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.

The aerodynamic noise of an aircraft leads to vibration fatigue damage to structures. Herein, a prediction method for aircraft surface noise under the comprehensive effect of mixed acoustic sources during flight, primarily surface aerodynamic, air intake, and tail nozzle jet noises, was studied. In the supersonic cruising state, the internal and external flow fields of the aircraft were solved using the Reynolds-averaged Navier–Stokes equations to obtain the statistical average solution of the initial turbulence. The non-linear disturbance equation was used to obtain the surface acoustic load of the aircraft. The calculation results revealed that the main source of aircraft surface noise is aerodynamic noise. The sound pressure level on the fuselage increases gradually from front to rear along the aircraft, and the OASPL at the air intake and tail nozzle is relatively large. The jet noise has little effect on the sound pressure level at the front of the fuselage and only contributes to the OASPL at the tail nozzle of the fuselage. The intensity of pressure pulsations from the engine exhaust in the tail section is 93.3% of the total intensity of pressure pulsations.

Monitoring the vibration responses of structures accurately and efficiently is the key point of structural health monitoring (SHM). The monitoring of structural vibration responses depends on the sensor system. Therefore, discovering critical sensor positions is a challenging but beneficial task for SHM. Unfortunately, the predominant approaches only focus on predicting the vibration responses and lack the capability to identify meaningful sensor positions, which inevitably restricts their predictive capabilities. Furthermore, the strong linear correlation between sensors results in instability when selecting sensor locations, which leads to the difficulty of sensor optimization. To bridge this gap, we propose a sparse learning method with feature selection (SLMFS) to identify meaningful and interpretable sensor positions. In this method, to facilitate interpretation and stability, we use sparsity-inducing penalties to select the important sensors at both individual and group levels. In addition, we introduce independent regularization (IR) for stable and consistent feature selection. We also present an efficient iterative optimization algorithm to address the SLMFS, which is guaranteed to converge to the global optimum. The synthetic data, simulation data and laboratory data are used to validate the effectiveness and accuracy of SLMFS. The results show that sensor selection and response prediction mutually reinforce each other. On one hand, the guidance from sensors further ensures the response prediction produces a good representation. On the other hand, the good representation enables more precise selection of target response-related sensors. Therefore, our learning method could improve the efficiency and accuracy of response prediction

In order to realize the continuous chord bending of the wing and consider the material deformation limitation, a trailing-edge curvature variable wing section combining rigid and flexible structures is proposed. In the wing configuration design, the optimal lift-to-drag ratio is used as the optimization objective, and the wing section mean line is parameterized to obtain the optimal rigid-flexible hybrid deformation configuration. The aerodynamic characteristics of hybrid rigid-flexible deflection wing and traditional rigid deflection wing were compared by flow field calculation. Under different angles of attack, the hybrid deflection airfoil has better aerodynamic performance, with the lift coefficient increasing by up to 1.66 times and the lift-to-drag ratio increasing by up to 2.86 times. Under various flight conditions, the rigid-flexible hybrid wing requires a smaller deflection angle and a better wing configuration than the traditional wing. Under high-angle deflection conditions such as landing, the lift-to-drag ratio of the rigid-flexible hybrid wing is increased by 78%, while delaying flow separation (x/c = 0.8) and reducing trailing-edge vortices, thereby improving aerodynamic efficiency. Additionally, for the flexible deformation part in the rigid-flexible hybrid wing, pneumatic muscles are used as flexible actuators, and a proportional integral sliding mode control method based on a nominal model is established. A test platform was built to control the deformation of the flexible part of the wing rib, and the ability of the flexible part to achieve the target curvature within the range of elastic deformation was verified. The actual deformation curve is very consistent with the target deformation curve. The dynamic performance test of the control method and parameters proves the rationality and effectiveness of the control system design.

In this paper, a thermal–mechanical–oxidation coupling experimental system based on laser heating is developed, containing two modes of Gaussian and flat-top lasers, which has a series of advantages such as high temperature range, rapid heating rate, and convenient observation. The system adopts active illumination and an optical filter to solve the problem where it is difficult for traditional digital-image correlation technology to image clearly under laser heating. A biaxial mechanical test machine is used to simulate the complex load by applying biaxial tension or compression loads on the material. Combined with the radiation temperature measurement and controllable flow field device, the thermal-mechanical-oxygen coupling experiment of high temperature resistant materials under aerobic environment can be carried out. The maximum uniform heat flux output density is 27.2 kW/cm2, and the maximum Gaussian heat flux output density is 105 kW/cm2. The thermal-mechanical-oxygen coupling experimental system and method are of great significance to the development of new high temperature resistant materials and thermal barrier coatings.