Statistical modeling and multi-objective optimization of a flat tubular indirect evaporative cooler for enhanced performance

Abstract

The flat tubular indirect evaporative cooler complements the conventional round tubular indirect evaporative cooler owing to its superior wetting surface area and enhanced heat transfer capacity. Given the multitude of influencing parameters, achieving a comprehensive analysis and optimization of the flat tubular indirect evaporative cooler demands a significant number of experimental tests or numerical simulations. Consequently, this study seeks to employ statistical techniques for constructing a precise and expeditious performance prediction model for the flat tubular indirect evaporative cooler. Nine pivotal parameters were selected to evaluate their impact on the cooling performance of the flat tubular indirect evaporative cooler. A numerical model was developed and verified experimentally. The response surface method was applied to establish polynomial regression equations for the cooling performance prediction of flat tubular indirect evaporative coolers. In addition, this work proposed a multi-objective optimization strategy for the proposed cooler. By innovatively using weighting factors to assign different weights to response values, multi-objective optimization with preferences was performed for the flat tubular indirect evaporative cooler with different demands to increase the applicability of the proposed cooler. The optimization results showed that when only the optimized wet-bulb efficiency was considered, the wet-bulb efficiency can reach up to 81.1%. When optimizing both the wet-bulb efficiency and the coefficient of performance simultaneously, the coefficient of performance can be up to 58.0, and the wet-bulb efficiency was 64.7%.