Pressure-based hydrodynamic disturbance inference

Surface pressure measurements contain information about wake-induced flow disturbances and the motion of disturbance sources, yet this information is embedded in complex temporal pressure signals that are difficult to interpret. Extracting informative flow features from such measurements therefore remains challenging. Many existing approaches rely on task-specific mappings between pressure signals and target states, which limits their ability to learn transferable representations of hydrodynamic signals. In this study, we propose a self-supervised learning framework to learn compact temporal representations from sparse surface pressure measurements. Water-tunnel experiments are conducted using a National Advisory Committee for Aeronautics 0015 sensing model equipped with fourteen pressure sensors that record wake-induced pressure fluctuations generated by upstream bodies. A gated recurrent unit encoder is trained through a self-supervised objective to extract temporal features from the pressure sequences, and the learned representations are evaluated through downstream tasks that infer the motion parameters of disturbance sources. The model is first trained using pressure responses induced by the wake of a pitching airfoil and subsequently evaluated under a second tested disturbance scenario involving a translating D-shaped cylinder. Experimental results show that the learned representation improves reconstruction accuracy and accelerates training convergence compared with randomly initialized models, while retaining useful predictive performance in the tested transfer case. Furthermore, spatial channel ablation provides pressure-based evidence for the relative importance of different sensor regions. The ablation results indicate that the most informative sensor region changes between the tested wake-generation mechanisms. These results suggest that self-supervised representation learning can capture temporal pressure-response features that are transferable within the tested configurations, providing a practical framework for pressure-based inference of upstream hydrodynamic states from limited sensor measurements.