An Artificial Neural Network-Based Model for Predicting Heat Transfer Coefficient in Heating-Cooling Systems with Chemically Synthesized Nanofluid

The purpose of this researsh is to investigate the efficiency of simultaneous use of /water hybrid nanofluid (NF) in concentrations of ۰.۱ to ۰.۵% by volume and using blade turbulators with a ۴۵-degree angle with different geometric shapes in the Reynolds number range of ۳۷۴۶ to ۱۰۰۴۱ in Convective Heat Transfer Coefficient (CHTC) and Nusselt number in the Double Pipe Heat Exchanger (DPHE). SEM photography methods were used to identify and determine the size of synthesized NanoParticles (NPs). Heat transfer (HT) experiments were designed using the Response Surface Methodology (RSM). Additionally, an Artificial Neural Network (ANN) with the Levenberg-Marquardt (LM) algorithm was utilized for data prediction. The most optimal increase in the hybrid NF with a concentration of ۰.۵% by volume with a Reynolds number of ۱۰۰۴۱ and a pitch distance of ۵ mm with two-sided blades is ۵۷ and ۵۵% for the CHTC and the Nusselt number, respectively. The highest and lowest Thermal Performance Factor (TPF) for NF with concentration ۰.۵ and Reynolds numbers ۳۷۴۶ and ۱۰۰۴۱ are equal to ۰.۹۰۸۵ and ۴.۰۴۵۴. The obtained values for the Margin of Deviation (MOD), correlation coefficient (R۲), and Mean Squared Error (MSE) using RSM and an ANN with a ۴-۲۲-۱ topology are as follows: ±۱۰ and ±۰.۰۱%, ۰.۹۹۳۸, ۱, ۰.۱, and ۰.۰۰۰۰۰۰۴۴۱, respectively.

کلیدواژه ها:

Hybrid NF ، Artificial neural network ، Nusselt number ، thermal performance factor