The development of hydrofoil cavitation has obvious unsteady characteristics that induce complex pressure variation on the hydrofoil surface. In this work, an algorithm for the pressure reconstruction of the foil based on compressed sensing (CS) technology combined with proper orthogonal decomposition (POD) and particle swarm optimization (PSO) is proposed. Based on the dataset produced by numerical simulation, the pressure field reconstruction with the algorithm is demonstrated to have good accuracy, generalization, and robustness. Firstly, the impact of the number of truncated POD modes and measuring points on the error of the algorithm reconstruction is researched that the error shows a downward trend with the rise in these two elements. Then the sensitivity of the algorithm reconstruction to the hydrofoil characteristics is investigated. It is found that the reconstruction results are more accurate than other stages when the foil is attached to the cloud cavitation and the reconstruction accuracy of the algorithm is proportional to the stability of the flow field. Finally, it is shown that the algorithm can be applied for the reconstruction of different flow parameters of the same model. The placements of the measuring points learned by the algorithm are more likely to be trained to the relatively unstable region.
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