ABSTRACT
The high-speed liquid-jet velocity achieved using an injector strongly depends on the piston motion, physical property of the liquid, and container shape of the injector. Herein, we investigate the liquid ejection mechanism and a technique for estimating the ejection velocity of a high-speed liquid jet using a pyro jet injector (PJI). We apply a two-dimensional numerical simulation with an axisymmetric approximation using the commercial software ANSYS/FLUENT. To gather the input data applied during the numerical simulation, the piston motion is captured with a high-speed CMOS camera, and the velocity of the piston is measured using motion tracking software. To reproduce the piston motion during the numerical simulation, the boundary-fitted coordinates and a moving boundary method are employed. In addition, we propose a fluid dynamic model (FDM) for estimating the high-speed liquid-jet ejection velocity based on the piston velocity. Using the FDM, we consider the liquid density variation but neglect the effects of the liquid viscosity on the liquid ejection. Our results indicate that the liquid-jet ejection velocity estimated by the FDM corresponds to that predicted by ANSYS/FLUENT for several different ignition-powder weights. This clearly shows that a high-speed liquid-jet ejection velocity can be estimated using the presented FDM when considering the variation in liquid density but neglecting the liquid viscosity. In addition, some characteristics of the presented PJI are observed, namely, (1) a very rapid piston displacement within 0.1 ms after a powder explosion, (2) piston vibration only when a large amount of powder is used, and (3) a pulse jet flow with a temporal pulse width of 0.1 ms.
ABSTRACT
The sensitivity of the gradient oscillatory number (GON), which is a potential hemodynamic indicator for cerebral aneurysm initiation, to flow input waveform shapes was examined by performing computational fluid dynamics (CFD) simulations of an anatomical model of a human internal carotid artery under three different waveform shape conditions. The local absolute variation (standard deviation) and relative variation (coefficient of variation) of the GON calculations for three waveform shapes were computed to quantify the variation in GON due to waveform shape changes. For all waveform shapes, an elevated GON was evident at a known aneurysm site, albeit occurring at additional sites. No significant differences were observed among the qualitative GON distributions derived using the three different waveform shapes. These results suggest that the GON is largely insensitive to the variability in flow input waveform shapes. The quantitative analysis revealed that GON displays an improved relative variation over a relatively high GON range. We therefore conclude that it is reasonable to use assumed flow input waveform shapes as a substitute for individual real waveform shapes for the detection of possible GON elevations of individual clinical cases in large-scale studies, where the higher values of GON are of primary interest.
