Based on lattice Boltzmann method(LBM), the process of droplets merging and bouncing at the top of inclined plane with wetting gradient was simulated. The effects of wetting gradient, wettability at the top of inclined plane, droplet size and Bond number on droplet merging and bouncing process were investigated. The calculation results show that the two droplets can bounce when merged under the action of non-equilibrium tension on a hydrophobic gradient slope. Both the droplet fusion velocity and the maximum jump height increase with the increase of wetting gradient. The maximum droplet jump height decreases with the increase of the wettability of the inclined surface. There is an optimal droplet size that optimizes the maximum jump height. The larger the Bond number, the smaller the maximum droplet jump height.

In this study, a mixed flow closed cooling tower was tested with a control variable method. The factors affecting outlet water temperature were screened with a grey correlation analysis method. Five most important factors were taken as input features in a gray_BP neural network which was developed to predict outlet water temperature of the mixed flow closed cooling tower. These factors include inlet water temperature, wet bulb temperature, water refill temperature, circulating water flow rate and air volume, and the prediction output is outlet water temperature. The network adopts a three-layer structure, four-hidden layer neurons, and 30 000 iterations. Experimental data that involve no training set were used to validate the developed model. It shows that the gray neural network model outperforms the traditional BP neural network model. The correlation coefficient, average relative error, root mean square error are 0.998 9, 0.293 4% and 0.152 9, respectively. We concluded that the gray_BP neural network is a promising algorithm for predicting water temperature of a mixed flow closed cooling tower.

A lattice Boltzmann method based on Shan-Chen pseudo-potential model was used to simulate the process of a droplet overcoming gravity and moving upward on a inclined plane with wetting gradient. Effects of wetting gradient, droplet size, Bond number and surface inclination angle on droplet motion were investigated. It shows that velocity vector appears in the droplet moving upwards along the slope. The greater the wetting gradient is, the faster the droplet moves, and the longer the wetting length is, and the faster the dynamic contact angle decreases. Droplet size and Bond number have weak influence on droplet motion, but there is a critical Bond number, beyond which the droplet moves down along the gradient wetting surface. The surface inclination angle has a significant effect on the droplet motion. As the inclination angle increases, the droplet motion speed and wetting length are significantly reduced.

A single-component multi-phase pseudo-potential lattice Boltzmann method is used to simulate the process of a large droplet vertically hitting a stationary small droplet on a wall with different wettability. The size ratio of the droplets is 1.5. Hydrophilic and superhydrophobic wall surfaces are studied. The droplet spreading factor and relative height are obtained. It shows that as We number increases the spreading factor of the droplet increases, the spreading diameter increases and the relative height reduces. And as We number increases, on a superhydrophobic surface, a cavity appears at the bottom of the spreading process and size of the cavity increases. In addition, as We number increases to 107.35 the droplet breaks.