Simplified effectiveness estimation model for a wet coil indirect evaporative cooler based on the effectiveness NTU model

Abstract

The main purpose of this research is to establish simplified effectiveness estimation models for wet coil Indirect Evaporative Coolers (wet coil IEC), experimental data from a pilot unit was obtained under various operation conditions in an environmental chamber configured with a wide-range of indoor and outdoor air conditions. The effectiveness Number of Transfer Unit (NTU) model was employed to describe the coupled heat and mass transfer. The operation of the wet coil IEC was configured to work in both wet and dry conditions. By statistically analyzing cooling performance data generated by the NTU model, the impact of various design parameters and their combinations on the wet coil IEC’s cooling capacity was estimated using the 2k factorial experimental design method. And then a linear regression equation was derived as a function of the major design parameters and interactions that have significant effects on the cooling capacity of the wet coil IEC. The simplified regression models were derived in such a way to form a first-order linear equation and returns effectiveness of the wet coil IEC under wet and dry operation conditions. To verify the derived regression models, a pilot unit test was conducted. The test results showed that the pilot unit had over 75% effectiveness under hot and humid outdoor air conditions. One can see that this wet coil IEC unit can effectively reduce the cooling coil size by pre-conditioning the outdoor air in wet conditions. The pilot system also had over 65% of effectiveness in the winter season. One can be verified that the wet coil IEC also reduces the conventional heating coil size by efficient heat recovery from the waste heat of indoor air. Finally, the derived models and experimental data were compared to validate the expected models. One can see that the predicted effectiveness of the wet coil IEC under wet and dry operation is in good agreement with the experimental data.