Physics informed neural network surrogate modeling and global sensitivity analysis of magnetized nanofluid flow over a porous cylinder for solar thermal energy applications

Abstract The accurate prediction of mass and heat transfer dynamics in the movement of magnetized nanofluids over porous cylindrical structures is crucial for the design and improvement of innovative solar thermal energy systems. However, traditional numerical methods need comprehensive high-fidelity simulations over several parameter domains, limiting their use in real-time evaluation, optimization, and decision-making. Physics-informed neural networks (PINNs) are becoming dependable alternatives to traditional computational fluid dynamics (CFD) methods for modeling nonlinear fluid dynamics in aerospace and medical engineering. In this study, we introduced a hybrid framework that leverage PINNs, and an adapted finite difference method to improve the computational analysis of nonlinear magnetized ternary nanofluid flow produced by a permeable stretching cylinder. The investigated working fluid is composed of multi-walled carbon nanotubes, $$A7075$$ A 7075 , and graphene oxide nanoparticles suspended in a blend of ethylene glycol and water.The framework of the PINNs, which includes a defined number of hidden layers, a training algorithm, a learning rate, and an epoch count, has been determined through systematic hyper-parameter optimization. We additionally integrated the dynamic influences of magneto hydrodynamic (MHD) phenomena, activation energy, and thermal radiation to enhance heat transfer and regulate fluid flow phenomena. The established set of mathematical equations are addressed using a modified finite difference method and PINNs to accurately predict fluid flow characteristics. The global sensitivity analysis (GSA) was performed to demonstrate the sensitivity and impact of governing factors on fluid dynamics. Thermal radiation and the Biot number strongly influence temperature variance, while the concentration of nanoparticles acts as a governing factor that exerts significant impacts on velocity and heat transmission. The persistently low PINNs loss metrics, remaining below $${10}^{-11}$$ 10 − 11 , indicate exceptional computational precision and validate the great fidelity of the suggested architecture in accurately representing the controlling thermodynamics of the system. Moreover, the DNN-enforced PINNs approach efficiently corresponds to solutions for an intricate thermal-fluid framework with coefficient of determination $$({R}^{2}\mathrm{) > 0.99}$$ ( R 2 )>0.99 . The established PINN surrogate delivers rapid predictions post-training, significantly alleviating the computing cost linked to recurrent high-fidelity numerical simulations, thus presenting an effective option for parametric analysis and global sensitivity evaluation.