Soot characteristics of high-reactivity gasoline under compression-ignition conditions using a gasoline direct injection (GDI) piezoelectric fuel injector

Wang, Libing; Wu, Zengyang; Badra, Jihad; Roberts, William L.; Fang, Tiegang

doi:10.1016/j.fuel.2019.116931

Cited by 14 publications

(1 citation statement)

References 64 publications

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“…Solenoid injectors are more economically viable than piezo injectors because they require lower precision in their manufacturing processes [32,33]. Piezo injectors, on the other hand, are more attractive because of their sensitive and fast response, which are critical for small quantities or closely spaced multiple injections [34]. The most widely used piezo injector is the outwardly opening hollow cone injector where spray features with visible striations and filaments are observed.…”

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confidence: 99%

Dynamics of the Ammonia Spray Using High-Speed Schlieren Imaging

Cheng¹,

Ojanen²,

Diao³

et al. 2022

SAE Int. J. Adv. & Curr. Prac. in Mobility

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A mmonia (NH3), as a carbon-free fuel, has a higher optimization potential to power internal combustion engines (ICEs) compared to hydrogen due to its relatively high energy density (7.1MJ/L), with an established transportation network and high flexibility. However, the NH3 is still far underdeveloped as fuel for ICE application because of its completely different chemical and physical properties compared with hydrocarbon fuels. Among all uncertainties, the dynamics of the NH3 spray at engine conditions is one of the most important factors that should be clarified for optimizing the fuel-air mixing. To characterize the evolution and evaporation process of NH3 spray, a high-speed Z-type schlieren imaging technique is employed to estimate the spray characteristics under different injection pressure and air densities in a constant volume chamber. Three renewable fuels, including NH3, methanol and ethanol, are investigated to compare the differences in their spray behavior at engine-like conditions. The basic parameters of the spray geometry such as spray penetration, spray cone angle and cross-section area are quantified based on the schlieren images postprocessing. The results show that the spray geometry of NH3 differs from that of the other fuels, which exhibits a longer penetration, larger spray cone angle and cross-section area. Moreover, the NH3 also shows a faster evaporation rate than methanol and ethanol. To extract more information from the spray images, an optical flow algorithm is derived to visualize the velocity field based on the schlieren images. The results indicate that NH3 spray is driven to the spray axis under the effect of the vortices. The vortices are induced by the entrainment of the surrounding gas and act as the driving forces that push the spray plumes towards the axis at the same time. The two vortices of NH3 grow much bigger and stronger and move closer to the spray axis compared to the ethanol and methanol.

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confidence: 99%

Dynamics of the Ammonia Spray Using High-Speed Schlieren Imaging

Cheng¹,

Ojanen²,

Diao³

et al. 2022

SAE Int. J. Adv. & Curr. Prac. in Mobility

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Spark Assisted Gasoline Compression Ignition (SAGCI) Engine Strategies

Vallinayagam

Chang

Engineer

et al. 2022

Gasoline Compression Ignition Technology

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Supercritical thermophysical properties prediction of multi-component hydrocarbon fuels based on artificial neural network models

Ming

Liu

Wang

et al. 2021

Sci. China Technol. Sci.

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A good understanding of the thermophysical properties of hydrocarbon fuels at supercritical pressure is important to research on experiment and numerical simulation of fuel supercritical spray. Experimental measurements are difficult to conduct directly because of the extremely high pressure and high temperature. In this study, back propagation (BP) neural network, BP optimized by mind evolution algorithm (MEA-BP) and BP neural network optimized by genetic algorithm (GA-BP) are established to determine the nonlinear temperature-dependent thermophysical properties of density, viscosity, and isobaric specific heat (C p ) of hydrocarbon fuels at supercritical pressure. Meanwhile, approximate formulas for these properties prediction are primarily proposed using polynomial fitting. In this paper, models that can predict three types of physical properties of three kinds of hydrocarbon fuels and their mixtures in a wide temperature range under supercritical pressure are established. In the prediction of density and C p , BP neural network has a good prediction effect. The results show that the MAPE is lower than 2% in the prediction of density and C p , but the MAPE of viscosity prediction is slightly higher than 5% using BP. Furthermore, MEA and GA are used to optimize the prediction of viscosity. The optimization effect and computation of the MEA is better than that of GA because MEA does not have the local optimization and prematurity problems. The present work offers an efficient tool to predict the thermophysical properties of hydrocarbon fuels over a wide range of temperatures under supercritical pressure which can be easily extended to other fuels of interest. It will be beneficial to the experiment and numerical simulation studies of supercritical sprays.

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Soot characteristics of high-reactivity gasoline under compression-ignition conditions using a gasoline direct injection (GDI) piezoelectric fuel injector

Cited by 14 publications

References 64 publications

Dynamics of the Ammonia Spray Using High-Speed Schlieren Imaging

Dynamics of the Ammonia Spray Using High-Speed Schlieren Imaging

Spark Assisted Gasoline Compression Ignition (SAGCI) Engine Strategies

Supercritical thermophysical properties prediction of multi-component hydrocarbon fuels based on artificial neural network models

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