This paper presents a microscale benefit of a secondary flow obtained in a curved rectangular microchannel, which is generally unfavorable and negligible in conventional fluid flow. We have demonstrated the separation and sorting of micro beads by their size using secondary flow. The physical mechanism occurring in the size-selective separation was explained based on the numerical analysis of the characteristic velocity distribution on the cross-sectional plane normal to the main flow stream. The dynamic trajectories of micro beads of different sizes and materials are visualized and compared for the experimental demonstration. We also discuss the effects of both the shape uniformity of the micro beads and the inlet condition on the size-selective separation.

In this article, the development of a novel technique to fabricate spherical polymeric microcapsules by utilizing microfluidic technology is presented. Atom transfer radical polymerization (ATRP) was employed to synthesize well-defined amphiphilic block copolymers. An organic polymer solution was constrained to adopt the spherical droplets in a continuous water phase at a T-junction microchannel, and the generation of the droplets was studied quantitatively. The flow conditions of two immiscible solutions were adjusted for the successful generation of the polymer droplets. The morphology of the microcapsules was examined. The efficiency of these polymer microcapsules as containers for the storage and controlled release of loaded molecules was evaluated by encapsulating the microcapsules with Congo-red dye and investigating the release performance using temperature controlled UV-VIS spectroscopy.

Pressure drop (Δp) and collection efficiency (η) are used to evaluate the separation performance of the cyclone separator. In this study, we conducted comparative study of cyclone models using response surface methodology (RSM), back propagation neural network (BPNN), and group method of data handling (GMDH) networks to develop optimal predictive cyclone models. Also, we conducted multi-objective optimization for maximizing model and minimizing model using genetic algorithm (GA). CFD was performed instead of experimental method to get the estimated values for modeling of Δp and η. The validation results of CFD showed 0.5% and 2% errors for Δp and η, respectively, compared with the experimental data. Second, design of experiment (DOE) analysis for 10 cyclone geometrical parameters was executed to obtain the significant geometrical parameters. Vortex finder diameter D_x, inlet width a, inlet height b and cone height H_co have a significant effect on η and Δp. However, interaction effects between the geometrical parameters have small effects. The cyclone models by RSM, BPNN and GMDH based on 25 CFD training set were developed. The predictive performance results by the cyclone models were compared by 25 CFD test set. The GMDH method achieved the best prediction for Δp ( $R^2=99.7$, RMSE = 0.102) $R_{\text{adjusted}}^2=98.99$, RMSE = 0.0119) than the RSM, BPNN cyclone models. Additionally, uncertainty analysis was performed to estimate the quantitative performance of cyclone models. The results show that the uncertainty width of GMDH models achieved the best prediction ( $\eta$: ±0.0065, Δp: ±0.0188). Finally, GA was applied to optimize the GMDH models simultaneously. GA generated 70 non-dominant solutions. Reproducibility of five optimal points was validated by using CFD. The trade-off optimal point showed improvement by 24.31%, 18% and 8.79% for Δp ${\text{d}}_{50}$ and $\eta$, respectively.

In this paper, the characteristics of the cyclone separator was analyzed from the Lagrangian perspective for designing the important dependent variables. The neural network network model was developed for predicting the separation performance parameter. Further, the predictive performances were compared between the traditional surrogate model and the developed neural network model. In order to design the important parameters of the cyclone separator based on the particle separation theory, the force acting until the particles are separated was calculated using the Lagrangian-based computational fluid dynamics (CFD) methodology. As a result, it was proved that the centrifugal force and drag acting on the critical diameter having a separation efficiency of 50% were similar, and the particle separation phenomenon in the cyclone occurred from the critical diameter, and it was set as an important dependent variable. For developing a critical diameter prediction model based on machine learning and multiple regression methods, unsteady-Reynolds averaged Navier-Stokes analyzes according to shape dimensions were performed. The input design variables for predicting the critical diameter were selected as four geometry parameters that affect the turbulent flow inside the cyclone. As a result of comparing the model prediction performances, the machine learning (ML) model, which takes into account the critical diameter and the nonlinear relationship of cyclone design variables, showed a 32.5% improvement in R-square compared to multi linear regression (MLR). The proposed techniques have proven to be fast and practical tools for cyclone design.

This paper presents the development of the capacitive sensor to measure the growth of ice on a fuel pipe surface in real time. The ice sensor consists of pairs of electrodes to detect the change in capacitance and a thermocouple temperature sensor to examine the ice formation situation. In addition, an environmental chamber was specially designed to control the humidity and temperature to simulate the ice formation conditions. From the humidity, a water film is formed on the ice sensor, which results in an increase in capacitance. Ice nucleation occurs, followed by the rapid formation of frost ice that decreases the capacitance suddenly. The capacitance is saturated. The developed ice sensor explains the ice growth providing information about the icing temperature in real time.

The thickness distribution of thermoformed products is greatly affected by the viscoelastic behavior of the extruded polymer sheet. In this work, linear and nonlinear rheological experiments are carried out to characterize the viscoelastic properties of acrylonitrile-butadiene-styrene sheets under thermoforming conditions including a wide range of temperatures, strains, and strain rates. First, aspects of linear viscoelasticity such as the storage modulus and loss modulus are measured by small-amplitude oscillatory shear experiments. The discrete relaxation spectra and the Williams-Landel-Ferry parameters are obtained from the constructed linear master curves. Then, nonlinear time-dependent extensional viscosity is measured by uniaxial extensional experiments. The parameters of the damping function are evaluated using an optimization method. In addition, the effect of the orientation of the polymer is analyzed. The uniaxial extensional stress and viscosity in the extruder direction demonstrate higher resistance against tearing and extreme thickness reduction during processing. Finally, the linear and nonlinear input parameters for the numerical simulation are prepared. Numerical simulations are performed using the Wagner model with the obtained nonlinear viscoelasticity. The thickness distribution in thermoformed ABS sheets, obtained numerically, shows good agreement with the experimentally obtained values.