Browsing by Author "Fares, Mohammad N."

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    Citation Count: 2
    Numerical examination of exergy performance of a hybrid solar system equipped with a sheet-and-sinusoidal tube collector: Developing a predictive function using artificial neural network
    (Elsevier, 2024) Sun, Chuan; Salahshour, Soheıl; Sajadi, S. Mohammad; Li, Z.; Jasim, Dheyaa J.; Hammoodi, Karrar A.; Alizadeh, As'ad
    Integrating cooling systems with photovoltaic-thermal (PVT) collectors has the potential to mitigate the exergy consumption in the building sector due to their capability for simultaneous power and thermal energy generation. The simultaneous utilization of nanofluid and geometry modification resulted in a synergetic enhancement in the performance of PVTs and thereby reducing their sizes and costs. In addition, there is still a lack of high accurate predictive model for the estimation of the performance of PVTs at a given Re number and nanofluid concentration ratio to be used in engineering design for the further product commercialization. To this end, the current numerical study investigates the exergy electricity, thermal, and overall exergies of a building-integrated photovoltaic thermal (BIPVT) solar collector with Al2O3/water coolant. The increase in nanoparticle concentration (omega) from 0 % to 1 % increased the useful thermal exergy and overall exergy efficiency (Exu,t/ Yov) by 0.3999 %/0.0497 %, 1.3959 %/0.2598 %, and 0.7489 %/0.1771 % at Re numbers of 500, 1000, and 1500, respectively, while Exu,t/ Yov exhibited a reducing trend at Re = 2000; 0.3928 %/0.1056 % decrease. In addition, the increase in omega from 0 % to 1 % caused the useful electricity and electrical exergy (Exu,e/ Ye) to be diminished by 0.0060 %/0.0025 % at Res 500 and 1000, and to be escalated by 0.0113 %/0.0055 % at Res of 1500 and 2000. Meanwhile, the Re augmentation, from 500 to 2000, improved the Exu,t, Exe, Ye, and Yov by 60 %, 1.26 %, 1.26 %, and 17.50 %, respectively, at different omega s. In addition, two functions were developed and proposed by applying a group method of data handling-type neural network (GMDH-ANN) to forecast the value of Υov based on two input values (Re and omega). The results showed high accuracy of the proposed model with MSE, EMSE, and R2 of 0.0138, 0.1143, and 0.99785, respectively.