We present an experimental investigation on emulsions created during the impact process between a surfactant-laden droplet and an oil layer on water. By varying the surfactant concentration and the viscosity of oil layer, we created emulsions and visualized them using multi-dimensional high-speed imaging. Our analysis shows that the emulsions are more likely to be unstable and decay within a minute if the impacting droplet contains more surfactant. We also found that there are three mechanisms of generation of emulsions depending on the concentration of surfactant and the viscosity of oil layer; the jet pinch-off, cavity pinch-off, and tearing of oil layer. Jet and cavity pinch-off turned out to be dominant mechanisms for high oil viscosities, while tearing of oil layer is dominant for low oil viscosities. Our result is potentially useful in designing optimal dispersant properties for offshore oil contamination.

In droplet microfluidics, precise droplet manipulation is required in numerous applications. This study presents ultrasonic surface acoustic wave (USAW)-based microfluidic device for label-free droplet separation based on size. The proposed device is composed of a slanted-finger interdigital transducer on a piezoelectric substrate and a polydimethylsiloxane microchannel placed on the substrate. The microchannel is aligned in the cross-type configuration where the USAWs propagate in a perpendicular direction to the flow in the microchannel. When droplets are exposed to an acoustic field, they experience the USAW-induced acoustic radiation force (ARF), whose magnitude varies depending on the droplet size. We modeled the USAW-induced ARF based on ray acoustics and conducted a series of experiments to separate different-sized droplets. We found that the experimental results were in good agreement with the theoretical estimation. We believe that the proposed method will serve as a promising tool for size-based droplet separation in a label-free manner.

A problem on the sloshing flow of highly-viscous fluid in a rectangular box was revisited by both of theoretical approach and experimental visualization method. Based on the theoretical prediction that a linear shape of free surface is prevailing in bulk zone, it has been studied an analogy between a near wall coating flow in sloshing problem and dip coating flow in Landau-Levich problem. Phenomenological observation confirms that, in the case of highly-viscous fluid, I.e., R_e ≪ 1, viscous dominant near-wall flow in sloshing problem is identical to dip coating flow generated by drag-out of the plate being in both motion of vertical translation and horizontal rotation.

Drying process is involved in the production of various products including food, textiles, paper, pharmaceuticals, and batteries. Phase change of liquid to vapor generally requires enormous thermal energy, so in order to save energy, it is advantageous to develop an appropriate drier and use it under appropriate operating conditions, depending on the characteristics of materials. However, due to the complex, multiscale heat and mass transfer occurring during drying processes, predictions of appropriate drying conditions before actual operation are not easily achieved, leading to challenges in designing driers. Here, we developed a lab-scale experimental setup to evaluate the performance of band dries. The experimental setup was used to measure the moisture content and temperature change in the materials being dried in a belt dryer. Experimental results obtained using our lab-scale setup allow us to predict the performance of a full-scale band drier, thus suggesting a practical framework for predicting the drying process of various materials and developing band driers.

The shortage of available freshwater is a major global issue worldwide and an increasing demand for clean water requires efficient water purification strategies. Here we describe a method to drastically increase the efficiency of a microreactor for photocatalytic water purification. To find out how the shape of the catalyst affects water purification, nanostructured catalysts of different structures, such as dense film, nanorod, and nanohelix, are prepared and their water purification characteristics are analyzed. Compared to the flat catalyst, the nanostructured catalyst showed a distinct ability in its pollutant degradation, but the detailed structural variation does not significantly affect the water purification. To further increase efficiency, we apply a micromixer to nanorod-based microreactor, which allows even enhanced mass transfer. This enables the solution of the water purification problem and greatly contributes to the industries where the efficiency of photocatalytic activity has attracted extensive interest.

