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Emissions from Diesel engines have been a major concern for many years, particularly with regards to the impact of NOx and particulate matter on human health. Exhaust gas re-circulation (EGR) is a widely used method in diesel engines for controlling NOx production. While EGR rates can be varied to ensure engine performance and reduce NOx emissions, EGR also influences the ignition delay, reduces the peak combustion temperature and increases particulate emissions. Moreover, the injection timing directly affects NOx and particulate emissions under the broad and highly variable operating conditions. An effective CFD-based design tool for diesel engines must therefore include robust and accurate predictive capabilities for combustion and pollutant formation, to address the complex design tradeoffs. The objective of the present study is to evaluate CFD modeling of diesel engine combustion and emissions for various combinations of EGR rates and injection timings.

One of the best tools to explore complicated in-cylinder physics is computational fluid dynamics (CFD). In order to assess the accuracy and reliability of the CFD simulations, it is critical to perform validation studies over different engine operating conditions. Simulation-based design of SI engines requires predictive capabilities, where results do not need to be tuned for each operating condition. This requires the models adopted to simulate their respective engine physics to be reliable under a broad range of conditions. A detailed set of experimental data was obtained to validate the CFD predictions of SI engine combustion.

Gasoline Direct Injection (GDI) is a key technology in the automotive industry for improving fuel economy and performance of gasoline internal combustion engines. GDI engine performance and emission characteristics are mainly determined by the complex interaction of in-cylinder flow, mixture formation and subsequent combustion processes. In a GDI engine, mixture formation depends on spray characteristics. Spray evolution and mixture formation is critical to GDI engine operation. In this work, a multi-component surrogate fuel blend was used to represent the chemical and physical properties of the gasoline employed in the experimental engine tests. Multi-component spray models were also validated in this study against experimental spray injection measurements in a chamber. The spray-chamber data include spray-penetration lengths, transient spray velocities and droplet Sauter mean diameter (SMD) at different axial and radial distances from the spray tip, obtained using a PDPA system.

Large-eddy simulation (LES) is a useful approach for the simulation of turbulent flow and combustion processes in internal combustion engines. This study employs the ANSYS Forte CFD package and explores several key and fundamental components of LES, namely, the subgrid-scale (SGS) turbulence models, the numerical schemes used to discretize the transport equations, and the computational mesh. The SGS turbulence models considered include the classic Smagorinsky model and a dynamic structure model. Two numerical schemes for momentum convection, quasi-second-order upwind (QSOU) and central difference (CD), were evaluated. The effects of different computational mesh sizes controlled by both fixed mesh refinement and a solution-adaptive mesh-refinement approach were studied and compared. The LES models are evaluated and validated against several flow configurations that are critical to engine flows, in particular, to fuel injection processes.

Accurate modeling of the characteristics of diesel-engine combustion leads to more efficient design. Accurate modeling in turn depends on correctly capturing spray dynamics, turbulence, and fuel chemistry. This work presents a computational fluid dynamics (CFD) investigation of a well characterized small-bore direct injection diesel engine at Sandia National Laboratories’ Combustion Research Facility. The engine has been studied for two piston-bowls geometries and various injection timings. Simulation of these conditions test the predictive capabilities of our approach to diesel engine modeling using Ansys Forte. An experimental database covering a wide range of operating conditions is provided by the Engine Combustion Network for this engine, which is used to validate our modeling approach. Automatic and solution-adaptive meshing is used, and the recommended settings are discussed.

This study focuses on Lagrangian spray models that are commonly used in engine CFD simulations. In modeling sprays, droplet collision is one of the physical phenomena that must be accounted for. There are two main parts of droplet collision models for sprays - detecting colliding pairs of droplets and predicting the outcomes of these collisions. For the first part, we focus on the efficiency of the algorithm. We present an implementation of the arbitrary adaptive collision mesh model of Hou and Schmidt [1], and examine its efficiency in dealing with large simulations. Through theoretical analysis and numerical tests, we show that the computational cost of this model scales pseudo-linearly with respect to the number of parcels in the sprays. Regarding the second part, we examine the variations in existing phenomenological models used for predicting binary droplet collision outcomes. A quantitative accuracy metric is used to evaluate the models with respect to the experimental data set.