Thermal fluid fields reconstruction for nanofluids convection based on physics-informed deep learning

Based on physics-informed deep learning method, the deep learning model is proposed for thermal fluid fields reconstruction. This method applied fully-connected layers to establish the mapping function from design variables and space coordinates to physical fields of interest, and then the performance characteristics Nusselt number Nu and Fanning friction factor f can be calculated from the reconstructed fields. Compared with reconstruction model based on convolutional neural network, the improved model shows no constrains on mesh generation and it improves the physical interpretability by introducing conservation laws in loss functions. To validate this method, the forced convection of the water-Al2O3 nanofluids is utilized to construct training dataset. As shown in this paper, this deep neural network can reconstruct the physical fields and consequently the performance characteristics accurately. In the comparisons with other classical machine learning methods, our reconstruction model is superior for predicting performance characteristics. In addition to the effect of training size on prediction power, the extrapolation performance (an important but rarely investigated issue) for important design parameters are also explored on unseen testing datasets.

Automated summary

This paper proposes a deep learning model for thermal fluid fields reconstruction based on physics-informed deep learning method. The model improves physical interpretability by establishing a mapping function and introducing conservation laws. Experimental results demonstrate that the model accurately reconstructs physical fields and calculates performance characteristics.