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The combustion efficiency of direct injection engines is largely dependent upon the mixing of fuel in air, thereby creating a combustible mixture. Such a process is highly dependent upon the motion of the charge in the cylinder. The shape of the intake runners and valves determines the charge motion generated within the engine. Swirl and tumble, generated along the vertical and horizontal axis respectively, govern the charge motion and hence distribution of combustible mixture. Unlike traditional parametric optimization where the parameter space has to be predetermined, adjoint optimization utilizes the gradient of objective functions obtained from a computational fluid dynamics solution to modify the shape of the original CAD geometry. During the optimization process, specific parts of the geometry can be morphed in any direction freely. The final design is a fluid volume generated as a result of such adjoint computations.

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.