Yazar "Rehman, Khalil Ur" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    Artificial Neural Networking (ANN) Model for Convective Heat Transfer in Thermally Magnetized Multiple Flow Regimes with Temperature Stratification Effects
    (Mdpi, 2022) Rehman, Khalil Ur; Colak, Andac Batur; Shatanawi, Wasfi
    The convective heat transfer in non-Newtonian fluid flow in the presence of temperature stratification, heat generation, and heat absorption effects is debated by using artificial neural networking. The heat transfer rate is examined for the four different thermal flow regimes namely (I) thermal flow field towards a flat surface along with thermal radiations, (II) thermal flow field towards a flat surface without thermal radiations, (III) thermal flow field over a cylindrical surface with thermal radiations, and (IV) thermal flow field over a cylindrical surface without thermal radiations. For each regime, a Nusselt number is carried out to construct an artificial neural networking model. The model prediction performance is reported by using varied neuron numbers and input parameters, and the results are assessed. The ANN model is designed by using the Bayesian regularization training procedure, and a high-performing MLP network model is used. The data used in the creation of the MLP network was 80 percent for model training and 20 percent for testing. The graph shows the degree of agreement between the ANN model projected values and the goal values. We discovered that an artificial neural network model can provide high-efficiency forecasts for heat transfer rates having engineering standpoints. For both flat and cylindrical surfaces, the heat transfer normal to the surface reflects inciting nature towards the Prandtl number and heat absorption parameter, while the opposite is the case for the temperature stratification parameter and heat generation parameter. It is important to note that the magnitude of heat transfer is significantly larger for Flow Regime-IV in comparison with Flow Regimes-I, -II, and -III.
  • Küçük Resim Yok
    Öğe
    Artificial Neural Networking (ANN) Model for Drag Coefficient Optimization for Various Obstacles
    (Mdpi, 2022) Rehman, Khalil Ur; Colak, Andac Batur; Shatanawi, Wasfi
    For various obstacles in the path of a flowing liquid stream, an artificial neural networking (ANN) model is constructed to study the hydrodynamic force depending on the object. The multilayer perceptron (MLP), back propagation (BP), and feed-forward (FF) network models were employed to create the ANN model, which has a high prediction accuracy and a strong structure. To be more specific, circular-, octagon-, hexagon-, square-, and triangular-shaped cylinders are installed in a rectangular channel. The fluid is flowing from the left wall of the channel by following two velocity profiles explicitly linear velocity and parabolic velocity. The no-slip condition is maintained on the channel upper and bottom walls. The Neumann condition is applied to the outlet. The entire physical design is mathematically regulated using flow equations. The result is presented using the finite element approach, with the LBB-stable finite element pair and a hybrid meshing scheme. The drag coefficient values are calculated by doing line integration around installed obstructions for both linear and parabolic profiles. The values of the drag coefficient are predicted with high accuracy by developing an ANN model toward various obstacles.
  • Küçük Resim Yok
    Öğe
    Thermal analysis of flowing stream in partially heated double forward-facing step by using artificial neural network
    (Elsevier, 2022) Rehman, Khalil Ur; Shatanawi, Wasfi; Colak, Andac Batur
    The regulators for thermal energy transfer, performances of heat exchangers, turbine blades subject to cooling structure, and energy storage procedures claim the use of a heated fluid with partially heated circular obstructions rooted in confined domains. Owing to such importance we consider a partially heated double forward-facing step (DFFS). To be more specific, from the inlet of DFFS, the viscous stream flows in parabolic form and the Neumann condition is implemented at the outlet. At each wall, no slip is incorporated. The mathematical formulation is constructed to narrate the flow field. The translation of the centers of mounted heated obstructions is considered in three separate situations. For every event, the strength of the Nusselt number is debated numerically. For all cases, the drag coefficient for partially heated obstruction is found a decreasing function of the Reynolds number. Besides this, for better estimation of Drag Coefficient (DC) and Lift Coefficient (LC), an artificial neural network (ANN) model with multilayer per-ceptron (MLP) is developed. MoD values shows that the error rates of the ANN model are very low. The findings show that the constructed ANN model can accurately predict DC and LC values with very low error rates.