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This paper implements a coupled approach to integrate the internal nozzle flow and the ensuing fuel spray using a Volume-of-Fluid (VOF) method in the CONVERGE CFD software. A VOF method was used to model the internal nozzle two-phase flow with a cavitation description closed by the homogeneous relaxation model of Bilicki and Kestin [1]. An Eulerian single velocity field approach by Vallet et al. [2] was implemented for near-nozzle spray modeling. This Eulerian approach considers the liquid and gas phases as a complex mixture with a highly variable density to describe near nozzle dense sprays. The mean density is obtained from the Favreaveraged liquid mass fraction. The liquid mass fraction is transported with a model for the turbulent liquid diffusion flux into the gas.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

In order to extend the operability limit of the gasoline compression ignition (GCI) engine, as an avenue for low temperature combustion (LTC) regime, the effects of parametric variations of engine operating conditions on the performance of six-stroke GCI (6S-GCI) engine cycle are numerically investigated, using an in-house 3D CFD code coupled with high-fidelity physical sub-models along with the Chemkin library. The combustion and emissions were calculated using a skeletal chemical kinetics mechanism for a 14-component gasoline surrogate fuel. Authors’ previous study highlighted the effects of the variation of injection timing and split ratio on the overall performance of 6S-GCI engine and the unique mixing-controlled burning mode of the charge mixtures during the two additional strokes. As a continuing effort, the present study details the parametric studies of initial gas temperature, boost pressure, fuel injection pressure, compression ratio, and EGR ratio.

Past experimental studies conducted by the current authors on a 13 liter 16.7:1 compression ratio heavy-duty diesel engine have shown that diesel-Compressed Natural Gas (CNG) Reactivity Controlled Compression Ignition (RCCI) combustion targeting low NOx emissions becomes progressively difficult to control as the engine load is increased. This is mainly due to difficulty in controlling reactivity levels at higher loads. For the current study, CFD investigations were conducted in CONVERGE using the SAGE combustion solver with the application of the Rahimi mechanism. Studies were conducted at a load of 5 bar BMEP to validate the simulation results against RCCI experimental data. In the low load study, it was found that the Rahimi mechanism was not able to predict the RCCI combustion behavior for diesel injection timings advanced beyond 30 degCA bTDC. This poor prediction was found at multiple engine speed and load points.

Gasoline compression ignition (GCI) technology shows the potential to obtain high thermal efficiencies while maintaining low soot and NOx emissions in light-duty engine applications. Recent experimental studies and numerical simulations have indicated that high reactivity gasoline-like fuels can further enable the benefits of GCI combustion. However, there is limited empirical data in the literature studying the gasoline compression ignition process at relevant in-cylinder conditions, which are required for further optimizing combustion system designs. This study investigates the temporal and spatial evolution of the compression ignition process of various high reactivity gasoline fuels with research octane numbers (RON) of 71, 74 and 82, as well as a conventional RON 97 E10 gasoline fuel. A ten-hole prototype gasoline injector specifically designed for GCI applications capable of injection pressures up to 450 bar was used.

In this paper, a model-based linear estimator and a non-linear control law for an Fe-zeolite urea-selective catalytic reduction (SCR) catalyst for heavy duty diesel engine applications is presented. The novel aspect of this work is that the relevant species, NO, NO2 and NH3 are estimated and controlled independently. The ability to target NH3 slip is important not only to minimize urea consumption, but also to reduce this unregulated emission. Being able to discriminate between NO and NO2 is important for two reasons. First, recent Fe-zeolite catalyst studies suggest that NOx reduction is highly favored by the NO 2 based reactions. Second, NO2 is more toxic than NO to both the environment and human health. The estimator and control law are based on a 4-state model of the urea-SCR plant. A linearized version of the model is used for state estimation while the full nonlinear model is used for control design.

