Optimization of Liquid Film Parameters of Indirect Evaporative Cooling System Based on Machine Vision

The wet channel plate surface of indirect evaporative cooler (IEC) can not be completely wet in the application of the actual working conditions，and the plate surface parameters of liquid film wetting rate and liquid film thickness are difficult to be observed, and the lack of relevant experimental research and data support. Therefore, in this paper, machine vision technology will be used to experimentally investigate the wetting rate and liquid film thickness of the secondary side channel of the IEC, and the UDF interface program and the parameters of the original simulation model will be improved. The results show that the average and maximum errors of the first and second outlet temperatures of the corrected analytical model are reduced compared with the original model, and the average error is within 5% under different spray flow rates.