a b ansari

In the present study, multi-objective optimization of latent heat energy storage performance in a double-pipe heat exchanger with wavy inner wall is carried out, in which both thermal and hydraulic perspectives are considered. By using the ANSYS-Fluent commercial software, the k-ɛ RNG model and enthalpy-porosity model are applied to simulate the turbulent flow of hot water and the behavior of PCM-RT35, respectively. The amplitude and wave number of the internal wall are considered as optimization variables, while minimizing PCM charging time and water flow pressure drop are considered as objective functions. In addition, the response surface method has been used as an optimization method and the desirability function has been used as a decision criterion for choosing the optimal system. The simulation results show that with the increase in amplitude and wave number, the pressure drop in the channel increases, while the charging time decreases due to the vortices and the increase of the heat transfer surface. Also, the optimization results show that compared to the simple geometry, the charging time is reduced by 45%, while the pressure drop is increased by about 5% in the optimized system.

The analysis of heat transfer in the channel in many types of heat exchangers, such as electric cooling equipment, solar collectors, heat exchanger systems, high-performance boilers, gas turbine blade coolers, etc., is the basis of the design, construction, and optimization. Controlling heat transfer to increase the rate of heat transfer in such systems by improving the cooling method is an effective energy engineering from the point of saving energy. Increasing the heat transfer performance in the scales of macro and microchannels is crucial. The use of expanded surfaces in the channel is a practical method to increase the heat transfer coefficient. In the upcoming article, the smart design of a two-dimensional nanofluid heat exchanger has been studied numerically in order to achieve optimal performance conditions in terms of heat transfer rate, the amount of deposition of nanoparticles in the structure of the exchanger, as well as the fluid pressure drop while passing through it. It can be seen that the geometric structure optimized by the combination of genetic algorithm and computational fluid dynamics of this channel causes an increase of 1.14% in the enthalpy of the passing nanofluid, a decrease of 11.21% in the pressure drop of the passing nanofluid, and a reduction of 8.44% percentage in the deposition of nanoparticles inside the channel and a total increase of 24.82% in the fitting function defined in terms of these three variables, compared to the channel designed in previous studies. Therefore, this optimal channel has a higher heat transfer rate with a pressure drop and a lower amount of nanoparticle deposition compared to the previous channel, which proves the ability of the genetic algorithm with computational fluid dynamics in the optimal design of all types of heat exchangers.