Paper

In computational fluid dynamics, the Boussinesq approximation is a popular model for the numerical simulation of natural convection problems. Although using the Boussinesq approximation leads to significant performance gains over a full-fledged compressible flow simulation, the model is only plausible for scenarios where the temperature differences are relatively small, which limits its applicability. This paper bridges the gap between Boussinesq flow and compressible flow via deep learning: we introduce a computationally-efficient CNN-based framework that corrects Boussinesq flow simulations by learning from the full compressible model. Based on a modified U-Net architecture and incorporating a weighted physics penalty loss, our model is trained with and evaluated against a specific natural convection problem. Our results show that by correcting Boussinesq simulations using the trained network, we can enhance the accuracy of velocity, temperature, and pressure variables over the Boussinesq baseline—even for cases beyond the regime of validity of the Boussinesq approximation.