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It has previously been shown experimentally and computationally that the process of Homogeneous Charge Compression Ignition (HCCI) is very dependent on the pre-combustion gas temperature field. This study looks in detail at how temperature fields can evolve by comparing results of two combustion chamber designs, a piston with a square bowl and a disk shaped piston, and relates these temperature fields to measured HCCI combustion durations. The contributions of combustion chamber surface area and turbulence levels to the gas temperature evolution are considered over the crank angle range from intake valve closure to top-dead-center. This is a CFD study, whose results were transformed into traditional analysis methods of convective heat transfer (q=h*A*ΔT) and boundary layers.

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.

The flow through diesel fuel injector nozzles is important because of the effects on the spray and the atomization process. Modeling this nozzle flow is complicated by the presence of cavitation inside the nozzles. This investigation uses a two-dimensional, two-phase, transient model of cavitating nozzle flow to observe the individual effects of several nozzle parameters. The injection pressure is varied, as well as several geometric parameters. Results are presented for a range of rounded inlets, from r/D of 1/40 to 1/4. Similarly, results for a range of L/D from 2 to 8 are presented. Finally, the angle of the corner is varied from 50° to 150°. An axisymmetric injector tip is also simulated in order to observe the effects of upstream geometry on the nozzle flow. The injector tip calculations show that the upstream geometry has a small influence on the nozzle flow. The results demonstrate the model's ability to predict cavitating nozzle flow in several different geometries.

A turbulent combustion model based on the coherent flamelet model was developed in this study and applied to diesel engines. The combustion was modeled in three distinct but overlapping phases: low temperature ignition kinetics using the Shell ignition model, high temperature premixed burn using a single step Arrhenius equation, and the flamelet based diffusion burn. Two criteria for transitions based on temperature, heat release rate, and the local Damköhler number were developed for the progression of combustion between each of these phases. The model was implemented into the computational computer code KIVA-II. Previous experiments on a Caterpillar model E 300, # 1Y0540 engine, a Tacom LABECO research engine, and a single cylinder version of a Cummins N14 production engine were used to validate the cylinder averaged predictions of the model.

This paper illustrates the applicability of a sequential fluid mechanics, multi-zone chemical kinetics model to analyze HCCI experimental data for two combustion chamber geometries with different levels of turbulence: a low turbulence disc geometry (flat top piston), and a high turbulence square geometry (piston with a square bowl). The model uses a fluid mechanics code to determine temperature histories in the engine as a function of crank angle. These temperature histories are then fed into a chemical kinetic solver, which determines combustion characteristics for a relatively small number of zones (40). The model makes the assumption that there is no direct linking between turbulence and combustion. The multi-zone model yields good results for both the disc and the square geometries. The model makes good predictions of pressure traces and heat release rates.

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.

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.

Two different types of mesh used for diesel combustion with the KIVA-4 code are compared. One is a well established conventional KIVA-3 type polar mesh. The other is a non-polar mesh with uniform size throughout the piston bowl so as to reduce the number of cells and to improve the quality of the cell shapes around the cylinder axis which can contain many fuel droplets that affect prediction accuracy and the computational time. This mesh is specialized for the KIVA-4 code which employs an unstructured mesh. To prevent dramatic changes in spray penetration caused by the difference in cell size between the two types of mesh, a recently developed spray model which reduces mesh dependency of the droplet behavior has been implemented. For the ignition and combustion models, the Shell model and characteristic time combustion (CTC) model are employed.

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.

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.

Large Eddy Simulation (LES) with a flamelet time scale combustion model is used to simulate diesel combustion. The flamelet time scale model uses a steady-state flamelet library for n-heptane indexed by mean mixture fraction, mixture fraction variance, and mean scalar dissipation rate. In the combustion model, reactions proceed towards the flamelet library solution at a time scale associated with the slowest reaction. This combination of a flamelet solution and a chemical time scale helps to account for unsteady mixing effects. The turbulent sub-grid stresses are simulated using a one-equation, non-viscosity LES model called the dynamic structure model. The model uses a tensor coefficient determined by the dynamic procedure and the subgrid kinetic energy. The model has been expanded to include scalar mixing and scalar dissipation. A new model for the conditional scalar dissipation has been developed to better predict local extinction.

