Nanofluids as secondary fluid in the refrigeration system: Experimental data, regression, ANFIS, and NN modeling. (December 2019)

• Record Type: Journal Article
• Authors: Li, Z.X.; Renault, Felipe Lemos; Gómez, Abdul Orlando Cárdenas; Sarafraz, M.M.; Khan, H.; Safaei, Mohammad Reza; Filho, Enio Pedone Bandarra
• Is Part Of: International journal of heat and mass transfer. Volume 144(2019)
• Publication Date: 2019-12
• Subjects: Nanofluid -- Thermophysical properties -- Heat exchangers -- Entropy generation -- ANFIS -- ANN -- Regression -- Experimental measurement; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2019.118635
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: Thermophysical properties of different nanofluids were measured. New correlations were extracted using regression, NN and ANFIS for nanofluids. Numerical simulation was performed to optimize a double-pipe heat exchanger. Some nanofluids present good results as working fluids in refrigeration systems. Abstract: This work is concerned with a comparison of measured and theoretical thermophysical properties of different nanofluids and using the properties to evaluate their suitability as a secondary working fluid in refrigeration systems, aiming at the external thermodynamic losses. Single-Walled Carbon Nanotubes (SWCNT) and silver nanoparticles were dispersed in distilled water to produce the nanofluid samples. After that and by using the regression, a neural network (NN), an adaptive neuro-fuzzy inference system (ANFIS) and through obtained experimental data, new correlations were extracted to determine the thermophysical properties of nanofluids. Then, a numerical simulation was performed taking into account a mathematical model for thermodynamic optimization of a double-pipe heat exchanger. A comparison between the results obtained by the measured data or those which is obtained by soft-computing techniques and those evaluated theoretically by previous available models were carried out, showing a notable difference among the results. The results obtained with the experimental or soft-computing data show that in the case that the heat exchanger is optimized to minimize the external entropy generation, silver nanofluids (at any volumetric concentration) and the SWCNT nanofluids (at lower concentrations) presented positive results, whereas using the former existing theoretical data indicated that silver nanofluids showed negative results and SWCNT nanofluids presented positive results in all volumetric concentrations.