Nozzle Flow Simulation of GDi for Measuring Near-Field Spray Angle and Plume Direction

Payri, Raúl; Martí-Aldaraví, Pedro; Martínez, María

doi:10.4271/2019-01-0280

Cited by 13 publications

(11 citation statements)

References 32 publications

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“…The conditions used in the simulation are reported in Table 4. The two most critical values are the spray angle, taken from the work of Payri et al [35] and the coefficient of area, from Moulai et al [29]. For spray angle, Payri et al performed CFD simulations that included the internal flow.…”

Section: Resultsmentioning

confidence: 99%

The Eulerian Lagrangian Mixing-Oriented (ELMO) Model

Schmidt¹,

Haghshenas²,

Mitra³

et al. 2021

Preprint

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Past Lagrangian/Eulerian modeling has served as a poor match for the mixinglimited physics present in many sprays. Though these Lagrangian/Eulerian methods are popular for their low cost, they are ill-suited for the physics of the dense spray core and suffer from limited predictive power. A new spray model, based on mixinglimited physics, has been constructed and implemented in a multi-dimensional CFD code. The spray model assumes local thermal and inertial equilibrium, with air entrainment being limited by the conical nature of the spray. The model experiences full two-way coupling of mass, momentum, species, and energy. An advantage of this approach is the use of relatively few modeling constants. The model is validated with three different sprays representing a range of conditions in diesel and gasoline engines.

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Section: Resultsmentioning

confidence: 99%

The Eulerian Lagrangian Mixing-Oriented (ELMO) Model

Schmidt¹,

Haghshenas²,

Mitra³

et al. 2021

Preprint

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show abstract

“…To date, and due to the lack of experimental data because of the complexity of the calculation, the external flow studies have been carried out starting from an initial ad hoc value for the cone angle and calibrate it to match experimental data, thus leading to incorrect approximations. This calculation has already been carried out in previous works of this group [12], although the used methodology was different (explained below). The new technique avoid dependence on image quality and take into account the influence of geometry on jet development [9].…”

Section: Resultsmentioning

confidence: 99%

“…The main features of this injector are its nominal diameters of 165 µm and 388 µm respectively, the ratio between the length and diameter of the counter-bored nozzle holes of 1 to 1.2 and the inclination of 37 • of the geometrical axis of the holes. Simulations carried out in the analysis are based on the operating condition proposed by ECN and named after the injector, Spray G (explained in detail in [12]). Concerning the initialization and boundary conditions, same procedure presented in previous works has been applied in this study [12].…”

Section: Geometry Details Computational Domain and Operating Conditionsmentioning

confidence: 99%

“…Simulations carried out in the analysis are based on the operating condition proposed by ECN and named after the injector, Spray G (explained in detail in [12]). Concerning the initialization and boundary conditions, same procedure presented in previous works has been applied in this study [12]. The only difference relates to the initialization of the turbulence where the use of LES models, one equation in this particular case, allows to initialize only the turbulent kinetic energy defined through the turbulent intensity with a value of 0.01.…”

Section: Geometry Details Computational Domain and Operating Conditionsmentioning

confidence: 99%

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Large Eddy Simulations of the fuel injection and mixing process of the ECN GDi Injector Spray G

Payri

Martí-Aldaraví

Martínez

2021

ICLASS

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Understanding unsteady effects of the Gasoline Direct injection (GDi) process, due to short injection duration, plays a major role in the analysis of the mixture formation and so combustion efficiency in the spark ignition engines. Focusing on the phase when the needle is at its maximum lift, there still are some uncertainties. For instance, the time evolution of the upstream pressure, together with the detailed geometry of the needle and the seat, shape of the rate of injection could affect the uniformity of the flow between orifices. Experimentally addressing these issues nowadays remains challenging, if not impossible, so Computational Fluid Dynamics (CFD) are used. Homogeneous Relaxation Model (HRM) is employed to consider the mass exchange between liquid and vapor phases of the fuel inside the nozzle. Different Large Eddy Simulations (LES) sub-grid models are employed in order to account for the unsteady effects of turbulence and analyze the predictive capabilities in resolving the spatial-temporal scales. Results are validated against experimental data. Vortices are generated inside the counterbore and enhance the liquid dispersion near the injector. Spray parameters, after a proper time-averaging, also accurately match the results reported in the literature. Root mean square velocity (Urms) fluctuation has higher levels of spatially located fluctuations in areas with significant levels of small-scale turbulence and noticeable liquid-vapor mixture.

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“…It can also be noted that the numerical spray evolves with a larger spray width which could be attributed to the adopted drill angle in this study, 37°. There are various uncertainties in literature [15,42] regarding the optimal drill angle value. Therefore, the drill angle was not used as a tuning factor in this study and the value of 37°was kept constant for all the simulations.…”

Section: Baseline G1 Conditionmentioning

confidence: 99%

Numerical Analysis of GDI Flash Boiling Sprays Using Different Fuels

et al. 2021

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Modeling the fuel injection process in modern gasoline direct injection engines plays a principal role in characterizing the in–cylinder mixture formation and subsequent combustion process. Flash boiling, which usually occurs when the fuel is injected into an ambient pressure below the saturation pressure of the liquid, is characterized by fast breakup and evaporation rates but could lead to undesired behaviors such as spray collapse, which significantly effects the mixture preparation. Four mono–component fuels have been used in this study with the aim of achieving various flashing behaviors utilizing the Spray G injector from the Engine Combustion Network (ECN). The numerical framework was based on a Lagrangian approach and was first validated for the baseline G1 condition. The model was compared with experimental vapor and liquid penetrations, axial gas velocity, droplet sizes and spray morphology and was then extended to the flash boiling condition for iso–octane, n–heptane, n–hexane, and n–pentane. A good agreement was achieved for most of the fuels in terms of spray development and shape, although the computed spray morphology of pentane was not able to capture the spray collapse. Overall, the adopted methodology is promising and can be used for engine combustion modeling with conventional and alternative fuels.

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Nozzle Flow Simulation of GDi for Measuring Near-Field Spray Angle and Plume Direction

Cited by 13 publications

References 32 publications

The Eulerian Lagrangian Mixing-Oriented (ELMO) Model

The Eulerian Lagrangian Mixing-Oriented (ELMO) Model

Large Eddy Simulations of the fuel injection and mixing process of the ECN GDi Injector Spray G

Numerical Analysis of GDI Flash Boiling Sprays Using Different Fuels

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