Combustion systems with advanced injection strategies have been extensively studied, but there still exists a significant fundamental knowledge gap on fuel spray interactions with the piston surface and chamber walls. This paper is meant to provide detailed data on spray-wall impingement physics and support the spray-wall model development. The experimental work of spray-wall impingement with non-vaporizing spray characterization, was carried out in a high pressure-temperature constant-volume combustion vessel. The simultaneous Mie scattering of liquid spray and schlieren of liquid and vapor spray were carried out. Diesel fuel was injected at a pressure of 1500 bar into ambient gas at a density of 22.8 kg/m3 with isothermal conditions (fuel, ambient, and plate temperatures of 423 K). A Lagrangian-Eulerian modeling approach was employed to characterize the spray-gas and spray-wall interactions in the CONVERGETM framework by means of a Reynolds-Averaged Navier-Stokes (RANS) formulation.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

An experimental and modeling study was conducted to study the passive regeneration of a catalyzed particulate filter (CPF) by the oxidation of particulate matter (PM) via thermal and Nitrogen dioxide/temperature-assisted means. Emissions data in the exhaust of a John Deere 6.8 liter, turbocharged and after-cooled engine with a low-pressure loop EGR and a diesel oxidation catalyst (DOC) - catalyzed particulate filter (CPF) in the exhaust system was measured and used for this study. A series of experiments was conducted to evaluate the performance of the DOC, CPF and DOC+CPF configurations at various engine speeds and loads.

Steady-state particulate loading experiments were conducted on an advanced production catalyzed particulate filter (CPF), both with and without a diesel oxidation catalyst (DOC). A heavy-duty diesel engine was used for this study with the experiments conducted at 20, 40, 60 and 75 % of full load (1120 Nm) at rated speed (2100 rpm). The data obtained from these experiments were used and are necessary for calibrating the MTU 1-D 2-Layer CPF model. These experimental and modeling results were compared to previous research conducted at MTU that used the same engine but an earlier development version of the combination of DOC and CPF. The motivation for the comparison of the two systems was to determine whether the reformulated production catalysts performed as good or better than the early development catalysts. The results were compared to understand the filtration and oxidation differences between the two DOC+CPF and the CPF-only aftertreatment systems.

Model-based control strategies are important for meeting the dual objective of maximizing NOx reduction and minimizing NH3 slip in urea-SCR catalysts. To be implementable on the vehicle, the models should capture the essential behavior of the system, while not being computationally intensive. This paper discusses the adequacy of two different reduced order SCR catalyst models and compares their performance with a higher order model. The higher order model assumes that the catalyst has both diffusion and reaction kinetics, whereas the reduced order models contain only reaction kinetics. After describing each model, its parameter identification and model validation based on experiments on a Navistar I6 7.6L engine are presented. The adequacy of reduced order models is demonstrated by comparing the NO, NO2 and NH3 concentrations predicted by the models to their concentrations from the test data.

Optimal fuel injection strategies are obtained with a micro-genetic algorithm and an adaptive gradient method for a nonroad, medium-speed DI diesel engine equipped with a multi-orifice, asynchronous fuel injection system. The gradient optimization utilizes a fast-converging backtracking algorithm and an adaptive cost function which is based on the penalty method, where the penalty coefficient is increased after every line search. The micro-genetic algorithm uses parameter combinations of the best two individuals in each generation until a local convergence is achieved, and then generates a random population to continue the global search. The optimizations have been performed for a two pulse fuel injection strategy where the optimization parameters are the injection timings and the nozzle orifice diameters.

The current study was focused on flow field measurements of diesel sprays. The global fuel spray characteristics, such as spray penetration, have also been measured. Particle Image Velocimetry (PIV) was utilized for flow field measurements and the global spray characteristics were recorded with high-speed back light photographing. The flow field was scanned to get an idea of the compatibility of PIV technique applied to dense and high velocity sprays. It is well proven that the PIV technique can be utilized at areas of low number density of droplets, but the center of the spray is way beyond the ideal PIV measurement conditions. The depth at which accurate flow field information can be gathered was paid attention to.

A quasi-steady 1-dimensional computer model of a catalyzed particulate filter (CPF) capable of simulating active regeneration of the CPF via diesel fuel injection upstream of a diesel oxidation catalyst (DOC) or other means to increase the exhaust gas temperature has been developed. This model is capable of predicting gaseous species concentrations (HC's, CO, NO and NO2) and exhaust gas temperatures within and after the CPF, for given input values of gaseous species and PM concentrations before the CPF and other inlet variables such as time-varying temperature of the exhaust gas at the inlet of the CPF and volumetric flow rate of exhaust gas.

