Reconstructing Multiphase Flow Fields with Limited Pressure Observations Based on an Improved Transformer Model

Abstract

In practical applications, the implementation of active cavitation control can significantly enhance the hydrodynamic performance of underwater vehicles. However, the sparsity of sensor information poses a substantial challenge to acquiring flow field states. Recent advances in deep learning offer reliable solutions to this problem. Specifically, deep learning methods, through the construction of sparse reconstruction models, establish connections between sparse observational information and flow field states with minimal or no prior knowledge. To reconstruct multiphase flow fields from sparse pressure observations on the hydrofoil surface, this work proposes a Transformer-based sparse reconstruction model. Test results on cavitation datasets demonstrate that the model's predictions of cavity contours, cavity lengths, and cavity volumes are highly consistent with actual results during the sheet cavity growth and cavity shedding stages. However, due to the highly unsteady nature of cavitation flow and the lack of far-field information, the model's prediction performance is limited during the cloud cavity aggregation and collapse stages. This model exhibits potential in the sparse reconstruction of multiphase flow fields, providing support for the observation of flow field states and active cavitation control.