CFD modeling of spray evolution for spark-ignition, direct injection engines

Paredi, Davide; Lucchini, Tommaso; D’Errico, Gianluca; Onorati, Angelo; Pickett, Lyle M.; Lacey, Joshua

doi:10.1063/1.5138858

Cited by 12 publications

(17 citation statements)

References 13 publications

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“…An over prediction is observed in the region of maximum penetration due to a possible higher concentration of liquid in the proximity of the plumes' tip in comparison with the experiments. 46 The analysis of the droplet characteristic diameters in Figures 12 and 13 reveals how the RD break-up model was able to capture the experimental quantities with the lowest level of error, showing a reasonable sigmoid shape aligned with the experimental trends. The KH-RT model instead shows an abnormal inflexion around the upper limit of the cumulative distribution.…”

Section: Factorial Screening and Spray Model Selectionmentioning

confidence: 70%

“…Plume tip: This region is usually characterised by a higher concentration of fuel vapour and a fully developed secondary break-up. 46 The clusters of highly disintegrated droplets (D d \ 10 mm) mimic the combined effect of turbulent motion and drag force, as well as the longest residence time (0.8 ms) of the liquid injected.…”

Section: Plume Bulkmentioning

confidence: 98%

“…The images show a more jagged shape of the experimental plumes compared to the smoothness of the numerical ones, likely to be due to the subtle threshold between liquid and vapour phase. 15,[46][47][48] Moreover, the RANS numerical approach simplifies the smallest turbulent scales, adding another potential source of difference. 49 However, the trade-off between computational costs and accuracy was considered acceptable.…”

Section: Spray Morphology Analysismentioning

confidence: 99%

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A systematic approach to calibrate spray and break-up models for the simulation of high-pressure fuel injections

Sciortino

Bonatesta

Hopkins

et al. 2021

International Journal of Engine Research

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A novel calibration methodology is presented to accurately predict the fundamental characteristics of high-pressure fuel sprays for Gasoline Direct Injection (GDI) applications. The model was developed within the Siemens Simcenter STAR-CD 3D CFD software environment and used the Lagrangian–Eulerian solution scheme. The simulations were carried out based on a quiescent, constant volume, computational vessel to reproduce the real spray testing environment. A combination of statistic and optimisation methods was used for spray model selection and calibration and the process was supported by a wide range of experimental data. A comparative study was conducted between the two most commonly used models for fuel atomisation: Kelvin–Helmholtz/Rayleigh–Taylor (KH–RT) and Reitz–Diwakar (RD) break-up models. The Rosin–Rammler (RR) mono-modal droplet size distribution was tuned to assign initial spray characteristics at the critical nozzle exit location. A half factorial design was used to reveal how the various model calibration factors influence the spray properties, leading to the selection of the dominant ones. Numerical simulations of the injection process were carried out based on space-filling Design of Experiment (DoE) schedules, which used the dominant factors as input variables. Statistical regression and nested optimisation procedures were then applied to define the optimal levels of the model calibration factors. The method aims to give an alternative to the widely used trial-and-error approach and unveils the correlation between calibration factors and spray characteristics. The results show the importance of the initial droplet size distribution and secondary break-up coefficients to accurately calibrate the entire spray process. RD outperformed KH–RT in terms of prediction when comparing numerical spray tip penetration and droplet size characteristics to the experimental counterparts. The calibrated spray model was able to correctly predict the spray properties over a wide range of injection pressure. The work presented in this paper is part of the APC6 DYNAMO project led by Ford Motor Company.

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Section: Factorial Screening and Spray Model Selectionmentioning

confidence: 70%

Section: Plume Bulkmentioning

confidence: 98%

Section: Spray Morphology Analysismentioning

confidence: 99%

See 1 more Smart Citation

A systematic approach to calibrate spray and break-up models for the simulation of high-pressure fuel injections

Sciortino

Bonatesta

Hopkins

et al. 2021

International Journal of Engine Research

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“…Generally, both the penetration lengths are directly proportional to the KH time constant ( B 1 ), that is, decreasing B 1 decreases the penetration lengths. 39 In literature, a wide range of values for B 1 (2 to 120) have been reported 40–42 based on different solvers and numerical approaches used. In this study, three values of B 1 were chosen: 7 (default), 20 and 50.…”

Section: Resultsmentioning

confidence: 99%

Coupled in-nozzle flow and spray simulation of Engine Combustion Network Spray-G injector

Mohan

Badra

Sim

et al. 2020

International Journal of Engine Research

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A coupled Eulerian-Lagrangian approach was employed to Engine Combustion Network (ECN) Spray-G simulations. The Eulerian in-nozzle flow simulation was conducted with a small plenum attached to the nozzles, and the results were fed to the Lagrangian spray simulation. For Eulerian simulation, the homogeneous relaxation model (HRM) coupled with the volume of fluid (VOF) method was used. HRM proved to be good at predicting the phase change phenomena due to vaporization mechanisms, that is, both cavitation and flash boiling. As a one-way coupling, quantities such as rate of injection (ROI), mass injected through each hole, discharge coefficient, spray plume angle and half cone angle predicted from the Eulerian simulations were used as the initial and boundary conditions for the subsequent Lagrangian spray simulations using the blob injection model. Non-flashing (Spray-G1) and flashing (Spray-G2) spray was simulated, and the results were validated quantitatively against the published data in terms of the liquid and vapor penetration lengths, and good agreements were obtained. Furthermore, the simulation predicted the liquid and gas axial velocity and sauter mean diameter for Spray-G1 condition in agreement with the droplet size and particle image velocimetry (PIV) measurements from literature.

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“…All simulations were carried out with the standard k − − turbulence model with a turbulent dissipation constant, C 1 , of 1.55 increased from a standard value of 1.44. The model was chosen due to the improved spray morphology and gas entrainment it entails as compared with other models, such as the RNG k − − model [26]. Increasing the C 1 parameter is consistent with the "round jet correction" according to Pope and improves the spray jet morphology when the standard k − − is used to resolve the turbulent field [27].…”

Section: Numerical Methodologymentioning

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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CFD modeling of spray evolution for spark-ignition, direct injection engines

Cited by 12 publications

References 13 publications

A systematic approach to calibrate spray and break-up models for the simulation of high-pressure fuel injections

A systematic approach to calibrate spray and break-up models for the simulation of high-pressure fuel injections

Coupled in-nozzle flow and spray simulation of Engine Combustion Network Spray-G injector

Numerical Analysis of GDI Flash Boiling Sprays Using Different Fuels

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