Different nanofluids as coolant in heat exchanger network: Thermoeconomic modeling and multi-objective optimization

Modeling and optimization of a multi tube heat exchanger (MTHE) network considering the effects of different nanoparticles on the tube side are carried out using Fast and elitist non-dominated sorting genetic algorithm. After thermal modeling in epsilon - NTU method, optimization is performed by increasing the effectiveness and decreasing total annual cost as two objective functions using eight design parameters such as number of MTHE and particles volumetric concentration. In addition, optimization is performed at three various cold mass flow rates and different nanoparticles including Al2O3, CuO and ZrO2 and results are compared with the base fluid (water). For the reliability of the present code, the modeling results are validated with the results obtained from both the numerical and experimental model. The results show that the optimal Pareto front is improved in nanoparticles case, and the rate of improvement in CuO nanoparticles case, especially in higher effectiveness and lower cold mass flow rate is more significant compared with the other studied cases. In addition, because of improvement in the thermal performance of MTHE network with nanoparticles, the heat transfer surface area and consequently the total volume of MTHE network for the fixed values of effectiveness are noticeably reduced. Finally, the effects of design parameters versus effectiveness are demonstrated and discussed.

Automated summary

The study optimized a multi tube heat exchanger (MTHE) network considering the effects of different nanoparticles using a genetic algorithm. The results showed a significant improvement in heat exchanger performance with nanoparticles, especially with CuO nanoparticles.