# Morphological Reconstruction for Variable Wing Leading Edge Based on the Node Curvature Vectors
## 3 Simulation Verifications
### Results and Discussion
#### 3.2.3 Comparison of Morphology Reconstruction Errors between LI-CPM and NCV-CPM  
To assess the feasibility of the aforementioned reconstruction method, simulations were conducted to verify the effectiveness of enhancing the accuracy of the morphological  
Figure 10: The number optimization process of sensing points based on the particle swarm optimization algorithm.  
reconstruction of the variable wing leading edge based on the curvature nodal vector solution and point optimization results.  
At a leading-edge deflection angle of 10\({}^{\circ}\), the reconstructed morphology curve of the wing leading edge along Path1 closely aligned with the simulated displacement curve, as illustrated in Figure 11.  
The conventional curvature propagation method (LI-CPM) based on linear interpolation yielded an average relative error and root mean square error of morphological reconstruction of 10.85% and 28.84 mm, respectively. In contrast, the proposed morphological reconstruction method (NCV-CPM) in this study achieved an average relative error and root mean square error of 3.34% and 7.06 mm, respectively, as depicted in Figure 12a.  
Figure 11: Morphological reconstruction results of NCV-CPM for wing leading edge of deflection angle 10\({}^{\circ}\): (**a**) Euclidean distance cloud map; (**b**) reconstruction effect comparison.  
Figure 12: Propagation reconstruction effect comparison of LI-CPM and NCV-CPM: (**a**) relative error under 10\({}^{\circ}\) deflection angle of leading edge; (**b**) root mean square error under four leading edge deflection angles.  
A comparison of the morphological reconstruction accuracy between the conventional curvature propagation method and the algorithm proposed in this study is presented in Figure 12b for leading-edge deflection angles of \(2^{\circ}\), \(4^{\circ}\), \(8^{\circ}\), and \(10^{\circ}\).  
From Figure 12, it is evident that, compared to the conventional curvature propagation method based on linear interpolation, the method proposed in this study significantly improved the morphological reconstruction accuracy at four different deflection angles. This improvement stemmed from the fact that the conventional curvature propagation method utilized a first-order linear interpolation function to obtain curvature information, which failed to meet the requirements for fitting the leading-edge curvature field, thereby affecting the accuracy of the morphological reconstruction. In contrast, the method proposed in this study enhanced the reconstruction effectiveness of the curvature fitting function by introducing additional nodes within the segments, thereby avoiding the influence of calculation errors in curvature information due to unattached sensor positions on the accuracy of the wing leading-edge morphology reconstruction.  
Furthermore, this study employed a particle swarm algorithm to determine the optimal number of virtual sensing points required for the morphological reconstruction of the wing leading edge, thereby reducing the complexity of the sensor network.