As MW-scale wind turbines are becoming larger with increasingly flexible blades, the need for sophisticated blade health monitoring techniques is growing. Recent work has shown that Microelectromechanical systems (MEMS) sensor strips are well suited for the purpose of monitoring wind turbine blade aerodynamics [1]. While these systems could potentially cover an entire airfoil section of a small wind turbine blade, for larger blades with more extensive chord lengths only relatively small segments can feasibly be instrumented. In such cases, it becomes important to place the sensing strips in optimal locations, which maximize the collection of pertinent data required for the downstream monitoring tasks (e.g. damage detection, angle of attack estimation, etc.). While these downstream tasks can be of diverse nature, we choose to focus on a more general problem: flow reconstruction. Reconstructing the flow around a blade entails using the pressure measured on its surface in order to extrapolate the features (pressure and velocity fields) of the surrounding flow. Reconstructed solutions can then be utilized to enhance the performance of other monitoring tasks. In previous work, we have demonstrated how Graph Neural Networks (GNNs) could be employed to reconstruct the flow field surrounding arbitrary 2D airfoils, using only as an input the surface pressure [2]. Building upon this framework, we frame the problem of optimal sensor placement in terms of maximizing a GNNs ability to reconstruct the surrounding flow field given variable partial airfoil surface pressure. This is a challenging optimization problem, which depends not only on the shape of the airfoil but also on the external operational and environmental conditions. We show how the expressivity and extrapolation capabilities of GNNs can be leveraged such that given an arbitrary airfoil shape, a set of input conditions and some physical sensor constraints, an optimal sensing patch location is selected such that a good estimation of the surrounding flow is obtained. Finally, by incorporating elements from Graph Sampling Theory, we highlight how to properly condition the learning objective for the reconstruction task.

{"references": ["Barber, S., Deparday, J., Marykovskiy, Y., Chatzi, E., Abdallah, I., Duth\u00e9, G., ... & M\u00fcller, H. (2022). Development of a wireless, non-intrusive, MEMS-based pressure and acoustic measurement system for large-scale operating wind turbine blades. Wind Energy Science, 7(4), 1383-1398.", "Duth\u00e9, G., Abdallah, I., Barber, S., & Chatzi, E. (2023). Graph Neural Networks for Aerodynamic Flow Reconstruction from Sparse Sensing. arXiv preprint arXiv:2301.03228."]}