As the incentive to produce cleaner and more efficient engines increases, diesel engines will become a primary, worldwide solution. Producing diesel engines with higher efficiency and lower emissions requires a fundamental understanding of the interaction of the injected fuel with air as well as with the surfaces inside the combustion chamber. One aspect of this interaction is spray impingement on the piston surface. Impingement on the piston can lead to decreased combustion efficiency, higher emissions, and piston damage due to thermal loading. Modern high-speed diesel engines utilize high pressure common-rail direct-injection systems to primarily improve efficiency and reduce emissions. However, the high injection pressures of these systems increase the likelihood that the injected fuel will impinge on the surface of the piston.

A one-dimensional model simulating the oxidation of CO, HC, and NO was developed to predict the gaseous emissions downstream of a diesel oxidation catalyst (DOC). The model is based on the conservation of mass, species, and energy inside the DOC and draws on past research literature. Steady-state experiments covering a wide range of operating conditions (exhaust temperatures, flow rates and gaseous emissions) were performed, and the data were used to calibrate and validate the model. NO conversion efficiencies of 50% or higher were obtained at temperatures between 300°C and 350°C. CO conversion efficiencies of 85% or higher and HC conversion efficiencies of 75% or higher were found at every steady state condition above 200°C. The model agrees well with the experimental results at temperatures from 200°C to 500°C, and volumetric flow rates from 8 to 42 actual m3/min.

This study presents diesel spray breakup regimes and the wave model basic theory from literature. The RD wave model and the KH-RT wave model are explained. The implementation of the KH-RT wave model in a commercial CFD code is briefly presented. This study relies on experimental data from non-evaporating sprays that have earlier been measured at Helsinki University of Technology. The simulated fuel spray in a medium-speed diesel engine had a satisfactory match with the experimental data. The KH-RT wave model resulted in a much faster drop breakup than with the RD wave model. This resulted in a thin spray core with the KH-RT model. The fuel viscosity effect on drop sizes was well predicted by the KH-RT wave model.

The characteristics of combustion knock metrics over a number of engine cycles can be an essential reference for knock detection and control in internal combustion engines. In a Spark-Ignition (SI) engine, the stochastic nature of combustion knock has been shown to follow a log-normal distribution. However, this has been derived from experiments done with gasoline only and applicability of log-normal distribution to dual-fuel combustion knock has not been explored. To evaluate the effectiveness and accuracy of log-normal distributed knock model for methane-gasoline blended fuel, a sweep of methane-gasoline blend ratio was conducted at two different engine speeds. Experimental investigation was conducted on a single cylinder prototype SI engine equipped with two fuel systems: a direct injection (DI) system for gasoline and a port fuel injection (PFI) system for methane.

The accurate modeling of fuel spray behavior under diesel engine conditions requires well-characterized boundary conditions. Among those conditions, the spray cone angle is important due to its impact on the spray mixing process, flame lift-off locations and subsequent soot formation. The spray cone angle is a highly dynamic variable, but existing correlations have been developed mainly for diesel fuels at quasi-steady state and relatively low injection pressures. The objective of this study was to develop spray cone angle correlations for both diesel and a light-end gasoline fuel over a wide range of diesel-engine operating conditions that are capable of capturing both the transient and quasi-steady state processes. Two important macroscopic characteristics of solid cone sprays, the spray cone angle and spray penetration, were measured using a single-hole heavy-duty injector using two fuels at diesel engine conditions in an optical constant volume vessel.

High fidelity engine simulation requires realistic fuel models. Although typical automotive fuels consist of more than few hundreds of hydrocarbon species, researches show that the physical and chemical properties of the real fuels could be represented by appropriate surrogate fuel models. It is desirable to represent the fuel using the same set of physical and chemical surrogate components. However, when the reaction mechanisms for a certain physical surrogate component is not available, the chemistry of the unmatched physical component is described using that of a similar chemical surrogate component at the expense of accuracy. In order to reduce the prediction error while maintaining the computational efficiency, a method of on-the-fly reactivity adjustment (ReAd) of chemical reaction mechanism along with fuel re-distribution based on reactivity is presented and tested in this study.