Research On The Mechanism And Regulation Of Coalescence-induced Jumping Of Droplet

The unique droplet motion form of coalescence-induced jumping on superhydrophobic surfaces makes it better than traditional methods for chip hot spot cooling,enhanced condensation,anti-icing/frost,and self-cleaning.However,due to the unclear jumping mechanism,weak jumping,and uncontrollable direction,its application is greatly limited.In this paper,we combine experiments,theories,numerical simulations,and machine learning to study the mechanism of coalescence induced jumping,aiming to clarify the mechanism of jumping and develop methods to enhance jumping and control the jumping direction,so as to provide theoretical guidance and technical support for wider and more efficient application of droplet jumping.The main work and results of this paper are as follows.The influence of the initial state of droplets on multi-droplet jumping is clarified,jumping potential model is established,and the jumping potential,which can simultaneously characterize the influence of droplet number,distribution,and radius ratio on the jumping ability,is defined.The jumping potential model was used to explain the phenomena that could not be explained in previous studies and to determine the critical conditions for the multi-droplet jumping.The mechanism of multi-hop jumping induced by the coalescence of vertical returning droplets and stationary droplets on the surface is revealed,the morphological evolution and momentum transfer process of multi-hop jumping are analyzed,and the relationship between multi-hop jumping velocity and intrinsic multi-hop jumping velocity and returning droplet velocity is established according to the contact position and the size of returning droplets.The mechanism of controlling the jumping direction is studied,and the surface energy gradient and surface structure are used to achieve the regulation of the jumping direction.The relationship between droplet jumping direction and surface structure angle,and initial distribution of droplets is established based on force analysis and convolutional neuron network.The mechanism of enhanced jumping is clarified,and the method to improve the energy conversion rate by reducing the viscous dissipation and enhancing the interaction between the liquid bridge and the structure to increase the orderliness of the droplet internal flow is proposed.Based on the experimental data and mechanistic analysis,machine learning is used to design an ultimate jumping structure that allows droplets to jump with an energy conversion rate close to the theoretical limit.Finally,based on the above study,a superhydrophobic surface with a serrated array is designed to realize the directional jumping of large-scale droplets.The droplet transport speed and relative transport distance of the directional jumping are two orders of magnitude higher than those of the traditional method,and the ability of the surface to remove droplets can be significantly improved.Enhanced jumping with a high energy conversion rate can enable other low surface energy fluid such as R22,R134 a,Gasoline,and Ethanol,which cannot jump on a flat surface,to jump,and reduce the surface hydrophobicity requirement for jumping to a certain extent.