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Journal Article

Characterization of Hollow Cone Gas Jets in the Context of Direct Gas Injection in Internal Combustion Engines

2018-04-03
2018-01-0296
Direct injection (DI) compressed natural gas (CNG) engines are emerging as a promising technology for highly efficient and low-emission engines. However, the design of DI systems for compressible gas is challenging due to supersonic flows and the occurrence of shocks. An outwardly opening poppet-type valve design is widely used for DI-CNG. The formation of a hollow cone gas jet resulting from this configuration, its subsequent collapse, and mixing is challenging to characterize using experimental methods. Therefore, numerical simulations can be helpful to understand the process and later to develop models for engine simulations. In this article, the results of high-fidelity large-eddy simulation (LES) of a stand-alone injector are discussed to understand the evolution of the hollow cone gas jet better.
Technical Paper

On Predictive Nozzle Simulations with Advanced Equations of State and Pressure Boundary Conditions

2022-03-29
2022-01-0504
The reduction of harmful emissions is a key challenge in fighting climate change and global warming. Besides battery electric vehicles (BEVs), improved engine efficiency and alternate fuels, such as e-fuels or biofuels, can improve the emission budget of the transportation sector. Pred ictive simulations can be utilized as these avoid relying on slow manufacturing processes and expensive experiments. As the properties of alternative fuels can change drastically compared to classical fuels, even engine parameters, such as the mass flow rate, need to be reevaluated and optimized. However, simulation frameworks often rely on mass flow rates as input quantity, and hence, a prediction is impossible. This paper gives accurate pressure-based boundary conditions for multiphase systems and focuses on equations of state (EOS) employed in homogeneous equilibrium models (HEMs). Additionally, a dual-density approach is introduced to correct modeling errors that are intrinsic to a particular EOS.
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