Real-time physical field reconstruction for nanofluids convection using deep learning with auxiliary tasks

Nanofluids has become one of the most promising cooling fluids due to its great thermophysical properties, especially for microchannels. In recent years, to avoid prohibitively expensive simulations in design process or iterative optimization, massive data-driven and machine learning methods have been employed to construct surrogate models for thermophysical properties or concerned performance characteristics. Though the favorable accuracy is validated, this kind of surrogate models ignore the importance of physical fields, which are also an evaluating criterion in heat transfer design scenarios. In this article, we develop a deep learning framework to achieve the main task of real-time reconstruction mapping from design variables to physical fields. In addition, the auxiliary tasks of prediction mapping from physical fields to performance characteristics are tailored to improve the accuracy and generalization of reconstruction mapping. To validate the feasibility of this framework, we assess the ability of the generator alone to reconstruct physical fields and predict performance characteristics for the flow and heat transfer of nanofluid first. The results show that the introduction of auxiliary task can greatly improve the performance of main task. The computational performance analysis indicates that a well-trained deep learning model with GPU-accelerated has three orders of magnitude faster than CFD solver. This proposed approach was expected to provide a real-time predictor with a reasonable level of accuracy for nanofluid convective heat transfer.