We performed numerical and experimental studies on the viscous folding in diverging microchannel flows which were recently reported by Cubaud and Mason (Phys Rev Lett 96:114501, 2006a). We categorized the flow patterns as ''stable'', ''folding,'' and ''chaotic'' depending on channel shape, flow ratio, and viscosity ratio between two fluids. We focused on the effect of kinematic history on viscous folding, in particular, by changing the shape of diverging channels: 90°, 45°, and hyperbolic channel. In experiments, the proposed power-law relation (f $ _ c 1 ; where f is the folding frequency, and _ c is the characteristic shear rate) by Cubaud and Mason (Phys Rev Lett 96:114501, 2006a) was found to be valid even for hyperbolic channel. The hyperbolic channel generated moderate flows with smaller folding frequency, amplitude, and a delay of onset of the folding compared with other two cases, which is considered to be affected by compressive stress when compared to the simulation results. In each channel, the folding frequency increases and the amplitude decreases as the thread width decreases since higher compressive stress is applied along the thin thread. The secondary folding was also reproduced in the simulation, which was attributed to locally heterogeneous development of compressive stresses along the thread. This study proves that the viscous folding can be controlled by the design of flow kinematics and of the compressive stresses at the diverging region.

Vehicle integrated thermal management system (VTMS) is an important technology used for improving the energy efficiency of vehicles. Physics-based modeling is widely used to predict the energy flow in such systems. However, physics-based modeling requires several experimental approaches to get the required parameters. The experimental approach to obtain these parameters is expensive and requires great effort to configure a separate experimental device and conduct the experiment. Therefore, in this study, a neural network (NN) approach is applied to reduce the cost and effort necessary to develop a VTMS. The physics-based modeling is also analyzed and compared with recent NN techniques, such as ConvLSTM and temporal convolutional network (TCN), to confirm the feasibility of the NN approach at EPA Federal Test Procedure (FTP-75), Highway Fuel Economy Test cycle (HWFET), Worldwide harmonized Light duty driving Test Cycle (WLTC) and actual on-road driving conditions. TCN performed the best among the tested models and was easier to build than physics-based modeling. For validating the two different approaches, the physical properties of a 1 L class passenger car with an electric control valve are measured. The NN model proved to be effective in predicting the characteristics of a vehicle cooling system. The proposed method will reduce research costs in the field of predictive control and VTMS design.

: This study is a follow-up study of "Development of Plasma Ignition System" was presented at the 2013 KSAE spring conference. This study compares lean limit of conventional ignition system with plasma ignition system on constant volume combustion test & Engine Combustion test.

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A high energy ignition system is essential for lean burn or high EGR gasoline engine, which is getting more and more interest to improve fuel economy. The high energy ignition systems comprise plasma jet, laser beam, corona discharge and so on. In this study, a high energy ignition system using corona discharge is developed and tested in a constant volume combustion chamber. The developed system shows extension of lean limit of propane-air mixture and enhencement of combustion speed. Various shape of corona discharge plugs are also tested and compared in this study.