To effectively predict the flutter critical velocity in the subcritical speed range, this study focuses on the analysis of the turbulent natural excitation response signals of wings in wind tunnel flutter tests. A flutter boundary prediction method is proposed using Long Short-Term Memory (LSTM) networks for flutter feature extraction from the time-frequency spectra of turbulent excitation response signals. The response data is transformed into the time-frequency domain through continuous wavelet transform, and LSTM networks are employed to establish a classification model for response data under different experimental conditions. Additionally, regression models are developed for response data at different wind speeds under the same experimental condition. By combining the two models and analyzing the flutter data, weighted calculations are performed to estimate the current flutter state, thereby predicting the critical velocity. The results demonstrate that compared to traditional flutter boundary prediction methods that utilize modal parameters, as well as flutter estimation methods employing spectral features and conventional classification models, the proposed approach enhances robustness of flutter estimation. Additionally, it improves estimation accuracy, thereby reducing the risk associated with flutter wind tunnel tests and flight tests.

A data-driven approach for flutter boundary prediction is proposed, which adopts a deep learning model to extract flutter features from measured signals, thereby enabling the analysis of aeroelastic system stability. Following a modeling strategy that leverages structural acceleration response signals, a dataset is constructed from experimental data acquired from wind tunnel tests, and a multidimensional feature system and a set of comparative models are subsequently established for performance evaluation. Comparative analytical results reveal that the integration of power spectral density (PSD) with the Bayesian optimization Transformer-long short-term memory hybrid model can markedly enhance the efficacy of feature extraction and the accuracy of flutter boundary prediction. Experimental validation demonstrates that the proposed method achieves a mean prediction error of 3.73% for unseen working conditions of the wind tunnel model and 4.14% for data obtained from a single flight test sortie. Furthermore, a prediction error below 10% is achievable at approximately 70% of the critical flutter speed, which could contribute to enhanced early warning performance in flutter tests.

A flexible skin structure based on jujube core shaped double-layer deformable honeycomb is proposed, which consists of jujube core shaped double-layer deformable honeycomb and flexible panel. Structural performance of flexible skin mainly depends on the deformable honeycomb, and flexible panel is used to maintain the smoothness and flatness of skin surface. Jujube core shaped double-layer deformable honeycomb is formed by staggered bending connection of curved plates at two different layers, which can simultaneously possess good in-plane uniaxial, biaxial and shear deformation capability, realizing various shape changes of morphing wing and adapting to multiple mission requirements. Mechanical properties of this double-layer deformable honeycomb were compared with single-layer ones. The results show that jujube core shaped double-layer deformable honeycomb has better in-plane deformability, while maintaining high out-of-plane stiffness, having more superior mechanical properties. In-plane deformation and out-of-plane bending resistance of jujube core shaped double-layer deformable honeycomb were studied. Theoretical model of the structure was established and verified by simulation and experiment, and influence of geometric parameters on in-plane and out-of-plane capabilities was analyzed. Surface deformation characteristic of the flexible skin was also studied. Relationship between local deformation of flexible panel in honeycomb pore and parameters of the flexible skin was derived. A flexible filling scheme is proposed to further improve local out-of-plane load-bearing capacity of the flexible skin. By filling honeycomb pores with silica gel foam, the foamed silica gel and the silica gel panel are connected into a whole to obtain a flexible skin with smooth surface.

A jujube core shaped double-layer deformable honeycomb structure is proposed, which is composed of curved plates in two different layers in a staggered connection manner. It can have good in-plane uniaxial, biaxial and shear deformation capability at the same time, realizing various shape changes of morphing wing to perform multiple missions. Theoretical models of in-plane equivalent tensile and shear moduli of the structure were established and verified by finite element analysis and experimental test. Influence of geometric parameters of the unit cell on equivalent moduli was studied. Mechanical properties of this double-layer deformable honeycomb with single-layer ones were compared. The results reveal that the jujube core shaped double-layer deformable honeycomb possesses better in-plane deformation ability, while maintaining high out-of-plane stiffness. This double-layer deformable honeycomb shows great potential as flexible structure for multiple in-plane morphing applications.

