Numerical study of coupled heat transfer in dual cavity using partitioned coupling strategy with physically informed neural networks

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ABSTRACT Physically Informed Neural Networks (PINNs) integrate physical knowledge into deep learning by mathematically encoding a system of partial differential equations, with successful applications in fluid mechanics and heat transfer. For instance, in the aerospace industry, PINNs are employed to predict the flow and temperature fields within a turbine blade. In this manuscript, a dual cavity model is employed as a simplified representation of the turbine blade, and the coupled heat transfer process within this model is numerically simulated to validate the efficacy of PINNs in this field. However, the strong coupling between solid thermal conductivity and fluid heat transfer renders it challenging for the generalized PINNs to effectively address the coupled heat transfer problem in this field. Consequently, in this manuscript, a PINNs based on a partitioned coupling strategy is employed to numerically simulate the coupled heat transfer problem. The strategy effectively couples the temperature fields in each subregion by dividing the complex coupled problem into multiple subregions and performing numerical simulations in each subregion using PINNs. The coupled heat transfer process is simulated in both cavities using COMSOL® software, and the predicted data set is exported for use in the model. Eventually, the multiphysics field results predicted by the Partitioned Coupling PINNs are compared with the predicted results of the CFD software. This comparison reveals that the relative errors of the velocity field, pressure field, and temperature field were 9.22 × 10−2, 3.06 × 10−4, and 1.21 × 10−1, respectively. These results demonstrate the efficacy of the Partitioned Coupling PINNs in simulating the coupled heat transfer problem. Moreover, the trained model is capable of numerically simulating the dual cavity model in a mere one minute, whereas the COMSOL® calculation requires 31 hours, representing a computational efficiency of over 1000 times.