An efficient scheme for surface sensor placement on flight vehicles via clustered statistical features

During the design and use of flight vehicles, to accurately infer the complete aerodynamic distributed loads from sparse surface measurements, it is essential to deploy the limited sensors in locations that encompass abundant information. To address this issue, we propose an efficient scheme based on clustered statistical features. Firstly, statistical features of the aerodynamic distributed loads at all candidate sensor locations are described through four statistical moments, i.e., mean, standard deviation, skewness and kurtosis. Secondly, the K-means++ clustering algorithm is employed to analyze the statistical features at each sensor location. Based on the clustered results, the sensor locations are determined by selecting the location closest to the center of each cluster. Finally, case studies on surface pressure sensor placement for the NACA 0012 airfoil and CRM aircraft are used to validate the sparse inversion accuracy of the measurement points obtained by the proposed method. The results show that for the NACA 0012 airfoil, only 2 sensors are required on the airfoil surface, and the mean absolute error of the inversed surface pressure for the test samples is on the order of 10⁻³. For the CRM aircraft, 4 sensors are placed on the fuselage and 3 on the wing, and the inversion error of the test samples for the fuselage and wing is on the orders of 10⁻⁴ and 10⁻³, respectively.