Flexible skin is an indispensable part of the morphing wing. Researchers proposed many kinds of flexible skin with different properties for different deformation modes and different deformation parts of the morphing wing. In this paper, a new type of double-layer honeycomb structure was proposed. The structural composition and deformation mechanism of double-layer deformable honeycomb were expounded. The in-plane and out-of-plane properties of double-layer deformable honeycomb flexible skin were studied by theoretical, finite element simulation methods, and experimental studies. The proposed flexible skin exhibits characteristics of large in-plane deformation, near-zero Poisson’s ratio, and sufficient out-of-plane load-bearing capacity. A parametric structural analysis was conducted, and the parameters were tailored to meet the application requirements of seamless trailing edge flaps. The designed flexible skin not only satisfies the requirement for large-angle deflection of trailing edge flaps with a smooth and continuous surface but also demonstrates a 3.0% increase in lift coefficient and a 2.8% improvement in lift-to-drag ratio compared to conventional seamless trailing edge flap.

In this paper, a classification method of flutter test signals based on convolutional neural network (CNN) and Hilbert-Huang transform (HHT) is established, which can be effectively applied to flutter boundary prediction. This method combines convolutional neural network and time-frequency analysis. Firstly, the flutter test signal is preprocessed by Hilbert-Huang transform and labeled according to the actual signal source. The label contains channel information and flutter information. All the signals and labels are composed of the dataset, the dataset is randomly scrambled and 80% of the number is taken as the training set, and the features are extracted, and the classification model is trained by convolutional neural network. The remaining 20% of the dataset is taken as the test set. The test set is used to test the classification model and verify the reliability and accuracy of the model. The accuracy of the final test set is above 90%, which indicates that the model trained by this method can effectively identify the channel information and the flutter information of the signal.

Aiming at the problem that the outdoor pointer instrument dial is easily obscured by environmental factors, an instrument reading recognition method based on two-dimensional convolution and calculus accumulation is proposed. A weighted multi-feature matching algorithm is proposed to improve the success rate of template matching and accurately obtain the center coordinates of the rotating shaft. The dial image is transformed by polar coordinate transformation and calculus accumulation. Finally, the maximum value is the position of the target pointer, which is automatically read in the two-axis coordinate system. Experiments show that the error compared with the actual values is <0.2%, with good robustness under harsh conditions such as too strong and too dark light intensity and occlusion on the dial.

The folding-wing aircraft can obtain appropriate lift and drag by changing the folding angle of the wing. At different stages of the aircraft executing tasks, there can be a corresponding flight state. When the carrier -based aircraft is parked, the occupation space can be reduced by folding by the wing, which can increase the number of carrier -based aircraft. The folding wing drive mechanism is a key technology for folding the wing, which has a key impact on the characteristics of structural transmission. The driving force of the traditional folding wing mechanism is positively related to the size of the drive. Therefore, in the traditional folding wing drive mechanism, if the driving force required when the wing is folded is large, it must use a large size actuators, but this is often limited by the space at the wing shaft. To solve this problem, a folding wing auxiliary drive mechanism is designed in this article. This mechanism uses spring deformation to store the gravity of the wing unfolding process as elastic potential energy and release it when the wing is folded. The use of this mechanism not only increases the driving capacity of the folding wing drive mechanism, but also reduces the power requirement of the main drive. In order to convert the rotation movement when folding the wing into a linear motion, a rotary-to-linear device is designed in this article. In order to eliminate the restrictions on the working load and displacement itinerary in the spring design, this article designed a new type of energy storage spring device: a solid-liquid hybrid spring device, and two spring design schemes are given. Based on the demand of force and displacement strokes, this article gives a detailed design of the auxiliary drive mechanism, and a detailed description of the structural layout and specific operation method is given. On this basis, a key parameter of the auxiliary drive mechanism is given. Finally, the renderings of the auxiliary drive when the wing folding and unfolding are shown.

