Deep Learning‐Based Magnetohydrodynamic Analysis of Tetra‐Hybrid Nanofluid Flow With Seawater at 20°C Over Bullet‐Shaped Structures

The magnetohydrodynamic (MHD) steady flow of a tetra‐hybrid nanofluid made up of MOS ₂ , GO, Al ₂ O ₃ , and Cu nanoparticles floating in a saltwater medium at 20°C across a porous, exponentially stretching bullet‐shaped geometry. It is novel in its use of multi‐nanoparticles to achieve improved thermal performance, along with deep learning and optimization methods for heat‐transfer predictions. By using similarity transformations, the nonlinear governing partial differential equations are reduced to a set of ordinary differential equations. The resulting system is subsequently solved using an LSTM‐based deep neural network and the Galerkin technique, with optimization carried out using the Adam algorithm to forecast the heat transfer rate. The response surface methodology (RSM) is also used to optimize the influence of significant regulating elements. The results show that temperature fields rise when magnetic field strength, Forchheimer number, radiation parameter, and opposing flow conditions all rise. Compared to Case‐2, the rate of heat transmission in Case‐1 is more improved, and percentage improvements were found to be between 0.00003378 to 1.25. It was observed that deep learning produced an accurate prediction of the heat transfer rate. The results indicate that tetra‐hybrid nanofluids in seawater 20°C are very effective in heat transfer and can thus be used in ocean applications like desalination systems, heat management in the ocean and underwater energy harvesting, and underwater heat exchangers.