Data-driven Modelling of Thermal Convection Problem Using U-Net Convolutional Neural Networks

Thermal convection is normally calculated by numerical solvers such as Nektar++. However, due to their high memory cost and time cost, their limitations promote the research to solve the complex fluid phenomena with alternative methods. In this research, the U- Net CNN was successfully constructed for learning the thermal convection problem and studying velocities, temperature, and pressure in a closed geometry with a periodic bottom heating source. Tests were conducted for its generalization for different bottom heating patterns and its performance regarding the time length as well as Raleigh’s number. The optimized model proved its high accuracy in prediction and was able to achieve a lower MSE and lower time cost compared with the result generated by Nektar++. Importantly, the model showed a stronger capability in recognizing the temperature distribution. The prediction quality of the model was decreased if the time skip length as well as the time length increased. Huge memories also occurred due to it pursuing high precision and studying four variables together.