Enhancing performance of polymer-based microchannel heat exchanger with nanofluid: A computational fluid dynamics-artificial neural network approach

Abstract

Polymer-based heat exchangers can offer a promising solution for environmental sustainability due to their low energy consumption. The incorporation of microchannels and nanofluids further enhances the heat transfer performance of these heat exchangersIn this study, a polymer-based microchannel heat exchanger combined with nanofluid is simulated through the integration of an artificial neural network predictive model and a three-dimensional computational fluid dynamics model. This study unveils an advanced calculation that integrates artificial intelligence and readily-available computational software provided as the advanced calculation system. A statistical mathematics response surface method which data is used for correlating the calculation model is applied to obtain the design parameters between operating conditions and for optimal performance. The optimized results reveal that polymer-based microchannel heat exchanger combined with nanofluid is a promising innovation. The heat transfer improvement achieved a 12 % increase in the overall heat transfer coefficient by using TiO2/Water compared to Water. Moreover, a 1.03 performance index is obtained when CuO/Water nanofluid is used, a 66 horizontal parallel connecting of the polymer-based microchannel heat exchanger shows that the equipment can afford the same heat transfer performance of the metal-based microchannel heat exchanger in TiO2/Water nanofluid usage and implying a balance between heat transfer enhancement and energy consumption.

Keywords

• Polymer-based microchannel
• Heat exchanger
• Performance optimization
• Nanofluid
• Computational fluid dynamics
• Artificial neural network