Model-based experimental analysis of enhanced boiling heat transfer by micro-nano porous surfaces

Abstract

Small bubbles are desirable to be frequently generated and detach from the heated surface in pool boiling. Bubble nucleation and bubble departure can be influenced by the size of micro-nano structure. In this work, four honeycomb micro-nano porous copper surfaces with successively increasing pore sizes are prepared by controlling the electrodeposition current densities. Both experiments on saturated pool boiling of water and numerical simulations are conducted. The heat transfer performance of prepared surfaces is improved significantly and then decreased slightly when the pore size ranges from 48 μm to 128 μm, with maximum critical heat flux and heat transfer coefficient reaching 187.3 W/cm2 and 23.7 W/cm2/K, respectively. Numerical simulations considering three-dimensional (3D) complex geometries of the four samples reconstructed by a computed tomography scanning technique are conducted. Numerical results show that the percentage of heat flowing out of the three-phase contact lines by phase change at high heat-flux supply can increase to 90%, which is consistent with previous literature results. Subject to a parametric 3D transient heat-conduction forward model developed in this work, a multi-layer end-to-end convolutional neural network is for the first time constructed and trained to solve the corresponding ill-posed inverse problems of 3D transient heat conduction efficiently. It provides a promising alternative way to develop a highly efficient temperature-to-heat-flux soft sensor technique for both boiling applications and many other similar engineering problems.