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Surface wettability and contact angle analysis by dissipative particle dynamics
Lin, Tzung-Han,Shih, Wen-Pin,Chen, Chuin-Shan Techno-Press 2012 Interaction and multiscale mechanics Vol.5 No.4
A dissipative particle dynamics (DPD) simulation was presented to analyze surface wettability and contact angles of a droplet on a solid platform. The many-body DPD, capable of modeling vapor-liquid coexistence, was used to resolve the vapor-liquid interface of a droplet. We found a constant density inside a droplet with a transition along the droplet boundary where the density decreased rapidly. The contact angle of a droplet was extracted from the isosurfaces of the density generated by the marching cube and a spline interpolation of 2D cutting planes of the isosurfaces. A wide range of contact angles from $55^{\circ}$ to $165^{\circ}$ predicted by the normalized parameter ($|A_{SL}|/B_{SL}$) were reported. Droplet with the parameters $|A_{SL}|>5.84B{_{SL}}^{0.297}$ was found to be hydrophilic. If $|A_{SL}|$ was much smaller than $5.84B{_{SL}}^{0.297}$, the droplet was found to be superhydrophobic.
Modeling and assessment of VWNN for signal processing of structural systems
Jeng-Wen Lin,Tzung-Han Wu 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.1
This study aimed to develop a model to accurately predict the acceleration of structural systems during an earthquake. The acceleration and applied force of a structure were measured at current time step and the velocity and displacement were estimated through linear integration. These data were used as input to predict the structural acceleration at next time step. The computation tool used was the Volterra/Wiener neural network (VWNN) which contained the mathematical model to predict the acceleration. For alleviating problems of relatively large-dimensional and nonlinear systems, the VWNN model was utilized as the signal processing tool, including the Taylor series components in the input nodes of the neural network. The number of the intermediate layer nodes in the neural network model, containing the training and simulation stage, was evaluated and optimized. Discussions on the influences of the gradient descent with adaptive learning rate algorithm and the Levenberg-Marquardt algorithm, both for determining the network weights, on prediction errors were provided. During the simulation stage, different earthquake excitations were tested with the optimized settings acquired from the training stage to find out which of the algorithms would result in the smallest error, to determine a proper simulation model.
Modeling and assessment of VWNN for signal processing of structural systems
Lin, Jeng-Wen,Wu, Tzung-Han Techno-Press 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.1
This study aimed to develop a model to accurately predict the acceleration of structural systems during an earthquake. The acceleration and applied force of a structure were measured at current time step and the velocity and displacement were estimated through linear integration. These data were used as input to predict the structural acceleration at next time step. The computation tool used was the Volterra/Wiener neural network (VWNN) which contained the mathematical model to predict the acceleration. For alleviating problems of relatively large-dimensional and nonlinear systems, the VWNN model was utilized as the signal processing tool, including the Taylor series components in the input nodes of the neural network. The number of the intermediate layer nodes in the neural network model, containing the training and simulation stage, was evaluated and optimized. Discussions on the influences of the gradient descent with adaptive learning rate algorithm and the Levenberg-Marquardt algorithm, both for determining the network weights, on prediction errors were provided. During the simulation stage, different earthquake excitations were tested with the optimized settings acquired from the training stage to find out which of the algorithms would result in the smallest error, to determine a proper simulation model.