a hill-climbing type algorithm to optimize operating parameters of a diesel engine over the entire speed-torque map of the engine. The optimized parameters are mass of fuel injected per cycle, shape of the injection profile for dual split injection, start of injection, EGR level and boost pressure. These have been optimized for minimum emissions. Another set of neural networks have been trained to predict the optimized parameters, based on the speed-torque point of the engine.

Neural networks have been used for engine computations in the recent past. One reason for using neural networks is to capture the accuracy of multi-dimensional CFD calculations or experimental data while saving computational time, so that system simulations can be performed within a reasonable time frame. This paper describes three methods to improve upon neural network predictions. Improvement is demonstrated for in-cylinder pressure predictions in particular. The first method incorporates a physical combustion model within the transfer function of the neural network, so that the network predictions incorporate physical relationships as well as mathematical models to fit the data. The second method shows how partitioning the data into different regimes based on different physical processes, and training different networks for different regimes, improves the accuracy of predictions.

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.

A modeling study has been conducted in order to characterize the heat transfer in an automotive diesel exhaust system. The exhaust system model, focusing on 2 exhaust pipes, has been created using a transient 1-D engine flow network simulation program. Model results are in excellent agreement with experimental data gathered before commencement of the modeling study. Predicted pipe exit stream temperatures are generally within one percent of experimental values. Sensitivity analysis of the model was the major focus of this study. Four separate variables were chosen for the sensitivity analysis. These being the external convective heat transfer coefficient, external emissivity, mass flow rate of exhaust gases, and amplitude of incoming pressure fluctuations. These variables were independently studied to determine their contribution to changes in exhaust gas stream temperature and system heat flux. There are two primary benefits obtained from conducting this analysis.

The present study investigated HCCI combustion in a heavy-duty diesel engine both experimentally and numerically. The engine was equipped with a hollow-cone pressure-swirl injector using gasoline direct injection. Characteristics of HCCI combustion were obtained by very early injection with a heated intake charge. Experimental results showed an increase in NOx emission and a decrease in UHC as the injection timing was retarded. It was also found that optimization can be achieved by controlling the intake temperature together with the start-of-injection timing. The experiments were modeled by using an engine CFD code with detailed chemistry. The CHEMKIN code was implemented into KIVA-3V such that the chemistry and flow solutions were coupled. The model predicted ignition timing, cylinder pressure, and heat release rates reasonably well. The NOx emissions were found to increase as the injection timing was retarded, in agreement with experimental results.

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 three-dimensional CFD codes KIVA-II and KIVA-3 have been used together to study the effects of intake generated in-cylinder flow structure on fuel-air mixing and combustion in a direct injected (DI) Diesel engine. In order to more accurately account for the effect of intake flow on in-cylinder processes, the KIVA-II code has been modified to allow for the use of data from other CFD codes as initial conditions. Simulation of the intake and compression strokes in a heavy-duty four-stroke DI Diesel engine has been carried out using KIVA-3. Flow quantities and thermodynamic field information were then mapped into a computational grid in KIVA-II for use in the study of mixing and combustion. A laminar and turbulent timescale combustion model, as well as advanced spray models, including wave breakup atomization, dynamic drop drag, and spray-wall interaction has been used in KIVA-II.

Previous studies have shown the importance of the in-cylinder flow field which exists prior to fuel injection on performance and emissions behavior of direct injected (DI) diesel engines. Key parameters in the flow field are the turbulence level and the resolved structures, such as swirl and tumble flow. These characteristics are known to have significant effects on the fuel vaporization, droplet break-up, and fuel-air mixing. The relative importance of these effects is investigated through simulation of injection into a stirred, heated, constant volume combustion bomb, using the computational fluid dynamics codes KIVA-3 [9] and KIVA-II [10]. Initial conditions for these simulations are based on in-cylinder conditions which exist in a heavy duty DI diesel engine immediately prior to fuel injection.

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 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.

Ignition dwell is defined as the interval between end of fuel injection and start of combustion in early injection diesel combustion that exhibits HCCI-like characteristics. In this project, the impact of in-cylinder temperature and fuel-air mixing on the ignition dwell was investigated. The engine cycle was simulated using the 3-D CFD code KIVA-3V. Work done by Klingbeil (2002) has shown that ignition dwell allows more time for fuel and air to mix and drastically reduces emissions of NOX and particulate matter. Temperature is known to have a direct impact on the duration of ignition dwell. However, initial fuel-air distribution and mixing (i.e. at the end of fuel injection) may also impact the duration of ignition dwell. To investigate this, variations in EGR, fuel injection timing, engine valve actuation and swirl were simulated. The aim was to use these techniques to generate varying levels of fuel-air mixing and to check if ignition dwell was affected.

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.