期刊
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
卷 -, 期 -, 页码 -出版社
SPRINGER
DOI: 10.1007/s10973-022-11822-6
关键词
Artificial neural network; Decision-making approach; Dynamic viscosity; Hybrid nanofluids; Multi-objective optimization; Thermal conductivity
This study proposes a methodology for assessing the utility of developing a new hybrid nanofluid based on its thermophysical properties. The results show that producing the new Fe-Si/water hybrid nanofluid is not recommended.
Studies on nanofluids have demonstrated their superior heat transfer capability. To enhance performance, the nanoparticle concentration in mono nanofluids can be increased. However, this is constrained by the increase in viscosity. To overcome this limitation, scientists have developed hybrid nanofluids. Consequently, it becomes crucial to evaluate the utility of developing a new hybrid nanofluid. This study proposes a methodology for assessing the utility of developing a new hybrid nanofluid based on its thermophysical properties. The methodology consists of three essential steps. In the first step, ANN models should be developed to predict the thermal conductivity and dynamic viscosity of nanofluids. In the second step, the process of optimization must be lanced in order to maximize the thermal conductivity and minimize the dynamic viscosity of nanofluids. In the third step, decision-making methods should be used to determine the final optimal operation variables, including nanofluid type, temperature, and nanoparticles' mass concentrations. The utility of developing the Fe-Si/water hybrid nanofluid by optimizing its thermophysical properties to those of the FeC/water and SiC/water nanofluids was evaluated by using the new methodology. The results show that the thermophysical properties of the FeC/water nanofluid are more attractive, so producing the new Fe-Si/water hybrid nanofluid is not recommended.
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