Reconstruction of helicopter rotor pressure fields using physics-informed neural networks and flexible pressure sensor arrays

To address the challenges of inaccessible pressure data on helicopter blade surfaces and the inability of sparse pressure measurement points to fully characterize the pressure field across the entire blade, this paper proposes a rotor-blade surface pressure reconstruction framework that combines flexible pressure sensing arrays, Kriging-based statistical priors, and physics-informed neural networks (PINNs). First, a scaled rotor experimental platform equipped with a flexible pressure sensor array is developed to acquire full-field reference pressure data under multiple rotational speeds, providing a verifiable benchmark for model training and evaluation. Under sparse sensing conditions, Kriging regression is then used to generate a prior pressure field together with a spatial variance map that quantifies uncertainty in unmeasured regions. Based on this prior, a residual-learning PINN is constructed, in which weak physical constraints are incorporated into the loss function, while the Kriging variance is further used to achieve spatially adaptive weighting between data fidelity and physics regularization. The main contribution of the proposed framework lies in the integration of statistical prior modeling, uncertainty-aware physics coupling, and flexible-array-based experimental benchmarking for rotor-specific pressure reconstruction. Experimental results show that the proposed method achieves accurate full-field reconstruction under sparse measurements, with an overall reconstruction error of approximately 4%, while preserving key pressure features in critical regions such as the leading edge and blade tip. In addition, sensitivity analysis indicates that the leading-edge and tip regions are the most critical to global reconstruction accuracy, providing practical guidance for sensor placement.