Development of limited-view tomography for measurement of Spray G plume direction and liquid volume fraction

Abstract

The method for direct injection of fuel in the cylinder of an IC engines is important to high-efficiency and low-emission performance. Optical spray diagnostics plays an important role in understanding plume movement and interaction for multihole injectors, and providing baseline understanding used for computational optimization of fuel delivery. Traditional planar or line-of-sight diagnostics fail to capture the liquid distribution because of optical thickness concerns. This work proposes a high-speed (67 kHz) extinction imaging technique at various injector rotations coupled to computed tomography (CT) for time-resolved reconstruction of liquid volume fraction in three dimensions. The number of views selected and processing were based on synthetic (modeled) liquid volume fraction data where extinction and CT adequately reconstructed each plume. The exercise showed that for an 8-hole, symmetric-design injector (ECN Spray G), only three different views are enough to reproduce the direction of each plume, and particularly the mean plume direction. Therefore, the number of views was minimized for experiments to save expense. Measurements applying this limited-view technique confirm plume-plume variations also detected with mechanical patternation, while providing better spatial and temporal resolution than achieved previously. Uncertainties due to the limited view within pressurized spray chambers, the droplet size, and optically thick regions are discussed.

Comparative Investigation of Gasoline-like Surrogate Fuels using 3D Computed Tomography

“…where d(=7 μm) and Cext(=53.8•10 -6 mm 2 ) refer to a representative average value of the droplet diameters range in the spray region and to the extinction coefficient designated by the optical setup, respectively. The method followed to determine the values of d and Cext are discussed in detail in [4]. The 72 PLV slices at each axial location were subsequently fed to a Filtered Back Projection algorithm, to create the time-resolved volumetric liquid volume fraction distributions of the spray plume.…”

“…Firstly, to introduce a novel many-view Computed-Tomography (CT) reconstruction technique combined with DBI extinction imaging that can quantify the 3D composition of asymmetric sprays. This approach differs from prior studies in that dozens of views (72) are used to reconstruct the liquid phase distribution in the spray, as compared to reconstruction using a handful of views that relied on geometric symmetry of the spray [4,5]. The second goal is to apply the technique to verify the capability of well-characterised surrogates to replicate the vaporisation and mixing behaviour of standardised gasolineethanol blends.…”

Machine Learning and transcritical sprays: A demonstration study of their potential in ECN Spray-A

“…Similarly to the above, for dodecane/nitrogen mixture modelling a property table of ;4.10 6 elements, was used, with resolution of 100 3 400 3 101 corresponding to regular log 10 p, T, y intervals, for a range of p:(10 Pa-2500 bar) 3 T:(280-2000K) 3 y:(0-1). In that case, the Artificial Neural Network was structured to receive as an input combinations of (log 10 p, T, y) and output r. The minimal neural network structure that was found to have a very good performance in capturing both sharp density variations near the saturation curve of pure dodecane and the smooth density transition in the multi-component mixture or pure nitrogen was a four layer structure with (20,5,10,20) hidden neurons respectively. From these tables, 90% of the data were used for training, 5% for validation and 5% for testing.…”

“…The Artificial Neural Network was structured to receive as an input combinations of (log 10 p, T) and output r, though application is straightforward for other thermodynamic variables as well. The minimal neural network structure that was found to have a very good performance in capturing both sharp density variations near the saturation curve and the smooth density transition beyond the critical point was a three layer structure with (4,10,20) hidden neurons respectively. Similarly to the above, for dodecane/nitrogen mixture modelling a property table of ;4.10 6 elements, was used, with resolution of 100 3 400 3 101 corresponding to regular log 10 p, T, y intervals, for a range of p:(10 Pa-2500 bar) 3 T:(280-2000K) 3 y:(0-1).…”

“…Indicatively, on the experimental techniques for spray diagnostics, notable recent works involve, for example, the pioneering works of Pickett et al 9 in an effort to quantify fuel mixing and penetration using Rayleigh Scattering investigations in high pressure and temperature Diesel injection. The works of Weiss et al 10 and Hwang et al 11 discussing the development and application of a novel optical 3D tomography technique for reconstructing the 3D liquid volume fraction of gasoline sprays, or Strek et al 12 exploring the capabilities of X-ray radiography and tomography to determine density distribution of gasoline sprays. Computational models have evolved to capture the turbulent mixing of fuel sprays with the ambient gases, see, for example, the works of Yue et al 13 in gasoline injectors and sprays, the fundamental work on highly resolved fuel break-up by Agarwal and Trujillo 14 investigating the effect of geometrical features of the injector in spray formation for the Engine Combustion Network (ECN) Spray-A injector, 15 or subsequent investigations of Battistoni et al 16 in Diesel injector related geometries.…”