Regression models for performance prediction of counter flow dew point evaporative cooling systems. (1st April 2019) - Pakari, Ali; Ghani, Saud
Highlights: Three regression models were developed using response surface methodology. 4 operational and 2 geometrical parameters are related to 3 output responses. The average discrepancy between the regression and numerical predictions is 1.32%. The average discrepancy between regression predictions and measurements is 5.05%. Outlet temperature is mostly affected by inlet temperature and relative humidity. Abstract: Practitioners take more interest in the output conditions of cooling systems than the details of the processes. In this study, regression models are developed that relate input parameters, including operational and geometrical parameters, to selected output responses of counter flow dew point evaporative cooling systems using numerical simulations and response surface methodology. The considered input operational parameters are inlet air temperature, inlet air relative humidity, inlet air velocity, and extraction ratio. The considered geometrical parameters are the channel length and channel width of the cooling system. The selected output responses are outlet air temperature, outlet air relative humidity, and wet-bulb effectiveness. The regression models are developed using a numerical model that is validated using experimental measurements. The predicted outlet temperatures of the counter flow dew point evaporative cooling system using the regression model match the numerical model predictions and experimental measurements within ±4% and ±10%, respectively. Therefore, the developed regression models provide a simple mean to predict the performance, aid in the design and optimization of counter flow dew point evaporative cooling systems.