Response prediction is a fundamental yet challenging task in aeronautical engineering, requiring an accurate selection of sensor positions correlated with the target responses to achieve precise predictions. Unfortunately, in large-scale structures, the rigorous selection of reliable sensor candidates for multi-target responses remains largely unexplored. In this paper, we propose a flexible and generalized framework for selecting the most relevant sensors to the multi-target response and predicting the target response, referred to as the Fast-aware Multi-Target Response Prediction (FMTRP) approach in the spirit of divide-and-conquer. Specifically, first, a multi-task learning module is designed to predict multi-point response tasks at the same time. Simultaneously, we meticulously devise adaptive mechanisms to facilitate loss-term reweighting and encourage prioritization of challenging tasks in multiple prediction tasks. Second, to ensure ease of interpretation, we introduce a hybrid penalty to select sensors at the group-sparsity, individual-sparsity and element-sparsity levels. Finally, due to the substantial number of candidate sensors posing a significant computational burden, we develop a more efficient search strategy and support computation to make the proposed approach applicable in practice, leading to substantial runtime improvements. Extensive experiments on aircraft standard model response datasets and large airliner test flight datasets validate the effectiveness of the proposed approach in identifying sensor locations and simultaneously predicting responses at multiple points. Compared to state-of-the-art methods, the proposed approach achieves an accuracy of over 99% in sinusoidal excitation and exhibits the shortest runtime (3.514 s).

A multi-body dynamic rigid-flexible coupling model of landing gear is established to study the gear walk instability caused by the friction characteristics of the brake disc. After validating the model with the experimental results, the influence of the landing gear structure and braking system parameters on gear walk is further investigated. Among the above factors, the slope of the graph for the friction coefficient of the brake disc and the relative velocity of brake stators and rotors is the most influential factor on gear walk instability. Phase trajectory analysis verifies that gear walk occurs when the coupling of multiple factors causes the system to exhibit an equivalent negative damping trend. To consider a more realistic braking case, a back propagation neural network method is employed to describe the nonlinear behavior of the friction coefficient of the brake disc. With the realistic nonlinear model of the friction coefficient, the maximum error in predicting the braking torque is less than 10% and the effect of the brake disc temperature on gear walk is performed. The results reveal that a more negative friction slope may contribute to a more severe unstable gear walk, and reducing the braking pressure is an effective approach to avoid gear walk, which provides help for future braking system design.