The interaction of planar shock wave with rectangular water column is investigated numerically. The flow phenomenon like reflection, transmission, cavitation, recirculation of shock wave, and large negative pressure due to expansion waves was discussed qualitatively and quantitatively. The numerical simulation was performed in a shock tube with a water column, and planar shock was initiated with a pressure ratio of 10. Three cases of the water column with different thicknesses, namely 0.5D, 1D, and 2D, were installed and studied. Water naturally has a higher acoustic impedance than air and mitigates the shock wave considerably. The numerical simulations were modelled using Eulerian and Volume of fluids multiphase models. The Eulerian model assumes the water as a finite structure and can visualize the shockwave propagation inside the water column. Through the volume of fluids model, the stages of breakup of the water column and mitigation effects of water were addressed. The numerical model was validated against the experimental results. The computational results show that the installation of a water column significantly impacts the mitigation of shock wave.

The high heat emitted from the process module and heat jacket may cause errors in semiconductor process equipment. Barriers were designed to reduce the temperature of surface on transfer module. A designed barrier was compared and analyzed by numerical analysis using ANSYS Fluent. The average temperature of barrier and effect of radiation heat transfer were also compared through absorbed radiative heat flux of the barrier. The adoption of the barrier had an effect on the radiative heat transfer reduction of the transfer module rod. The effect of the angles of barrier from 50° to 90° on the heat transfer was investigated using the absorbed radiative heat flux with the average temperature. The angle of barrier of 50° reduced the temperature up to 9.6 %.

Underwater environmental pollution caused by microplastic particles is considered to be one of the most serious problems in many oceans and countries nearby. Previous academic studies or field technologies tried to remove the micro-sized particles are often energy-consuming and costly, so it is hard to be employed for the actual uses. In this study, the mechanism of removal of micro-sized polyethylene spheres (size in order of 100㎛) using a rising bubble is experimentally investigated. It is found that the particles are either affected by bubble wake, thus translocated close to the water surface, or pushed far away by the surrounding fluid flow, depending on their initial position relative to the bubble. By scrutinizing the visualized behaviors of bubble-particle interaction, we draw the governing parameter, i.e., the polar angle between the particle and the bubble, to determine the effective capturing of the particles with a rising bubble.

We propose a Cubic-Interpolated Pseudo-Particle Lattice Boltzmann method (CIP-LBM) for the convection-diffusion equation (CDE) based on the Bhatnagar-Gross-Krook (BGK) scheme equation. The CIP-LBM relies on an accurate numerical lattice equilibrium particle distribution function on the advection term and the use of a splitting technique to solve the Lattice Boltzmann equation. Different schemes of lattice spaces such as D1Q3, D2Q5, and D2Q9 have been used for simulating a variety of problems described by the CDE. All simulations were carried out using the BGK model, although another LB scheme based on a collision term like two-relation time or multi-relaxation time can be easily applied. To show quantitative agreement, the results of the proposed model are compared with an analytical solution.

An electrostatic precipitator (ESP) is an industrial post processing facility for high efficiency dust mitigation. Uniformity of the flow passing through the inlet duct leading into the main chamber is important for efficient reduction of dust. To examine flow uniformity, this study conducted a numerical analysis of the flow within a scale-down ESP inlet duct. Magnetic resonance velocimetry (MRV) results from a prior study were utilized to validate the Reynolds-averaged Navier-Stokes (RANS) numerical simulations. Both the experimental and computational results displayed a similar recirculation zone shape and normalized velocity profile near the duct outlet for the baseline geometry. To optimize the uniformity of the flow, the number of guide vanes was modified, and the guide vanes were partially extended straight upward. Design evaluation is done based on the outlet velocity distribution and mass flowrate balance between the two outlets. Simulation results indicate that the vane extension is critical for flow optimization in curved ESP ducts.

The evaluation of the drag and lift as the aerodynamic performance of airfoils is essential. In addition, the analysis of the velocity and pressure fields is needed to support the physical mechanism of the force coefficients of the airfoil. Thus, the present study aims at establishing two different deep learning models to predict force coefficients and flow fields of the airfoil. One is the convolutional neural network (CNN) model to predict drag and lift coefficients of airfoil. Another is the Encoder-Decoder (ED) model to predict pressure distribution and velocity vector field. The images of airfoil section are applied as the input data of both models. Thus, the computational fluid dynamics (CFD) is adopted to form the dataset to training and test of both CNN models. The models are established by the convergence performance for the various hyperparameters. The prediction capability of the established CNN model and ED model is evaluated for the various NACA sections by comparing the true results obtained by the CFD, resulting in the high accurate prediction. It is noted that the predicted results near the leading edge, where the velocity has sharp gradient, reveal relatively lower accuracies. Therefore, the more and high resolved dataset are required to improve the highly nonlinear flow fields.

