A modular transfer learning-based method for predicting high-load support reactions in full-scale static tests

To address the problem of neglecting load uncertainties and structural deformation in conventional support reaction calculations for full-scale aircraft static tests, this paper proposes a novel approach based on modular transfer learning for accurately predicting high-load support reactions. The proposed method introduces a modular transfer learning framework in which separate models are developed to account for loading uncertainties. A primary prediction model is first established using historical test data to extract general static testing characteristics. Subsequently, a residual correction model trained with low-load data from the current test condition is integrated, enabling adaptation and correction of prediction errors specific to the present testing scenario. Additionally, to explicitly consider structural deformation effects, a binomial regression technique is employed to estimate loading-point coordinates at high-load levels, facilitating accurate determination of loading directions. By combining these coordinates with spatial force equilibrium equations, precise prediction of support reactions is achieved. The effectiveness and accuracy of the proposed method are validated through a full-scale static test case involving a representative verification aircraft. Comparative analyses demonstrate that, compared with traditional rigid-body assumptions, the developed method significantly enhances the prediction accuracy for high-load stages, thereby providing robust theoretical and technical support for support reaction estimation in large-scale and complex testing environments.