Browsing by Author "Alimoradi, Ashkan"

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    Hydrodynamic Analysis of a Heat Exchanger With Crosscut Twisted Tapes and Filled With Thermal Oil-Based Swcnt Nanofluid: Applying Ann for Prediction of Objective Parameters
    (Springer, 2021) Almitani, Khalid H.; Gari, Abdullatif A.; Alimoradi, Ashkan; Ahmadian, Ali; Baleanu, Dumitru; Abu-Hamdeh, Nidal H.
    Present study investigates hydrodynamic analysis of heat exchanger with crosscut twisted tapes and filled with thermal oil-based SWCNT nanofluid (NF). SIMPLE algorithm and FVM method are used. The heat transfer fluid enters the test section at T-in = 300 K in different flow velocities, which are related with Reynolds numbers 5000, 10,000, 15,000 and 20,000. For ensuring that the input flow to test section is always fully developed, the input part to length 2L is considered. It is also intended to ensure that the flow does not return to test section of the exit section of length L. Also, the test section has the constant temperature of T-s = 400 K. Different geometrical parameters of twisted tapes in heat exchanger are studied. The optimization is carried out due to fulfill the highest performance evaluation criterion (PEC index). Based on results, usage of twisted tapes has a sharp impact on thermal and hydraulic characteristics of heat exchanger and leading to swirling motion, which improve the heat transfer coefficient and augment the pressure drop (Delta P). Besides, usage of simple model is more efficient than crosscut model. Also, it is understood that the PEC index values always are more than 1.11, which means that employment of these turbulators is effective and positive with thermal-hydraulic viewpoint. The simple model (Case K and N = 8) is introduced as the most optimum model in this paper, and its PEC values for system filled with NF in phi = 0.8% at Re = 5000, 10,000, 15,000 and 20,000 are 1.37, 1.59, 1.78 and 1.93, respectively. The application of machine learning methods showed that the output parameters in the simulation of heat exchangers are well predicted. The accuracy of the developed neural network was such that the maximum error was below 1%.