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In multidimensional modeling, fuels have been represented predominantly by single components, such as octane for gasoline. Several bicomponent studies have been performed, but these are still limited in their ability to represent real fuels, which are blends of as many as 300 components. This study outlines a method by which the fuel composition is represented by a distribution function of the fuel molecular weight. This allows a much wider range of compositions to be modeled, and only requires including two additional “species” besides the fuel, namely the mean and second moment of the distribution. This approach has been previously presented but is applied here to multidimensional calculations. Results are presented for single component droplet vaporization for comparison with single component fuel predictions, as well as results for a multicomponent gasoline and a diesel droplet.

A three-dimensional homogeneous equilibrium model (HEM) has been developed and implemented into an engine computational fluid dynamics (CFD) code KIVA-3V. The model was applied to simulate cavitating flow within injector nozzle passages. The effects of nozzle passage geometry and injection conditions on the development of cavitation zones and the nozzle discharge coefficient were investigated. Specifically, the effects of nozzle length (L/D ratio), nozzle inlet radius (R/D ratio) and K or KS factor (nozzle passage convergence) were simulated, and the effects of injection and chamber pressures, and time-varying injection pressure were also investigated. These effects are well captured by the nozzle flow model, and the predicted trends are consistent with those from experimental observations and theoretical analyses.

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.

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.

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.

DPF regenerations involve a trade-off between fuel economy and DPF durability. High temperature regenerations of DPFs have fewer fuel penalties but simultaneously tend to give higher substrate temperatures, which can reduce thermal reliability. In order to weaken the trade-off, the integrated system-level model [1,2,3,4] is used to conduct optimization studies and explore novel regeneration strategies for DPF aftertreatment devices. The integrated model developed in the Engine Research Center (ERC) includes sub-models for engines, emissions, aftertreatment devices and controllers. Based on the engine and regeneration fuel economy, multiple and single cycle regeneration tests are performed and analyzed. The optimal soot loadings to initiate and terminate regenerations are discussed. A pulsed regeneration strategy, which is characterized by injecting multiple pulses of fuel (upstream of a DOC) during regenerations, is investigated.

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.

To help account for fuel distribution during combustion in diesel engines, a fuel film model has been developed and implemented into the KIVA-II code [1]. Spray-wall interaction and spray-film interaction are also incorporated into the model. Modified wall functions for evaporating, wavy films are developed and tested. The model simulates thin fuel film flow on solid surfaces of arbitrary configuration. This is achieved by solving the continuity, momentum and energy 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, dynamic pressure effects, and convective heat and mass 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.

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.

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.

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.

Multidimensional computations were carried out to simulate the in-cylinder fuel/air mixing process of a direct-injection spark-ignition engine using a modified version of the KIVA-3 code. A hollow cone spray was modeled using a Lagrangian stochastic approach with an empirical initial atomization treatment which is based on experimental data. Improved Spalding-type evaporation and drag models were used to calculate drop vaporization and drop dynamic drag. Spray/wall impingement hydrodynamics was accounted for by using a phenomenological model. Intake flows were computed using a simple approach in which a prescribed velocity profile is specified at the two intake valve openings. This allowed three intake flow patterns, namely, swirl, tumble and non-tumble, to be considered. It was shown that fuel vaporization was completed at the end of compression stroke with early injection timing under the chosen engine operating conditions.

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.

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.

Early direct injection with HCCI like properties is characterized by the presence of an ignition dwell - the interval between end of fuel injection and start of combustion, during which fuel-air mixing occurs. Previous work by Jhavar and Rutland (2005) has focused on investigating different methods to affect fuel-air mixing during the ignition dwell. That study helped to evaluate the relative influence of various mixing control strategies to achieve ignition control. In this study, we attempt to look into the mixture preparation process in more detail. Therefore, turbulence is studied using Large Eddy Simulation (LES) models in place of Reynolds Averaged Navier Stokes (RANS) models. While LES is computationally more expensive than RANS, it depicts the flow structure more accurately. Therefore, it can be applied to engines in order to gain a better representation of local mixing as well as accurately simulate unsteady flow behavior in engines.

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.