Research On Principle And Application Of Solid-Liquid Two-Phase Fluid Coupling Electrification

Considerable energy can be collected in the natural environment,such as the mechanical energy of raindrops,the blue energy contained in the sea water,etc.Thus,the collection and utilization of these energy is conducive to environmental protection and resource conservation.Inspried by the phenomenon of friction/contact electrification,where the electric charges would transfer during the friction process of neighboring materials,the triboelectricity of the solid-liquid interface can be used to collect and utilize water energy in nature.However,in nature and industry,solid particles are easily mixed in liquids,for example,rainwater and river water are mixed with sand to form a solid-liquid two-phase fluid.Therefore,this thesis studies the characteristics of solid-liquid interface electrification and solid-liquid two-phase fluid coupling electrification,which provides a theoretical and experimental reference for improving the efficiency of water energy collection and expanding the application of frictional electrification between solid and liquid.As for the tribological nanogenerator characteristics for solid-liquid interface,a liquid triboelectric nanopower test platform was built to explore the relationship between droplet electrification characteristics and droplet motion.The relative factors of droplet size,hydrophilic and hydrophobic characteristics of solid surface,droplet type and solid surface pre-charging are investigated.In addition,the finite element simulation is used to explore the droplet-induced electrification characteristics of the solid-liquid interface from a theoretical perspective.The results confirm that when a droplet falls on a solid surface,an output of positive-negative-negative current pulses would be generated along with the droplet deformation of spreading-rebound-slip-off.As the size of the droplet increases,the amplitude of the output electrical signal caused with larger droplet deformation.Also,the hydrophilic and hydrophobic properties of the solid surface will affect the droplet deformation process and thus affect the positive and negative characteristics of the droplet electrification signal.Moreover,the positive and negative characteristics of the output signal will change with the change of the contact state of the solid-liquid interface,and the size of the output signal is related to the solid material and the liquid material.The pre-charging method can improve surface charge density,thereby increasing the electrification signal.To clarify the electrification characteristics of the solid-liquid two-phase fluid,the solid-liquid two-phase fluid coupling electromechanical theory is obtained by testing the electrification signal of the mixed droplet and the pure droplet.The influence of particle quantities and sizes are further investigated,followed by the finite element simulation of the electrification process.Studies have shown that the electrification of a solid-liquid two-phase fluid includes the electrification process of the electrostatic induction between the solid particles and the electrode and the electrification process of the droplet and the solid surface contact deformation,which is the coupling electrification of the solid-solid and solid-liquid interfaces.The electrification of a solid-liquid two-phase fluid is closely related to the number and size of solid particles in the fluid,Moreover,the mass fraction and average particle size of solid particles are negatively related to the pulse amplitude generated by the contact deformation of the solid surface,and positively related to the frequency of the pulse signal generated by electrostatic inductionFor the application of solid-liquid two-phase fluid coupling for electrification,a solid particle parameter monitoring method including two modules of signal acquisition and signal recognition is designed.The signal acquisition module adopts a self-designed floating ball device to realize the acquisition of water drop signals,realized by the Convolutional Neural Networks in the deep learning algorithm.The results further show that the recognition accuracy of 12 groups of mixed droplets containing solid particles with different mass fractions reaches 96.65±0.20%,and for 6 groups with different average particle diameters,with the recognition accuracy of 99.84±0.06%.