Abstract
The introduction of new emissions tests in real driving conditions (Real Driving Emissions—RDE) as well as of improved harmonized laboratory tests (World Harmonised Light Vehicle Test Procedure—WLTP) is going to dramatically cut down NOx and particulate matter emissions for new car models that are intended to be fully Euro 6d compliant from 2020 onwards. Due to the technical challenges related to exhaust gases’ aftertreatment in small-size diesel engines, the current powertrain development trend for light passenger cars is shifted towards the application of different degrees of electrification to highly optimized gasoline direct injection (GDI) engines. As such, the importance of reliable multidimensional computational tools for GDI engine optimization is rapidly increasing. In the present paper, we assess a hybrid scale-resolving turbulence modeling technique for GDI fuel spray simulation, based on the Engine Combustion Network “Spray G” standard test case. Aspects such as the comparison with Reynolds-averaged methods and the sensitivity to the spray model parameters are discussed, and strengths and uncertainties of the analyzed hybrid approach are pointed out. The outcomes of this study serve as a basis for the evaluation of scale-resolving turbulence modeling options for the development of next-generation directly injected thermal engines.
Highlights
The efficient design of small-sized gasoline direct injection (GDI) engines for the EU market is a key factor for current car manufacturers, which are going to face the forthcoming regulations on polluting emissions [1]
We validated the numerical model by performing a URANS simulation and by comparing the results, in terms of both liquid and vapor penetration, with the available experimental data of Sandia National Laboratories (SNL) for the baseline “Spray G”
We investigated the applicability of two different variants of a hybrid turbulence modeling methodology for the simulation of GDI sprays
Summary
The efficient design of small-sized gasoline direct injection (GDI) engines for the EU market is a key factor for current car manufacturers, which are going to face the forthcoming regulations on polluting emissions [1]. In order to reduce turnaround times, an increasing amount of resources are being devoted to high-fidelity computational fluid dynamics (CFD) during the development and optimization of fuel injection systems [2,3,4,5]. The most widespread method for the multidimensional modeling of reactive and non-reactive fuel sprays is the Eulerian–Lagrangian (EL) approach, in which the gaseous phase treatment follows the classical Eulerian description while the liquid phase is tracked as a discrete set of Lagrangian particles (LPs). Turbulence modeling has a great influence on LP tracking, as it is directly linked to the spray particle aerodynamics and dispersion and, to the spray pattern and fuel mixing in the engine cylinder volume. The EL approach was originally coupled to time-dependent forms of Reynolds-averaged turbulence treatments (i.e., to the so-called unsteady-RANS (URANS) turbulence models). The adoption of URANS commonly represents a good compromise between computational
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