Enhanced hydrofoil pressure field reconstruction through phase-informed sensing and sensor optimization

Abstract

This study proposes a cost-effective mixed-learning method for the cavitation behavior on hydrofoils, aiming to predict the global pressure field on the hydrofoil surface with sparse placed pressure sensors and the variation of the cavity outline captured by the high-speed cameras. The method integrates CS (compressed sensing) and deep learning techniques. Firstly, within the framework of CS, the Particle Swarm Optimization (PSO) algorithm is utilized to identify the best measuring points and achieve the initial reconstruction accordingly. Subsequently, in combination with the phase information, the final reconstructions are obtained within the deep learning framework. Through validation, the proposed mixed-learning model demonstrates significantly improved reconstruction performance compared to a single source of pressure information and an unoptimized measurement point position and significantly reduces the number of sensors. This provides a novel and effective approach for accurately predicting the pressure field on hydrofoil surfaces. Furthermore, the study evaluates the robustness concerning hydraulic sensor measurement errors, sensor placement deviations, missing measuring points, reconstruction performance under various cases and hydrofoil surface regions, and the contribution of optimized measuring points to the global field. Results show that the mixed-learning model exhibits robustness against typical measurement errors, position deviations of hydraulic sensors and missing measuring points on the suction side. Additionally, a positive correlation exists between the average pressure gradient under different conditions and regions and the reconstruction error, with the pressure side region significantly contributing to global field reconstruction. These findings underscore the mixed model's superiority in predicting pressure fields on hydrofoils and offer guidance for rational sensor installation.