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The air-fuel mixture formation in an optical direct-injection internal combustion engine is investigated by numerical simulations for the two biofuels Ethanol and 2-Butanone. The gas phase in the internal combustion (IC) engine is predicted by a large-eddy simulation, in which the fuel phase is determined by a spray model based on Lagrangian particle tracking. A hollow-cone injector is used for which the primary breakup is modeled by a series of small full-cone injections, while the Rosin-Rammler initial droplet size distribution is used. The secondary spray break-up is modeled by the Kelvin-Helmholtz-Rayleigh-Taylor (KHRT) model, and the evaporation of the fuel is determined by the Bellan-Harstad model. The gas phase simulation is based on a finite-volume method formulated for hierarchical Cartesian grids, in which the immersed moving boundaries are resolved using a multiple level-set/cut-cell approach.

The interaction of biofuel sprays from an outward opening hollow cone injector and the flow field inside an internal combustion engine is analyzed by Mie-Scattering Imaging (MSI) and high-speed stereoscopic particle-image velocimetry (stereo-PIV). Two fuels (ethanol and methyl ethyl ketone (MEK)), four injection pressures (50, 100, 150, and 200 bar), three starting points of injection (60°, 277°, and 297° atdc), and two engine speeds (1,500 rpm and 2,000 rpm) define the parameter space of the experiments. The MSI measurements determine the vertical penetration length and the spray cone angle of the ethanol and MEK spray. Stereo-PIV is used to investigate the interaction of the flow field and the ethanol spray after the injection process for a start of injection at 60° atdc. These measurements are compared to stereo-PIV measurements without fuel injection performed in the same engine [19].

Numerical analyses of the liquid fuel injection and the subsequent fuel-air mixing in a high-tumble, long-stroke direct injection engine at operation conditions of 2000 RPM are presented. The Navier-Stokes equations are numerically solved with a finite-volume method for compressible flow based on a hierarchical Cartesian mesh. The solid wall boundaries are represented by a conservative multiple cut- and split-cell method, where a semi-Lagrange level-set solver is used to track the location of the individual moving boundaries. To determine the fuel vapor before ignition, a two-way coupled large-eddy simulation of the turbulent flow field with the spray droplets is conducted. Due to the large number of spray droplets, a Lagrangian Particle Tracking (LPT) algorithm is used to predict the liquid spray penetration and evaporation.

The influence of in-cylinder flow on the propagation of 2-Butanone and Ethanol sprays is studied. To solely evaluate the interaction of air flow and fuel, high-speed Mie-Scattering Imaging of hollow cone sprays is conducted both in a single-cylinder optical engine with tumble movement and in a pressure vessel with negligible air flow. The direct comparison reveals an improved spray propagation of 2-Butanone due to the engine’s air flow. The lower viscosity of 2-Butanone causes an enhanced jet breakup compared to Ethanol such that the spray consists of more and smaller droplets. Small droplets possess a lower momentum, which allows the droplets to be more efficiently transported by the air flow. Consequently, the fuel distribution across the cylinder is enhanced. As the liquid fuel is distributed to a larger volume, improved convection accelerates evaporation.

Numerical analyses of the liquid fuel injection and subsequent fuel-air mixing for a high-tumble direct injection engine with an active pre-chamber ignition system at operation conditions of 2000 RPM are presented. The Navier-Stokes equations for compressible in-cylinder flow are solved numerically using a hierarchical Cartesian mesh based finite-volume method. To determine the fuel vapor before ignition large-eddy flow simulations are two-way coupled with the spray droplets in a Lagrangian Particle Tracking (LPT) formulation. The combined hierarchical Cartesian mesh ensures efficient usage of high performance computing systems through solution adaptive refinement and dynamic load balancing. Computational meshes with approximately 170 million cells and 1.0 million spray parcels are used for the simulations.