This study investigates and compares various multivariate regression methods, including principal component regression (PCR) and partial least squares regression (PLSR), for flight load analysis and demonstrates their high learning efficiency and strong generalization capabilities, making them highly suitable for this purpose. The flight load data of a civil aircraft use altitude, Mach number and load factors as input parameters, which are used as sample data to establish regression models for predicting wing loads under different flight conditions. The accuracy of all regressions are confirmed through evaluation, with PLSR being the most efficient. In the comparison of computational times, it was found that the computational efficiency of regression methods was significantly superior to traditional panel methods. The flight load calculation shows that PCR and PLSR can significantly improve analysis efficiency and provide new insights into efficient flight load analysis.

Flight load calculation, an important step in aircraft design and optimization, typically involves millions of computations and requires significant computing resources and time. Improving the efficiency of flight load calculations while maintaining accuracy is therefore of great significance for shortening research and development cycles. This study investigated and compared multiple algorithms, including the neural network model, the Kriging surrogate model, and the neural network residual Kriging (NNRK) model, for flight load analysis. The accuracies of all models were confirmed through evaluation, with NNRK being the most efficient, making it highly suitable for flight load analysis. The flight load data of a civil aircraft, including the total weight, the center of gravity, the pitch moment of inertia, the altitude, the Mach number, the airspeed, the velocity pressure, the pitch rate, the load factor, and the angle of attack as input parameters, were used as sample data to establish models, for predicting wing loads under different flight conditions. The accuracies of all regressions were confirmed through evaluation, with NNRK being the most efficient. The flight load calculation shows that NNRK can significantly improve analysis efficiency and provide new insights into efficient and comprehensive flight load analysis.