Stages That Lead to Drop Depinning and Onset of Motion.

In this paper, we consider drops that are subjected to a gradually increasing lateral force and follow the stages of the motion of the drops. We show that the first time a drop slides as a whole is when the receding edge of the drop is pulled by the advancing edge (the advancing edge drags the receding edge). The generality of this phenomenon includes sessile and pendant drops and spans over various chemically and topographically different cases. Because this observation is true for both pendant and sessile cases, we exclude hydrostatic pressure as its reason. Instead, we explain it in terms of the wetting adaptation and interfacial modulus, that is, the difference in the energies of the solid interface at the advancing and receding edges. At the receding edge, a slight motion exposes to the air a recently wetted solid surface whose molecules had reoriented to the liquid and will take time to reorient back to the air. This results in a high surface energy at the solid-air interface which pulls on the triple line, that is, inhibits the motion of the receding edge. On the other hand, at the advancing edge, a slight advancement does not change the nature of the solid interfacial molecules outside the drop, and the advancing side's sliding can continue. Moreover, the solid molecules under the drop at the advancing edge take time to reorient, and hence, their configuration is not yet adapted for the liquid and therefore not adapted for retention of the advancing edge. Therefore, in sliding-drop experiments, the advancing edge moves before the receding one, typically a few times before the receding edge moves. For the same reason, the last motion of the receding edge usually happens as a result of the advancing edge pulling on it.

Monte Carlo optimization for site selection of new chemical plants.

Geographic distribution of chemical manufacturing sites has significant impact on the business sustainability of industrial development and regional environmental sustainability as well. The common site selection rules have included the evaluation of the air quality impact of a newly constructed chemical manufacturing site to surrounding communities. In order to achieve this target, the simultaneous consideration should cover the regional background air-quality information, the emissions of new manufacturing site, and statistical pattern of local meteorological conditions. According to the above information, the risk assessment can be conducted for the potential air-quality impacts from candidate locations of a new chemical manufacturing site, and thus the optimization of the final site selection can be achieved by minimizing its air-quality impacts. This paper has provided a systematic methodology for the above purpose. There are total two stages of modeling and optimization work: i) Monte Carlo simulation for the purpose to identify background pollutant concentration based on currently existing emission sources and regional statistical meteorological conditions; and ii) multi-objective (simultaneous minimization of both peak pollutant concentration and standard deviation of pollutant concentration spatial distribution at air-quality concern regions) Monte Carlo optimization for optimal location selection of new chemical manufacturing sites according to their design data of potential emission. This study can be helpful to both determination of the potential air-quality impact for geographic distribution of multiple chemical plants with respect to regional statistical meteorological conditions, and the identification of an optimal site for each new chemical manufacturing site with the minimal environment impact to surrounding communities. The efficacy of the developed methodology has been demonstrated through the case studies.

Model-based detector and extraction of weak signal frequencies from chaotic data.

Detecting a weak signal from chaotic time series is of general interest in science and engineering. In this work we introduce and investigate a signal detection algorithm for which chaos theory, nonlinear dynamical reconstruction techniques, neural networks, and time-frequency analysis are put together in a synergistic manner. By applying the scheme to numerical simulation and different experimental measurement data sets (Henon map, chaotic circuit, and NH(3) laser data sets), we demonstrate that weak signals hidden beneath the noise floor can be detected by using a model-based detector. Particularly, the signal frequencies can be extracted accurately in the time-frequency space. By comparing the model-based method with the standard denoising wavelet technique as well as supervised principal components analysis detector, we further show that the nonlinear dynamics and neural network-based approach performs better in extracting frequencies of weak signals hidden in chaotic time series.