Yazar "Khan, Sher Afghan" seçeneğine göre listele

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    Analysis of bubble departure and lift-off boiling model using computational intelligence techniques and hybrid algorithms
    (Elsevier France-Editions Scientifiques Medicales Elsevier, 2024) Quadros, Jaimon Dennis; Mogul, Yakub Iqbal; Agbulut, Umit; Gurel, Ali Etem; Khan, Sher Afghan; Akhtar, Mohammad Nishat; Jilte, R. D.
    The bubble departure and lift-off boiling (BDL) model was studied using computational intelligence techniques and hybrid algorithms. Quite a few studies have predicted the relationship between wall heat fluxes and wall temperature in the form of flow boiling curves. The output wall temperature is a performance indicator that depends on many operating parameters. The current study, therefore, analyses the predictability of the wall temperature in terms of operating pressure, bulk flow velocity, and wall heat flux, based on the BDL model developed by Zenginer, which included two suppression factors namely, flow-induced and subcooling factors, respectively. The soft computing techniques used for prediction were - the artificial neural network (ANN), and the Fuzzy Mamdani model, and the hybrid algorithms were adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained particle swarm optimization (ANN-PSO). In addition, the ANN-PSO conducted a parametric analysis to evaluate the best model configuration by considering various factors. The comparison of all four techniques showed that the ANFIS model exhibited the prediction performance for wall temperature. Moreover, the results obtained from the ANFIS model have been compared with the different flow boiling curves from the literature and observed that the curve fitted well for higher bulk flow velocities with an MSE and R2 was found to be 0.85 % and 0.9933, respectively.
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    Battery thermal management of a novel helical channeled cylindrical Li-ion battery with nanofluid and hybrid nanoparticle-enhanced phase change material
    (Pergamon-Elsevier Science Ltd, 2023) Jilte, Ravindra; Afzal, Asif; Agbulut, Uemit; Shaik, Saboor; Khan, Sher Afghan; Linul, Emanoil; Asif, Mohammad
    Electric vehicles (EVs) have emerged as a viable alternative to Internal Combustion (IC) engine-powered vehicles, and efforts have been directed toward developing EVs that are more reliable and safer to operate. The safe working of EVs necessitates the use of an efficient battery cooling system. In this paper, cooling of cylindrical type Li-ion battery embedded with helical coolant channels is proposed. The effects of nanoparticles on removing heat from the battery cooling system have been investigated for four different nanoparticle concentrations: 0, 2, 5, and 10% of Al2O3 in the base fluid. Two cases of base fluids are considered: phase change material kept in a concentric container surrounding battery volume and coolant water circulated through liquid channels attached to the outer walls of the PCM (phase change material) cylindrical container. This study presented the three configurations (i) base case PCM-WLC: battery cooling system with a cylindrical enclosure filled with RT-42 phase change material. (ii) base case nePCM-WLC: battery cooling system filled with nano-enhanced phase change material. (iii) nePCM-LC: battery cooling system with helical liquid channels and filled with nanoenhanced PCM. The nanofluid was circulated through the liquid passages connected to the PCM container. Results showed using the helical channels, the nePCM-LC arrangement efficiently removes accumulated heat from the phase change material and provides better battery cooling than straight rectangular channel-based BTMS (battery thermal management system).