In the fan module of the intercooling refrigerator, a drain hole structure was designed for stable drainage of defrost water. However, the airflow passing through the drain hole can disturb flow features around the evaporator. Since this backflow leads to an increase in flow loss, the accurate experimental and numerical analyses are important to understand the flow characteristics around the fan module. Considering the complex geometry around the fan module, three different turbulence models (Standard k-ε model, SST k-ω model, Reynolds stress model) were used in computational fluid dynamics (CFD) analysis. According to the quantitative and qualitative comparison results, the Standard k-ε model was most suitable for the research object. High-accuracy results well match with the experiment result and overcome the limitation of the experiment setup. The method used in this study can be applied to a similar research object with an orifice outflow driven by a rotating blade.

The optimized design of airfoils is essential to increase the performance and efficiency of wind turbines. The aerodynamic characteristics of airfoils near the stall show large deviation from experiments and numerical simulations. Hence, it is needed to perform repetitive analysis of various shapes near the stall. To overcome this, the artificial intelligence is used and combined with numerical simulations. In this study, three types of airfoils are chosen, which are S809, S822 and SD7062 used in wind turbines. A convolutional neural network model is proposed in the combination of VGG16 and U-Net. Learning data are constructed by extracting pressure fields and aerodynamic characteristics through numerical analysis of 2D shape. Based on these data, the pressure field and lift coefficient of untrained airfoils are predicted. As a result, even in untrained airfoils, the pressure field is accurately predicted with an error of within 0.04%.

The present study has investigated the performance of hydrogen gas generators for inhalation purposes based on polyelectrolyte membrane water electrolysis (PEMWE). The system applied two watering methods. One is pumped water (pumping system) and the other is gravity-fed water without a pump (non-pumping system). The cell efficiencies were compared by measuring the cell voltage and temperature in the hydrogen gas generator, respectively. The results show that the cell voltage and temperature increase with the cell current. The cell temperature is lower in the pumping system than that in the non-pumping system at a given cell current. Even though the amount of hydrogen production is the same regardless of the pumping system, the cell efficiency of the hydrogen gas generator in the non-pumping system is better than that in the pumping system.

Cavitation occurs inevitably in marine propellers rotating at high speed in the water, which is a major cause of underwater radiated noise. Cavitation-induced noise from propellers rotating at a specific frequency not only reduces the sonar detection capability, but also exposes the ship's location, and it causes very fatal consequences for the survivability of the navy vessels. Therefore cavity inception speed (CIS) is one of the important factors determining the special performance of the ship. In this study, we present a method using computer vision that can detect and quantitatively estimate tip vortex cavitation on a propeller rotating at high speed. Based on the model test results performed in a large cavitation tunnel, the effectiveness of this method was verified.

Later flow immunoassay (LFIA) is a protein analytical method based on immunoreaction. On the LFIA based protein analytical method, bioreceptor molecule plays a key role, and so a system that evaluates and manages the binding affinity of bioreceptor is needed to secure detection reliability. In this study, Lateral Flow Immunoassay based rapid Bioreceptor Screening Method (rBSM) is presented that provide a simple and quick evaluating method for the binding affinity to the target protein of the antibody as model bioreceptor. To verify this evaluation method, Virus-like particles (VLP) and anti-VLP antibodies are selected as a model norovirus, which is target protein, and the candidate bioreceptors respectively. Among the 5 different candidate antibodies, appropriate antibody could be sorted out within 30 minutes through rBSM. In addition, selected antibodies were applied to two representative LFIA based techniques, sandwich assay and competitive assay. Among these methods, sandwich assay showed more effective VLP detection method. Through applying selected antibodies and techniques to the commercialized mass production lines, an VLP detecting LFIA kit was developed with a detection limit of 10¹² copies/g of VLPs in real samples. Since this proposed method in this study could be easily transformable into other combinations with bioreceptors, it is expected that this technique would be applied to LFIA kit development system and bioreceptor quality management.