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Multi-dimensional computational efforts using comprehensive and skeletal kinetics have been made to investigate the cool flame region in HCCI combustion. The work was done in parallel to an experimental study that showed the impact of the negative temperature coefficient and the cool flame on the start of combustion using different fuels, which is now the focus of the simulation work. Experiments in a single cylinder CFR research engine with n-butane and a primary reference fuel with an octane number of 70 (PRF 70) were modeled. A comparison of the pressure and heat release traces of the experimental and computational results shows the difficulties in predicting the heat release in the cool flame region. The behavior of the driving radicals for two-stage ignition is studied and is compared to the behavior for a single-ignition from the literature. Model results show that PRF 70 exhibits more pronounced cool flame heat release than n-butane.

A particle swarm optimization (PSO) algorithm was implemented with engine testing in order to accelerate the engine development process. The PSO algorithm is a stochastic, population-based evolutionary optimization algorithm. In this study, PSO was used to reduce exhaust emissions while maintaining high fuel efficiency. A merit function was defined to help reduce multiple emissions simultaneously. Engine operations using both single-injection and double-injection strategies were optimized. The present PSO algorithm was found to be very effective in finding the favorable operating conditions for low emissions. The optimization usually took 40-70 experimental runs to find the most favorable operating conditions under the constraints specified in the present testing. High EGR levels, small pilot amount, and late main injection were suggested by the PSO. Multiple emissions were reduced simultaneously without a compromise in the brake specific fuel consumption.

The objective of this work was to identify methods of reliably predicting optimum operating conditions in an experimental compression ignition engine using multiple injections. Abstract modeling offered an efficient way to predict large volumes data, when compared with simulation, although the initial cost of constructing such models can be large. This work aims to reduce that initial cost by adding knowledge about the favorable network structures and training rules which are discovered. The data were gathered from a high pressure common rail direct injection turbocharged compression ignition engine utilizing a high EGR configuration. The range of design parameters were relatively large; 100 MPa - 240 MPa for fuel pressure, up to 62% EGR using a modified, long-route, low pressure EGR system, while the pilot timing, main timing, and pilot ratio were free within the safe operating window for the engine.

Accelerated computational optimization of a diesel engine calibration was achieved by combining Support Vector Regression models with the Particle Swarm Optimization routine. The framework utilized a full engine simulation as a surrogate for a real engine test with test parameters closely resembling a typical 4.5L diesel engine. Initial tests were run with multi-modal test problems including Rastragin's, Bukin's, Ackely's, and Schubert's functions which informed the ML model tuning hyper-parameters. To improve the performance of the engine the hybrid approach was used to optimize the Fuel Pressure, Injection Timing, Pilot Timing and Fraction, and EGR rate. Nitrogen Oxides, Particulate Matter, and Specific Fuel Consumption are simultaneously reduced. As expected, optimums reflect a late injection strategy with moderately high EGR rates.

One of the most critical elements in diesel engine design is the selection and matching of the fuel injection system. The injection largely controls the combustion process, and with it also a wide range of related issues, such as: fuel efficiency, emissions, startability, load acceptance (acceleration) and combustion noise. Simulation has been a valuable tool for the engine design engineer to predict and optimize key parameters of the fuel injection system. This is a problem that spans a number of subsystems. Historically, simulations of these subsystems (hydraulics, gas dynamics, engine performance and 3-D CFD cylinder modeling) have typically been done in isolation. Recently, a simulation tool has been developed, which models the different subsystems in an integrated manner. This simulation tool combines a 1-D simulation tool for modeling of hydraulic and gas dynamics systems, with 3-D CFD code for modeling the in-cylinder combustion and emissions.

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.

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 computer model that is able to predict the occurrence of knock in spark ignition engines has been developed and implemented into the KIVA-3V code. Three major sub-models were used to simulate the overall process, namely the spark ignition model, combustion model, and end-gas auto-ignition models. The spark ignition and early flame development is modeled by a particle marker technique to locate the flame kernel. The characteristic-time combustion model is applied to simulate the propagation of the regular flame. The autoignition chemistry in the end-gas was modeled by a reduced chemical kinetics mechanism that is based on the Shell model. The present model was validated by simulating the experimental data in three different engines. The spark ignition and the combustion models were first validated by simulating a premixed Caterpillar engine that was converted to run on propane. Computed cylinder pressure agrees well with the experimental data.

A level set G-equation model has been developed to model the combustion process in spark ignition engines. The spark ignition process was modeled using an improved version of the Discrete Particle Ignition Kernel (DPIK) model. The two models were implemented into the KIVA-3V code to simulate SI engine combustion under both premixed and direct injection conditions. In the ignition model, the ignition kernel growth is tracked by Lagrangian markers, and spark discharge energy and flow turbulence effects on the kernel growth are considered. Once the ignition kernel grows to a size where the turbulent flame is fully developed, the G-equation model is used to track the mean turbulent flame evolution. When combined with a characteristic time scale combustion model, the models were also used to simulate stratified combustion in DISI engines, where the triple flame structure must be considered.

The combustion and emissions of a diesel/natural gas dual-fuel engine are studied. Available engine experimental data demonstrates that the dual-fuel configuration provides a potential alternative to diesel engine operation for reducing emissions. The experiments are compared to multi-dimensional model results. The computer code used is based on the KIVA-3V code and consists of updated sub-models to simulate more accurately the fuel spray atomization, auto-ignition, combustion and emissions processes. The model results show that dual-fuel engine combustion and emissions are well predicted by the present multi-dimensional model. Significant reduction in NOx emissions is observed in both the experiments and simulations when natural gas is substituted for diesel fuel. The HC emissions are under predicted by numerical model as the natural gas substitution is increased.

