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Optimization of the engine cold start is critical for gasoline direct injection (GDI) engines to meet increasingly stringent emission regulations, since the emissions during the first 20 seconds of the cold start constitute more than 80% of the hydrocarbon (HC) emissions for the entire EPA FTP75 drive cycle. However, Direct Injection Spark Ignition (DISI) engine cold start optimization is very challenging due to the rapidly changing engine speed, cold thermal environment and low cranking fuel pressure. One approach to reduce HC emissions for DISI engines is to adopt retarded spark so that engines generate high heat fluxes for faster catalyst light-off during the cold idle. This approach typically degrades the engine combustion stability and presents additional challenges to the engine cold start. This paper describes a CFD modeling based approach to address these challenges for the Ford 3.5L V6 EcoBoost engine cold start.

This paper describes a CFD modeling based approach to address design challenges in GDI (gasoline direct injection) engine combustion system development. A Ford in-house developed CFD code MESIM (Multi-dimensional Engine Simulation) was applied to the study. Gasoline fuel is multi-component in nature and behaves very differently from the single component fuel representation under various operating conditions. A multi-component fuel model has been developed and is incorporated in MESIM code. To apply the model in engine simulations, a multi-component fuel recipe that represents the vaporization characteristics of gasoline is also developed using a numerical model that simulates the ASTM D86 fuel distillation experimental procedure. The effect of the multi-component model on the fuel air mixture preparations under different engine conditions is investigated. The modeling approach is applied to guide the GDI engine piston designs.

The present paper describes a CAE analysis approach to evaluate the transient cylinder head gasket sealing performance of a turbo charged GDI engine in the bench test development. In this approach, both transient gasket sealing force and gasket wear work are calculated to allow design engineers to find out the root cause of cylinder head gasket leakage failures. In this paper, the details of the method development are described. Firstly how to use and get the cylinder head gasket property are described, which is the basic theory and data for the gasket sealing analysis. A transient heat transfer calculation for accurately simulating the engine thermal shock test is established, which is mapped to the transient gasket sealing calculation as pivotal boundary.

In order to predict more accurately the cracking failure of cylinder head during the durability test of turbocharged engine in the development, a comprehensive evaluation method of cylinder head durability is established. In this method, both high cycle and low cycle fatigue performance are calculated to provide failure assessment. The method is then applied to investigate the root cause of cracking of cylinder head and assess design optimizations. Multidisciplinary approach is adopted to optimize high cycle fatigue and low cycle fatigue performance simultaneously to achieve the best comprehensive performance. In this paper, the details of the method development are described. First, the high cycle and low cycle fatigue properties of cylinder head material were measured at different temperature condition, and the fatigue life and high temperature creep properties of materials under thermo-mechanical fatigue cycle were also tested.

Recently, Ford Motor Company announced the introduction of EcoBoost engines in its Ford, Lincoln and Mercury vehicles as an affordable fuel-saving option to millions of its customers. The EcoBoost engine is planned to start production in June of 2009 in the Lincoln MKS. The EcoBoost engine integrates direct fuel injection with turbocharging to significantly improve fuel economy via engine downsizing. An application of this technology bundle into a 3.5L V6 engine delivers up to 12% better drive cycle fuel economy and 15% lower emissions with comparable torque and power as a 5.4L V8 PFI engine. Combustion system performance is key to the success of the EcoBoost engine. A systematic methodology has been employed to develop the EcoBoost engine combustion system.

SAIC Motor Corporation Limited (SAIC Motor) has developed a new 1.5 L 4-cylinder turbocharged gasoline direct injection engine to meet the market demand and increasingly stringent requirement of CAFE and tail-pipe emission regulations. A series of advanced technologies for improving engine fuel economy, engine-out emission, torque and power output specially low end torque performance have been employed, such as: central gasoline direct injection, integrated exhaust manifold, high tumble combustion system, Miller Cycle, cooled external EGR, 35MPa fuel injection system, multi-hole injector with variable hole size design, efficient turbo charging with electric wastegate (EWG), etc. As a result, the engine is able to achieve over 39% brake thermal efficiency (BTE), as well as substantial fuel consumption reduction in vehicle driving cycle. It delivers 275 Nm maximum torque and 127kW rated power, with fast low end torque response.

The accurate prediction of fuel sprays is critical to engine combustion and emissions simulations. A fine computational mesh is often required to better resolve fuel spray dynamics and vaporization. However, computations with a fine mesh require extensive computer time. This study developed a methodology that uses a locally refined mesh in the spray region. Such adaptive mesh refinement will enable greater resolution of the liquid-gas interaction while incurring only a small increase in the total number of computational cells. The present study uses an h-refinement adaptive method. A face-based approach is used for the inter-level boundary conditions. The prolongation and restriction procedure preserves conservation of properties in performing grid refinement/coarsening. The refinement criterion is based on the mass of spray liquid and fuel vapor in each cell. The efficiency and accuracy of the present adaptive mesh refinement scheme is demonstrated.

