Abstract
To understand the effect of ZnO nanostructures on various metal surfaces, previous experiments have tested the evaporation performance of ZnO coated surfaces through single droplet deposition and pool boiling experiments. Results indicate that applying ZnO superhydrophilic surfaces on metal substrates enhances surface wettability and single droplet vaporization. These superhydrophilic surfaces have the potential to be easily scaled up to larger and more complex heat exchangers and enhance spray cooling processes. Consequently, this study explores the direct evaporative cooling of ZnO coated and non-coated copper wavy fin heat exchangers. The experimental apparatus consists of an airflow and water flow system. The airflow system consists of a variable speed fan and an electric heater that preheats air before it flows into the wavy fins and the water flow system consists of a low volumetric water flow nozzle that sprays droplets into the wavy fins. Experimental results and machine learning tools are used in tandem to characterize the efficiency of the heat exchangers and to extract fundamental heat transfer performance parameters such as the average heat transfer coefficient. Specifically, a genetic algorithm is used to determine how the Colburn heat transfer factor varies with Reynolds number, and how the wetted interfacial area varies with flow conditions. Thus, in addition to providing greater insight into the performance of the heat exchanger and its dependence on system parameters, a genetic algorithm helps attain an optimal design consisting of ZnO coated surfaces that will greatly enhance spray cooling systems.