Detailed chemical kinetics was implemented in the KIVA-3V multidimensional CFD code to study the combustion process in Homogeneous Charge Compression Ignition (HCCI) engines. The CHEMKIN code was implemented such that the chemistry and flow solutions were coupled. Detailed reaction mechanisms were used to simulate the fuel chemistry of ignition and combustion. Effects of turbulent mixing on the reaction rates were also considered. The model was validated using the experimental data from two modified heavy-duty diesel engines, including a Volvo engine and a Caterpillar engine operated at the HCCI mode. The results show that good levels of agreement were obtained using the present KIVA/CHEMKIN model for a wide range of engine conditions, including various fuels, injection systems, engine speeds, and EGR levels. Ignition timings were predicted well without the need to adjust any kinetic constants.

A dual fuel engine simulation model was formulated and the combustion process of a diesel/natural gas dual fuel engine was studied using an updated KIVA-3V Computational Fluid Dynamic (CFD) code. The dual fuel engine ignition and combustion process is complicated since it includes diesel injection, atomization and ignition, superimposed with premixed natural gas combustion. However, understanding of the combustion process is critical for engine performance optimization. Starting from a previously validated Characteristic-Timescale diesel combustion model, a natural gas combustion model was implemented and added to simulate the ignition and combustion process in a dual fuel bus engine. Available engine test data were used for validation of both the diesel-only and the premixed spark-ignition operation regimes. A new formulation of the Characteristic-Timescale combustion model was then introduced to allow smooth transition between the combustion regimes.

Understanding the fundamental details of drop/wall interactions is important to improving engine performance. Most of the drop-wall interactions studies are based on the impact of a single drop on the wall. To accurately mimic and model the real engine conditions, it is necessary to characterize spray/wall interactions with different impingement frequencies at a wide range of wall temperatures. In this study, a numerical method, based on Smoothed Particle Hydrodynamics (SPH), is used to simulate consecutive droplet impacts on a heated wall both below and above the Leidenfrost temperature. Impact regimes are identified for various impact conditions by analyzing the time evolution of the post-impingement process of n-heptane drops at different impingement frequencies and wall surface temperatures. For wall temperature below the Leidenfrost temperature, the recoiled film does not leave the surface.

The present study uses a numerical model to investigate the effects of flow turbulence on premixed iso-octane HCCI engine combustion. Different levels of in-cylinder turbulence are generated by using different piston geometries, namely a disc-shape versus a square-shape bowl. The numerical model is based on the KIVA code which is modified to use CHEMKIN as the chemistry solver. A detailed reaction mechanism is used to simulate the fuel chemistry. It is found that turbulence has significant effects on HCCI combustion. In the current engine setup, the main effect of turbulence is to affect the wall heat transfer, and hence to change the mixture temperature which, in turn, influences the ignition timing and combustion duration. The model also predicts that the combustion duration in the square bowl case is longer than that in the disc piston case which agrees with the measurements.

A phenomenological nozzle flow model has been developed and implemented in both the FIRE and KIVA-II codes to simulate the effects of the nozzle geometry on fuel injection and spray processes. The model takes account of the nozzle passage inlet configuration, flow losses and cavitation, the injection pressure and combustion chamber conditions and provides initial conditions for multidimensional spray modeling. The discharge coefficient of the injector, the effective injection velocity and the initial drop or injected liquid ‘blob’ sizes are calculated dynamically during the entire injection event. The model was coupled with the wave breakup model to simulate experiments of non-vaporizing sprays under diesel conditions. Good agreement was obtained in liquid penetration, spray angle and drop size (Sauter Mean Diameter). The integrated model was also used to model combustion in a Cummins single-cylinder optical engine with good agreement.

Intake flow for a four-stroke experimental gasoline engine is modeled considering moving valves and realistic port geometries. The numerical model is based on the KIVA-3 code and computed flow velocities are compared with LDV measurements. Computations start prior to intake valve opening and the pressure boundaries are specified at both the intake and exhaust pipe cross sections. Numerical results show that the in-cylinder flow pattern is well simulated in the symmetric plane passing through the cylinder axis. The computed and measured cylinder pressure and flow velocities agree reasonably well during the intake process. At top-dead-center, computations show a rotating flow pattern exists in the squish region corresponding to an area with relatively high turbulent kinetic energy. Results of intake flow modeling also show the evolution of in-cylinder averaged turbulent kinetic energy is different if the intake charging process is not modeled.

The impact of fuel droplets on heated surfaces is of great importance in internal combustion engines. In engine computational fluid dynamics (CFD) simulations, the drop-wall interaction is usually considered by using models derived from experimental data and correlations rather than direct simulations. This paper presented a numerical method based on smoothed particle hydrodynamics (SPH), which can directly simulate the impact process of fuel droplets impinging on solid surfaces. The SPH method is a Lagrangian meshfree particle method. It discretizes fluid into a number of SPH particles and governing equations of fluid into a set of particle equations. By solving the particle equations, the movement of particles can be obtained, which represents the fluid flows. The SPH method is able to simulate the large deformation and breakup of liquid drops without using additional interface tracking techniques.

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.

An integrated numerical model has been developed for diesel engine computations based on the KIVA-II code. The model incorporates a modified RNG k-ε, turbulence model, a ‘wave’ breakup spray model, the Shell ignition model, the laminar-and-turbulent characteristic-time combustion model, a crevice flow model, a spray/wall impingement model that includes rebounding and breaking-up drops, and other improved submodels in the KIVA code. The model was validated and applied to model successfully different types of diesel engines under various operating conditions. These engines include a Caterpillar engine with different injection pressures at different injection timings, a small Tacom engine at different loads, and a Cummins engine modified by Sandia for optical experiments. Good levels of agreement in cylinder pressures and heat release rate data were obtained using the same computer model for all engine cases.