Recently experiments were conducted on an automotive homogeneous-charge-compression-ignition (HCCI) research engine with a negative-valve-overlap (NVO) cam. In the study two sets of experiments were run. One set injected a small quantity of fuel (HPLC-grade iso-octane) during NVO in varying amounts and timings followed by a larger injection during the intake stroke. The other set of experiments was similar, but did not include an NVO injection. By comparing both sets of results researchers were able to investigate the use of NVO fuel injection to control main combustion phasing under light-load conditions. For this paper a subset of these experiments are modeled with the computational-fluid-dynamics (CFD) code KIVA3V [ 6 ] using a multi-zone combustion model. The computational domain includes the combustion chamber, and intake and exhaust valves, ports, and runners. Multiple cycles are run to minimize the influence of initial conditions on final simulated results.

To facilitate the growing interest in hydrogen combustion for internal combustion engines, computer models are being developed to simulate gaseous fuel injection, air entrainment and the ensuing combustion. This paper introduces a new method for modeling the injection and air entrainment processes for gaseous fuels. Modeling combustion is not covered in this paper. The injection model uses a gaseous sphere injection methodology, similar to liquid droplet injection techniques used for liquid fuel injection. In this paper, the model concept is introduced and model results are compared with correctly- and under-expanded experimental data.

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.

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.

Analysis of the cold-starting performance of diesel engines requires the development of advanced models to describe the multicomponent nature of the fuel as well as the spray impingement and wall film behavior. A new approach to modeling the multicomponent nature of commercial fuels was implemented. This model is based on a continuous distribution using a probability density function, rather than the use of discrete components, to capture more accurately the entire range of composition in commercial fuels. The model was applied to single droplet calculations to validate the predictions against experimental results. Previous discrete component wall-film modeling has been extended to include the continuous multicomponent fuel representation. A significant factor that has received little attention in analyzing the cold-start performance of diesel engines is the spray impingement angle and location. This has been investigated using the modified KIVA code.

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.

A fuel film model has been developed and implemented into the KIVA-II code to help account for fuel distribution during combustion in diesel engines. Spray-wall interaction and spray-film interaction are also incorporated into the model. The model simulates thin fuel film flow on solid surfaces of arbitrary configuration. This is achieved by solving the continuity and momentum equations for the two dimensional film that flows over a three dimensional surface. The major physical effects considered in the model include mass and momentum contributions to the film due to spray drop impingement, splashing effects, various shear forces, piston acceleration, and dynamic pressure effects. In order to adequately represent the drop interaction process, impingement regimes and post-impingement behavior have been modeled using experimental data and mass, momentum and energy conservation constraints. The regimes modeled for spray-film interaction are stick, rebound, spread, and splash.

Multi-zone CFD simulations with detailed kinetics were used to model iso-octane HCCI experiments performed on a single-cylinder research engine. The modeling goals were to validate the method (multi-zone combustion modeling) and the reaction mechanism (LLNL 857 species iso-octane) by comparing model results to detailed exhaust speciation data, which was obtained with gas chromatography. The model is compared to experiments run at 1200 RPM and 1.35 bar boost pressure over an equivalence ratio range from 0.08 to 0.28. Fuel was introduced far upstream to ensure fuel and air homogeneity prior to entering the 13.8:1 compression ratio, shallow-bowl combustion chamber of this 4-stroke engine. The CFD grid incorporated a very detailed representation of the crevices, including the top-land ring crevice and head-gasket crevice. The ring crevice is resolved all the way into the ring pocket volume. The detailed grid was required to capture regions where emission species are formed and retained.

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.