The study investigates flutter boundaries and auto-regressive moving average model (ARMA) stability analysis through numerical simulation cases. Two wind tunnel flutter tests were conducted to analyze the influence of parameter identification errors on boundary prediction accuracy. The research findings indicate: (a) Damping errors have smaller impact than frequency errors on the criterion of flutter boundaries. The criterion in the flutter boundary method is more insensitive to damping errors than the criterion in the ARMA stability analysis method. (b) For wing bending-torsion coupled mode flutter, the criteria of these two methods exhibit similar decreasing trend, which helps to predict flutter boundaries in advance. (c) For planar bending flutter, the ARMA stability analysis method may be more suitable than the flutter boundary method.

This paper implements modal parameter identification of the wing response signal under environmental excitation using continuous wavelet transform. A suitable wavelet basis function was selected based on the characteristics of flutter data to achieve good time-frequency analysis of the data. The endpoint effect was effectively suppressed by using support vector machine regression. By employing the Crazy Climbing algorithm to identify the wavelet ridges and obtain the wavelet cross section, the modal damping of flutter data was identified. Finally, this method was applied to wind tunnel test data of a 3D printed wing to identify its modal parameters, and the results were compared with numerical simulation data, which validated the reliability of Continuous Wavelet Transform-Based Analysis of Aircraft Wing Flutter Data (CWT) in identifying modal parameters of wing flutter data under environmental excitation.

A flutter boundary prediction method based on HHT, and machine learning is proposed to predict the flutter velocity before the wind speed reaches the subcritical state. Natural excitation technique is used to extract impulse response signals. EMD (empirical Mode decomposition method) is used to decompose the signal. Hilbert spectrum was obtained and analyzed by HHT to decompose the signal. The analysis methods included HHT spectrum and marginal spectrum analysis, so as to extract the characteristic quantity and establish the classification model according to different flight states. Then, regression models were established under different flutter modes for flutter degree analysis. During the prediction, according to the classification performance of the data to be measured, the flutter degree analysis result is weighted to obtain the flutter degree corresponding to the current wind speed, and then the flutter wind speed is calculated. In the selection of machine learning algorithm, naive Bayes algorithm, K-nearest neighbor algorithm and other machine learning algorithms are used to construct the classification model, linear regression, Gaussian process regression and so on are used to construct the regression model. The results show that the K-nearest neighbor algorithm performs best in the classification algorithm, while the Gaussian process regression algorithm performs best in the regression algorithm. Through the cross-validation of the test data, the proposed method can accurately predict the critical flutter velocity when it is far away from the flutter boundary through flutter mode recognition and flutter degree analysis.

In order to predict the flutter boundary of the wing under the action of atmospheric turbulence, the obtained turbulence signal is first denoised, and the attenuation signal containing a single mode is obtained by using the variational modal decomposition method. The data containing few attenuation points are extended by using machine learning methods, and the matrix pencil method is used to identify the modes. Finally, the stability criterion is calculated by using the modal parameters, The flutter boundary is calculated from these data. In this paper, the wind tunnel test data are used to analyze and solve the modal, and the flutter boundary is accurately predicted in advance.

The trailing edge of the variable camber wing is mainly composed of flexible skin and deformable ribs. The connection method of the skin and ribs will directly affect the performance of the wing. The use of corrugated flexible skin and segmented ribs can achieve the continuous, uniform and coordinated deformation of the trailing edge. This paper adopts an origami-style connection structure for the corrugated flexible skin and segmented ribs, which can store and transfer deformations, match the skin and rib segments geometrically, and transfer the load to the ribs evenly. The calculation results show that the variable camber trailing edge of this structure has good deformation capacity and high out-of-plane load-bearing capacity.

In flutter tests, particularly in wind tunnel experiments, the aircraft model is generally excited by atmospheric turbulence, which increases the difficulty in precisely identifying the modal parameters. To estimate the modal parameters under turbulence excitation for flutter boundary prediction, a technique was developed and evaluated depending on the Hilbert-Huang transform in this paper. The results of simulated flutter cases show that the developed technique can identify modal frequencies more precisely than the modal damping ratio, while the estimation of the modal damping ratio is quite good. Finally, in a wind tunnel flutter test, good flutter boundaries were predicted in advance by using the modal parameters identified from the turbulence response at low airspeeds.

