Browsing by Author "Khan, Umair"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method
    (Springer Nature, 2024-03-25) Chandan, K.; Saadeh, Rania; Qazza, Ahmad; Karthik, K.; Kumar, R. S. Varun; Kumar, R. Naveen; Khan, Umair; Masmoudi, Atef; Abdou, M. Modather M.; Ojok, Walter; Kumar, Raman
    Fins are widely used in many industrial applications, including heat exchangers. They benefit from a relatively economical design cost, are lightweight, and are quite miniature. Thus, this study investigates the influence of a wavy fin structure subjected to convective effects with internal heat generation. The thermal distribution, considered a steady condition in one dimension, is described by a unique implementation of a physics-informed neural network (PINN) as part of machine-learning intelligent strategies for analyzing heat transfer in a convective wavy fin. This novel research explores the use of PINNs to examine the effect of the nonlinearity of temperature equation and boundary conditions by altering the hyperparameters of the architecture. The non-linear ordinary differential equation (ODE) involved with heat transfer is reduced into a dimensionless form utilizing the non-dimensional variables to simplify the problem. Furthermore, Runge–Kutta Fehlberg’s fourth–fifth order (RKF-45) approach is implemented to evaluate the simplified equations numerically. To predict the wavy fin's heat transfer properties, an advanced neural network model is created without using a traditional data-driven approach, the ability to solve ODEs explicitly by incorporating a mean squared error-based loss function. The obtained results divulge that an increase in the thermal conductivity variable upsurges the thermal distribution. In contrast, a decrease in temperature profile is caused due to the augmentation in the convective-conductive variable values.
  • Thumbnail Image
    Item
    Two‑phase simulation of entropy optimized mixed convection flow of two different shear‑thinning nanomaterials in thermal and mass diffusion systems with Lorentz forces
    (Springer Nature, 2024-01-04) Suresha, S.; Khan, Umair; Soumya, D. O.; Venkatesh, P.; Gasmi, Hatem; Sunitha, M.; Zaib, Aurang; Al‑Naghi, Ahmed; Karoui, Hatem; Ishak, Anuar; Ojok, Walter
    This research compares the momentum, thermal energy, mass diffusion and entropy generation of two shear thinning nanofluids in an angled micro-channel with mixed convection, nonlinear thermal radiation, temperature jump boundary condition and variable thermal conductivity effects. The approach was used to solve the Buongiorno nonlinear governing model. The effect of different parameters on the flow, energy, concentration, and entropy generating fields have been graphically illustrated and explained. The hyperbolic tangent nanoliquid has a better velocity than the Williamson nanofluid. The Williamson nanofluid has higher thermal energy and concentration than the hyperbolic tangent nanoliquid in the microchannel. The Grashof number, both thermal and solutal, increases the fluid flow rate throughout the flow system. The energy of the nanoliquid is reduced by the temperature jump condition, while the energy field of the nanoliquid is enhanced by the improving thermal conductivity value. The nanoliquids concentration rises as the Schmitt number rises. The irreversibility rate of the channel system is maximized by the variable thermal conductivity parameter.