الفهرس 
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Abstract
Nanoparticles enhanced ionic liquids (NEILs) are unique and ingenious types of heat transfer fluids (HTFs). Ionic liquids possess distinguished thermophysical properties that make them eco-friendly. The current thesis presents the numerical investigation of the thermal behavior of the Ionic nanofluid [C4mim] [NTF2] +ϕAl2O3, which filled the square enclosure under the impacts of natural convection, and magnetic field. The magnetic field is imposed in the right horizontal direction. Moreover, a heat source is located on the bottom side of the enclosure, whereas the top wall is kept at a low temperature. Both the right and the left sides of the enclosure are thermally insulated. The governing equations consisted of mass conservation, momentum conservation, and energy conservation. These equations were solved by using FORTRAN software depending on the finite volume method. The analysis was made according to the following conditions , , and . While the value of the Prandtl number is kept constant during the investigation. The results of the numerical examination explained the impacts of the increase in Hartman number, the Rayleigh number, and the variation of the concentration of nanoparticles on the performance of heat transfer. The results indicated that the ariation in the rate of the heat transfer, which happened due to adding nanoparticles, depended on the strength of the magnetic field and Rayleigh number. There were two modes of heat transfer that relied on the influence of the magnetic field and the influence of the Rayleigh number. At a low value of the Rayleigh number, heat is transferred by conduction only because the high effect of viscosity inhibited the fluid motion Moreover, the increase in the value of Rayleigh number caused an enhancement of the average Nusselt number. On the other hand, the magnetic field had negative impacts on the heat transfer behavior for all values of Hartman number. Additionally, a model of an artificial neural network (ANN) was designed to predict the value of the average Nusselt number at different operating conditions. The configured artificial neural network is a multi-layer perceptron consists of an input layer, a single hidden layer, and an output layer with (5,8,1) neurons respectively for each layer. The used artificial neural network model was trained using the Levenberg-Marquardt backpropagation algorithm to adjust the accurate values of both the weight of each connection and the biases. Also, the used neural network was tested for two different operating conditions. The results of the ANN showed a great agreement between the numerical data and the ANN data.