飞行器因长期服役或加改装导致的几何形变和性能漂移问题，对其全生命周期性能评估与运维保障提出了严峻挑战。本文提出一种基于实测点云的“逆向建模—自由形变—气动仿真”一体化评估框架。框架在逆向建模阶段，发展了包含法向量信息的点云鲁棒重建流程，结合屏蔽泊松重建与气动拓扑逻辑，将非结构化的无序点云转化为满足计算流体动力学边界条件的水密网格。为克服泊松算法对法向噪声敏感的问题，引入特征保持平滑策略对点位与法向量场进行联合优化，在抑制高频噪声的同时，有效兼顾了机翼后缘等关键气动特征的几何连续性。在自由变形阶段，无需原始参数化几何模型，基于FFD自由变形技术构建控制体可实现对机翼扭转、上反等关键气动外形的参数化驱动。最后利用无网格的格子玻尔兹曼方法，重建几何可直接开展多工况绕流流场数值模拟，分析形变前后气动参数与流场结构的响应规律。研究结果表明，该评估框架能够从高精度扫描点云数据中重构出高保真度水密几何模型，并依靠FFD自由变形技术实现几何外形参数化变形，为复杂构型飞行器的数字孪生构建与其多学科性能评估提供了可靠工程路径。
Geometric deformations and performance drifts resulting from long-term service or modifications present significant challenges to the lifecycle assessment and maintenance of aircraft. To address this, we propose an integrated assessment framework based on measured point clouds that synthesizes reverse modeling, free-form deformation (FFD), and aerodynamic simulation. In the reverse modeling phase, we develop a robust point cloud reconstruction workflow incorporating normal vector information. By combining Screened Poisson Reconstruction with aerodynamic topological logic, unstructured point clouds are converted into watertight meshes that satisfy the boundary conditions required for computational fluid dynamics. To mitigate the sensitivity of the Poisson algorithm to normal noise, a feature-preserving smoothing strategy is introduced to jointly optimize point positions and normal vector fields. This approach effectively suppresses high-frequency noise while preserving the geometric continuity of critical aerodynamic features, such as the wing trailing edge. Subsequently, in the deformation phase, control volumes are constructed using FFD technology without the need for original parameterized geometric models, enabling the parametric manipulation of key aerodynamic shapes including wing twist and dihedral. Finally, utilizing the meshless Lattice Boltzmann Method (LBM), the reconstructed geometry is directly employed for multi-condition flow simulations to analyze the response patterns of aerodynamic parameters and flow structures before and after deformation. Results demonstrate that this framework reconstructs high-fidelity watertight geometric models from high-precision scanned data and facilitates parameterized geometric deformation, offering a robust engineering pathway for digital twin construction and multidisciplinary performance assessment of complex aircraft configurations.
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