This article reports nonintuitive characteristic of a splashing drop on a solid surface discovered through extracting image features using a feedforward neural network (FNN). Ethanol of area-equivalent radius about 1.29 mm was dropped from impact heights ranging from 4 cm to 60 cm (splashing threshold 20 cm) and impacted on a hydrophilic surface. The images captured when half of the drop impacted the surface were labeled according to their outcome, splashing or nonsplashing, and were used to train an FNN. A classification accuracy ≥96% was achieved. To extract the image features identified by the FNN for classification, the weight matrix of the trained FNN for identifying splashing drops was visualized. Remarkably, the visualization showed that the trained FNN identified the contour height of the main body of the impacting drop as an important characteristic differentiating between splashing and nonsplashing drops, which has not been reported in previous studies. This feature was found throughout the impact, even when one and three-quarters of the drop impacted the surface. To confirm the importance of this image feature, the FNN was retrained to classify using only the main body without checking for the presence of ejected secondary droplets. The accuracy was still ≥82%, confirming that the contour height is an important feature distinguishing splashing from nonsplashing drops. Several aspects of drop impact are analyzed and discussed with the aim of identifying the possible mechanism underlying the difference in contour height between splashing and nonsplashing drops.
This study investigated the fluid–tissue interaction of needle-free injection by evaluating the dynamics of the cavity induced in body-tissue simulant and the resulting unsteady mechanical stress field. Temporal evolution of cavity shape, stress intensity field, and stress vector field during the injection of a conventional injection needle, a proposed highly focused microjet (tip diameter much smaller than capillary nozzle), and a typical non-focused microjet in gelatin were measured using a state-of-the-art high-speed polarization camera, at a frame rate up to 25,000 f.p.s. During the needle injection performed by an experienced nurse, high stress intensity lasted for an order of seconds (from beginning of needle penetration until end of withdrawal), which is much longer than the order of milliseconds during needle-free injections, causing more damage to the body tissue. The cavity induced by focused microjet resembled a funnel which had a narrow tip that penetrated deep into tissue simulant, exerting shear stress in low intensity which diffused through shear stress wave. Whereas the cavity induced by non-focused microjet rebounded elastically (quickly expanded into a sphere and shrank into a small cavity which remained), exerting compressive stress on tissue simulant in high stress intensity. By comparing the distribution of stress intensity, tip shape of the focused microjet contributed to a better performance than non-focused microjet with its ability to penetrate deep while only inducing stress at lower intensity. Dynamic mechanical interaction revealed in this research uncovered the importance of the jet shape for the development of minimally invasive medical devices.
The impact of a drop on a solid surface is an important phenomenon that has various implications and applications. However, the multiphase nature of this phenomenon causes complications in the prediction of its morphological evolution, especially when the drop splashes. While most machine-learning-based drop-impact studies have centred around physical parameters, this study used a computer-vision strategy by training an encoder-decoder to predict the drop morphologies using image data. Herein, we show that this trained encoder-decoder is able to successfully generate videos that show the morphologies of splashing and non-splashing drops. Remarkably, in each frame of these generated videos, the spreading diameter of the drop was found to be in good agreement with that of the actual videos. Moreover, there was also a high accuracy in splashing/non-splashing prediction. These findings demonstrate the ability of the trained encoder-decoder to generate videos that can accurately represent the drop morphologies. This approach provides a faster and cheaper alternative to experimental and numerical studies.
A high-resolution background-oriented schlieren (BOS) technique, which utilizes a high-resolution camera and a microdot background pattern, is proposed and used to measure the pressure field of an underwater shock wave in a microtube. The propagation of the shock wave subsequently reaches a concave water–air interface set in the microtube resulting in the ejection of a focused microjet. This high spatial-resolution BOS technique can measure the pressure field of a shock front with a width as narrow as the order of only $$10^1\,\upmu$$ 10 1 μ m with a peak pressure as large as almost 3 MPa. This significant breakthrough has enabled the simultaneous measurement of the pressure impulse of the shock front and the velocity of the microjet tip. As a result, we have experimentally observed the linear relation between the velocity of the microjet tip and the pressure impulse of the shock front for the cases without secondary cavitation in the liquid bulk. Such relation was theoretically/numerically predicted by Peters et al. (J Fluid Mech 719:587–605, 2013). This study demonstrated the capability of the proposed high-resolution BOS technique as a microscale contactless pressure measurement tool for underwater shock waves and potentially other micro- and nanofluids. Graphical abstract
In order to increase the thermal efficiency of a gas turbine, the operating temperature has to be increased. This increment may cause the material of the blade to melt. Film cooling is a good option to solve this problem. Various studies has been done on film cooling include the shallow hole and sister holes. The present study focused on an experimental of film cooling effectiveness on shallow hole of 20° with upstream sister holes with 3 blowing ratios which are 1.0, 15 and 2.0. The result showed significant improvement compared to shallow hole of 35°. The optimum blowing ratio is 1.5. Smaller shallow angle and upstream sister holes reduce the jet lifting effect of the secondary air flow. Future study can be done on shallow hole of shallow angle and blowing ratio around 1.5 in order to further improve the film cooling effectiveness.
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