A general combustion model, in the context of large eddy simulations, was developed to simulate the full range of combustion in conventional diesel-type and HCCI-type diesels. The combustion model consisted of a Chemkin sub-model and an Extended Flamelet Time Scale (EFTS) sub-model. Specifically, Chemkin was used to simulate auto-ignition process. In the post-ignition phase, the combustion model was switched to EFTS. In the EFTS sub-model, combustion was assumed to be a combination of two elementary combustion modes: homogeneous combustion and flamelet combustion. The combustion index acted as a weighting factor blending the contributions from these two modes. The Chemkin sub-model neglected the subgrid scale turbulence-chemistry interactions whereas the EFTS model took them into account through a presumed PDF approach. The model was used to simulate an early injection mode of a Cummins DI diesel engine and a mode of a Caterpillar DI diesel engine.

Parallel computing schemes were developed to enhance the computational efficiency of engine spray simulations with adaptive mesh refinement (AMR). Spray simulations have been shown to be grid dependent and thus fine mesh is often used to improve solution accuracy. In this study, dynamic mesh refinement adaptive to spray region was developed and parallelized in KIVA-4. The change of cell and node numbers and the local characteristics in the dynamic mesh refinement posed difficulties in developing efficient parallel computing schemes to achieve low communication overhead and good load balance. The present strategy executed AMR on one processor with data scattering among processors following the adaptation, and performed AMR every ten computational timesteps for enhanced parallel performance. The re-initialization was required and performed at the minimized cost.

This paper proposes a film dynamics model for liquid film resulting from fuel spray impinging on a wall surface. It is based on a thin film assumption and uses numerical particles to represent the film to be compatible with the particle spray models developed previously. The Lagrangian method is adopted to govern the transport of the film particles. A new, statistical treatment was introduced of the momentum exchange between the impinging spray and the wall film to account for the directional distribution of the impinging momentum. This model together with the previously published models for outgoing droplets constitutes a complete description of the spray wall impingement dynamics. For model validation, films resulting from impinging sprays on a flat surface with different impingement angles were calculated and the results were compared with the corresponding experimental measurements.

This paper describes the CFD modeling work conducted for the development and research of a Vortex Induced Stratification Combustion (VISC) system that demonstrated superior fuel economy benefits. The Ford in-house CFD code and simulation methodology were employed. In the VISC concept a vortex forms on the outside of the wide cone angle spray and transports fuel vapor from the spray to the spark plug gap. A spray model for an outward-opening pintle injector used in the engine was developed, tested, and implemented in the code. Modeling proved to be effective for design optimization and analysis. The CFD simulations revealed important physical phenomena associated with the spray-guided combustion system mixing preparation.

The sources of unburned hydrocarbon (UHC) emissions in direct injection stratified charge engines are presented. Whereas crevices in the combustion chamber are the primary sources of UHC emissions in homogeneous charge engines, lean quenching and liquid film layers dominate UHC emissions in stratified charge operation. Emissions data from a single cylinder engine, operating in stratified charge mode at a low speed / light load condition is summarized. This operating point is interesting in that liquid film formation, as evidenced by smoke emissions, is minimal, thus highlighting the lean quenching process. The effects of operating parameters on UHC emissions are demonstrated via sweeps of spark advance, injection timing, manifold pressure, and swirl level. The effects of EGR dilution are also discussed. Spark advance is shown to be the most significant factor in UHC emissions. A semi-empirical model for UHC emissions is presented based on the analysis of existing engine data.

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 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.

A CFD based design optimization methodology was developed and adopted to the development of a stratified-charge direct-injection spark ignition (DISI) combustion system. Two key important issues for homogeneous charge operation, volumetric efficiency and mixing homogeneity, are addressed. The intake port is optimized for improved volumetric efficiency with a CFD based numerical optimization tool. It is found that insufficient fuel-air mixing is the root cause for the low rated power of most DISI engines. The fuel-air mixing in-homogeneity is due to the interaction between intake flow and injected fuel spray. An injector mask design was proposed to alleviate such interaction, then to improve air-fuel mixture homogeneity. It was then confirmed with dynamometer testing that the optimized design improved engine output and at the same time had lower soot and CO emissions.

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.

Engine downsizing has become a leading trend for fuel consumption reduction while maintaining the high specific power and torque output. Because of this, Turbo-charged Gasoline Direct Injection (TGDI) technology has been widely applied in passenger vehicles even though a number of technical challenges are presented during the engine development. This paper presents the investigation results of three key issues in the combustion development of a 2.0L TGDI engine at SAIC motor: fuel dilution, smoke emission and low speed stochastic pre-ignition(LSPI). The effect of the injection timing and injection strategy on fuel dilution and smoke emission, and LSPI are the focus of the experimental study.

RNG k-ε closure turbulence dissipation equations are evaluated employing the CFD code KIVA-3V Release 2. The numerical evaluations start by considering simple jet flows, including incompressible air jets and compressible helium jets. The results show that the RNG closure turbulence model predicts lower jet tip penetration than the "standard" k-ε model, as well as being lower than experimental data. The reason is found to be that the turbulence kinetic energy is dissipated too slowly in the downstream region near the jet nozzle exit. In this case, the over-predicted R term in RNG model becomes a sink of dissipation in the ε-equation. As a second step, the RNG turbulence closure dissipation models are further tested in complex engine flows to compare against the measured evolution of turbulence kinetic energy, and an estimate of its dissipation rate, during both the compression and expansion processes.