Browsing by Author "Wang, C. -H."

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Controlling droplet bouncing and coalescence with surfactant
(2016-07)
Pan, K. -L.
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Tseng, Y. -H.
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Chen, J. -C.
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Huang, K. -L.
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Wang, C. -H.
The collision between aqueous drops in air typically leads to coalescence after impact. Rebounding of the droplets with similar sizes at atmospheric conditions is not generated, unless with significantly large pressure or high impact parameters exhibiting near-grazing collision. Here we demonstrate experimentally the creation of a non-coalescent regime through addition of a small amount of water-soluble surfactant. We perform a direct simulation to account for the continuum and short-range flow dynamics of the approaching interfaces, as affected by the soluble surfactant. Based on the immersed-boundary formulation, a conservative scheme is developed for solving the coupled surface-bulk convection-diffusion concentration equations, which presents excellent mass preservation in the solvent as well as conservation of total surfactant mass. We show that the Marangoni effect, caused by non-uniform distributions of surfactant on the droplet surface and surface tension, induces stresses that oppose the draining of gas in the interstitial gap, and hence prohibits merging of the interfaces. In such gas-liquid systems, the repulsion caused by the addition of surfactant, as frequently observed in liquid-liquid systems such as emulsions in the form of an electric double-layer force, was found to be too weak to dominate in the attainable range of interfacial separation distances. These results thus identify the key mechanisms governing the impact dynamics of surfactant-coated droplets in air and imply the potential of using a small amount of surfactant to manipulate impact outcomes, for example, to prevent coalescence between droplets or interfaces in gases.
Defect selective passivation in GaN epitaxial growth and its application to light emitting diodes
(2009-11-23)
Lo, M. -H.
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Tu, P. -M.
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Wang, C. -H.
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Cheng, Y. -J.
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Hung, C. -W.
A defect selective passivation method to block the propagation of threading dislocations in GaN epitaxial growth is demonstrated. The defect selective passivation is done by using defect selective chemical etching to locate defect sites, followed by silicon oxide passivation of the etched pits, and epitaxial over growth. The threading dislocation density in the regrown epilayer is significantly improved from 1x10(9) to 4x10(7) cm(-2). The defect passivated epiwafer is used to grow light emitting diode and the output power of the fabricated chip is enhanced by 45% at 20 mA compared to a reference one without using defect passivation.
Image-based fuzzy control system
(2008-03-27)
Chen, G. -M.
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Lin, P. -Z.
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Wang, W. -Y.
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Lee, T. -T.
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Wang, C. -H.
A novel image-based fuzzy control (IBFC) scheme is developed to imitate the way humans use visual information to control objects. A CCD camera gathers images of the controlled plant, and a simple algorithm analyses the images. The proposed image analysis algorithm utilises image information more intuitively than visual servo control systems. The difference between a reference image and the current image is numerically expressed and directly used by a fuzzy control system using a human-like control law. To investigate the effectiveness of the proposed IBFC scheme, it is applied to control an inverted pendulum system. Simulation results show that the IBFC system can achieve favourable tracking performance without prior knowledge of the controlled plant.
Robust fuzzy-neural sliding-mode controller design via network structure adaptation
(2008-12-01)
Lin, P. -Z.
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Hsu, C. -F.
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Lee, T. -T.
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Wang, C. -H.
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電控工程研究所
A robust fuzzy-neural sliding-mode control (RFSC) scheme for unknown nonlinear systems is proposed. The RFSC system is composed of a computation controller and a robust controller. The computation controller containing a self-structuring fuzzy-neural network (SFNN) identifier is the principle controller, and the robust controller is designed to achieve L(2) tracking performance. The SFNN identifier uses the structure- and parameter-learning phases to perform the estimation of the unknown system dynamics. The structure-learning phase consists of the growing of membership functions, the splitting of fuzzy rules and the pruning of fuzzy rules, and thus the SFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. Finally, the proposed RFSC system is applied to three nonlinear dynamic systems. The simulation results show that the proposed RFSC system can achieve favourable tracking performance by incorporating SFNN identifier, sliding-mode control and robust control techniques.