TY - JOUR
TI - Modeling and simulation of micropolar flow and thermal radiation in porous media
AU - Mahariq Ibrahim
AU - Ullah Kashif
AU - Fiza Mehreen
AU - Ullah Hakeem
AU - Akgul Ali
AU - Elnaggar Ghada R
AU - Khan Ilyas
AU - Koh Wei Sin
JN - Thermal Science
PY - 2025
VL - 29
IS - 4
SP - 3075
EP - 3086
PT - Article
AB - Recurrent neural networks (RNN) have attracted attention in the academic  community because of their capability to handle intricate, non-linear  models. The RNN, with their strong pattern recognition abilities, are  therefore well-equipped to be applied in intricate fields such as fluid  dynamics, biological computing, and biotechnology. This study investigates  the effectiveness of the Levenberg-Marquardt algorithm combined with  recurrent neural networks (LMA-RNN) is simulating the heat transfer of a  micropolar fluid through a porous medium with radiation (HTMFPMR) model. In  this research, data is obtained using the Adams numerical technique and  later optimized through the application of LMA-RNN. The LMA-RNN approach  divides the data by using 80% for training, 10% for testing, and the  remaining 10% for validation purposes. The velocity and temperature  distributions are presented, and the effects of the inertia coefficient,  micro-rotation, radiation parameter, and Prandtl number on the heat transfer  are thoroughly analyzed. An upsurge in the permeability constraint outcomes  in a rise in angular velocity and temperature, while causing a reduction in  velocity. As the vortex-viscosity constraint increases, both the velocity  and angular velocity show an upward trend. The temperature field declines  with rise radiation constraint. Mean squared error (MSE), regression plots,  and error histograms are used to assess the performance of the LMA-RNN that  have been applied. Reduced MSE indicates more accurate model predictions,  validating the proposed strategy.