Yazar "Shatanawi, Wasfi" seçeneğine göre listele

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    Artificial neural networking estimation of skin friction coefficient at cylindrical surface: a Casson flow field
    (Springer, 2023) Rehman, Khalil Ur; Shatanawi, Wasfi; Çolak, Andaç Batur
    In this article, we constructed Artificial Neural Networking (ANN) models to predict values of the skin friction coefficient for two different flow regimes of non-Newtonian fluid. More specifically, flow of Casson fluid is considered toward an inclined surface with stagnation point and mixed convection effects. Energy equation is considered by means of thermal radiations, viscous dissipation, heat generation and temperature-dependent variable viscosity effects. The flow regime is carried as a two various models namely Model-I: Casson fluid flow in the presence of magnetic field and Model-II: Casson fluid flow in the absence of magnetic field. Mathematical formulation is presented for each model, and shooting method is used to obtain the numerical data of skin friction coefficient. In contrast to the Casson fluid, mixed convection, and velocities ratio parameters, the skin friction coefficient exhibits a direct relationship with the magnetic field parameter and the curvature parameter. The MoD values for both models (I, II) show that there is relatively little variation between targeted and the projected values produced from the constructed ANN models.
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    Artificial Neural Networking Magnification for Heat Transfer Coefficient in Convective Non-Newtonian Fluid with Thermal Radiations and Heat Generation Effects
    (MDPI, 2023) Rehman, Khalil Ur; Shatanawi, Wasfi; Çolak, Andaç Batur
    In this study, the Casson fluid flow through an inclined, stretching cylindrical surface is considered. The flow field is manifested with pertinent physical effects, namely heat generation, viscous dissipation, thermal radiations, stagnation point flow, variable thermal conductivity, a magnetic field, and mixed convection. In addition, the flow field is formulated mathematically. The shooting scheme is used to obtain the numerical data of the heat transfer coefficient at the cylindrical surface. Further, for comparative analysis, three different thermal flow regimes are considered. In order to obtain a better estimation of the heat transfer coefficient, three corresponding artificial neural networks (ANN) models were constructed by utilizing Tan-Sig and Purelin transfer functions. It was observed that the heat transfer rate exhibits an inciting nature for the Eckert and Prandtl numbers, curvature, and heat generation parameters, while the Casson fluid parameter, temperature-dependent thermal conductivity, and radiation parameter behave oppositely. The present ANN estimation will be helpful for studies related to thermal energy storage that have Nusselt number involvements.
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    Computational Analysis on Magnetized and Non-Magnetized Boundary Layer Flow of Casson Fluid Past a Cylindrical Surface by Using Artificial Neural Networking
    (MDPI, 2023) Rehman, Khalil Ur; Shatanawi, Wasfi; Çolak, Andaç Batur
    In this article, we constructed an artificial neural networking model for the stagnation point flow of Casson fluid towards an inclined stretching cylindrical surface. The Levenberg–Marquardt training technique is used in multilayer perceptron network models. Tan–Sig and purelin transfer functions are carried in the layers. For better novelty, heat and mass transfer aspects are taken into account. The viscous dissipation, thermal radiations, variable thermal conductivity, and heat generation effects are considered by way of an energy equation while the chemical reaction effect is calculated by use of the concentration equation. The flow is mathematically modelled for magnetic and non-magnetic flow fields. The flow equations are solved by the shooting method and the outcomes are concluded by means of line graphs and tables. The skin friction coefficient is evaluated at the cylindrical surface for two different flow regimes and the corresponding artificial neural networking estimations are presented. The coefficient of determination values’ proximity to one and the low mean squared error values demonstrate that each artificial neural networking model predicts the skin friction coefficient with high accuracy
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    Different scenarios to enhance thermal comfort by renewable-ecological techniques in hot dry environment
    (Elsevier Ltd, 2022) Sakhri, Nasreddine; Ahmad, Hijaz; Shatanawi, Wasfi; Menni, Younes; Ameur, Houari; Botmart, Thongchai
    Recently, building thermal studies have focused more and more on providing the right living conditions inside buildings, houses, schools, hospitals, etc., especially in hot-dry regions to defeat energy consumption dilemmas generally coming from fossil fuels source by renewable energy. In this paper, a field of experiments in actual conditions is conducted to investigate the influence of external parameters on the occupant's thermal comfort inside a typical dry region house. The obtained results are projected directly on the psychometric chart to position the real thermal comfort current situation. The results confirm the direct influence and indirect influence of external climatic conditions (temperature and humidity, respectively) on internal comfort. Two scenarios with renewable techniques are investigated experimentally based on the obtained results. An earth-to-air heat exchanger (EAHE) and solar chimney (SC) are connected separately to a similar building, and parameters affecting thermal comfort are discussed. The results show that both techniques improve thermal comfort inside the structure with efficiently saving energy. Renewable energy can enhance thermal comfort with significant power- and cost-saving in hot-dry regions.
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    Levenberg–Marquardt Training Technique Analysis of Thermally Radiative and Chemically Reactive Stagnation Point Flow of Non-Newtonian Fluid with Temperature Dependent Thermal Conductivity
    (MDPI, 2023) Rehman, Khalil Ur; Shatanawi, Wasfi; Çolak, Andaç Batur
    We have examined the magnetized stagnation point flow of non-Newtonian fluid towards an inclined cylindrical surface. The mixed convection, thermal radiation, viscous dissipation, heat generation, first-order chemical reaction, and temperature-dependent thermal conductivity are the physical effects being carried for better novelty. Mathematical equations are constructed for four different flow regimes. The shooting method is used to evaluate the heat transfer coefficient at the cylindrical surface with and without heat generation/thermal radiation effects. For better examination, we have constructed artificial neural networking models with the aid of the Levenberg– Marquardt training technique and Purelin and Tan-Sig transfer functions. The Nusselt number strength is greater for fluctuations in the Casson fluid parameter, Prandtl number, heat generation, curvature, and Eckert number when thermal radiations are present.
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    Neural networking-based analysis of heat transfer in MHD thermally slip Carreau fluid flow with heat generation
    (Elsevier, 2024) Ur Rehman, Khalil; Shatanawi, Wasfi; Çolak, Andaç Batur
    The formulation of heat transfer in non-Newtonian fluid models remains a topic of great interest for researchers. The ultimate flow differential equations in this direction are non-linear and hence difficult to solve analytically. Therefore, we offer state of the art determination of film effectiveness (heat transfer coefficient) by using artificial intelligence (AI). To be more specific, the Carreau fluid model is formulated at a flat surface along with thermal slip, heat generation, velocity slip, chemical reaction, and magnetohydrodynamics (MHD) effects. The developed equations are reduced by the application of the Lie symmetry approach and solved by the shooting method. The artificial neural networking model is built by using 132 sample values of the Nusselt number (NN) as an output. Porosity parameter, Prandtl number, magnetic field parameter, and heat production parameter are all inputs. 92 (70 %) is designated for training, whereas 20 (15 %) is designated for validation and testing. The Levenberg-Marquardt algorithm is used to train the neural network. The constructed artificial neural networking (ANN) is best to predict the NN at a flat surface. It is observed that for large Prandtl numbers, the magnitude of the Nusselt number is greater for porous surface, but the converse is true for magnetic field parameter.