Purpose Adaptive bump inlet can adaptively change the shape of inlet bump surface according to the flight speed of aircraft, ensuring that the inlet has good inlet-engine match performance in a wide speed range. This paper aims to use a composite flexible skin reinforced by shape memory alloy (SMA) fiber as the deformable structure at bump surface to realize the adjustable bump surface of adaptive bump inlet. Design/methodology/approach According to the deformation and load-bearing requirements of adaptive bump, SMA is applied to the design of adaptive bump inlet due to its characteristic of super-elasticity. A kind of SMA fiber is studied. A composite flexible skin reinforced by SMA is proposed, and its mechanical properties are analyzed. On this basis, an adaptive bump inlet is designed in which the composite flexible skin reinforced by SMA is used as bump surface, and the shape of the bump surface is adjusted by way of pressuring. The design scheme and specific parameters of the adaptive bump are given. Findings An adaptive bump surface that meets the design requirements of the inlet is designed, which can effectively adjust the inlet throat area with a throat area change rate of 20%. Originality/value An adaptive bump inlet with composite flexible skin as a deformable structure at bump surface is designed, and SMA is applied as the reinforcing fiber.

A new method of mode parameter identification based on Extreme-point Symmetric Mode Decomposition (ESMD) and Matrix Pencil Method (MPM) is proposed for processing wind tunnel test data.The proposed method first decomposes the test data to a series of narrow-band signals by band-pass filtering.Then, the ESMD method is used to perform modal decomposition to obtain several single-mode response signals.Next, each singlemode response signal is processed using Natural Excitation Technique(NExT) to obtain a free attenuation response signals.Finally, the mode parameters were identified by the MPM.After the verification of simulation data, the proposed method is applied to identifying the mode parameters of the wind tunnel test data, and the results are compared with the mode parameter identification results based on the empirical mode decomposition (Empirical Mode Decomposition, EMD). The results show that the proposed method can better identify the mode parameters of the structure from the wind tunnel test data with good applicability and sufficient identification accuracy.

In this paper, a squid-like jet propeller actuated by piezoelectric pumps is designed, which can realize underwater pulsedjet process. The squid-like jet propeller comprises a bionic mantle and a rigid framework. The bionic mantle is a sealedflexible cavity, and a plurality of piezoelectric pumps are embedded on the bionic mantle to continuously absorbwaterinto the cavity. When the piezoelectric pump works, water is sucked into the flexible cavity, when the pressure inthecavity reaches a certain value, the jet is ejected through the nozzle. Firstly, this paper studies the structural designof thebionic mantle, then studies the influence of the number and the water absorption performance of piezoelectric pumpsaswell as water absorption time on the propulsion performance of the squid-like jet propeller, and then studies thestructural deformation of the bionic mantle and the variation of the parameters of the jet propeller during the pulsedjet process. The squid-like jet propeller can control the pulsed jet cycle and other propulsion performance.

In order to predict the flutter boundary of the wing under turbulent excitation, wavelet decomposition is used to preprocess the signal, and the free attenuation signal is extracted based on CEEMDAN and natural excitation technology. The matrix pencil method is used to identify the modal parameters. Finally, the Z-W method is used to determine the aircraft loss stability, fit the change curve of the judgment and extrapolate the flutter boundary. The modal parameters of the simulated turbulence excitation signal are identified, the numerical simulation of the flat wing model is carried out, and the wind tunnel flutter test data of a single wing model are calculated. The results show that: using matrix pencil method to process the free attenuation signal obtained by ceemdan, wavelet de-noising and natural excitation technology, the modal parameters of turbulent excitation response can be identified more accurately, combined with Z-W method, the flutter boundary can be predicted in the case of early wind speed, and the test safety can be improved.

A novel global parametric system identification framework is introduced in this work for aeroelastic modeling under varying flight states. The presented framework is based on the functionally pooled (FP) time-series models that enable explicit analytical inclusion of any admissible flight states into the model parameters and thus the system dynamics. In this paper, the autoregressive (AR) type of the FP model, which is designated as the FP-AR, is employed to interpret the aerodynamics of a wing structure. The data were recorded by accelerometers during a dedicated wind-tunnel flutter test with the airspeed increasing all the way to the flutter boundary. Including the varying flight states defined by the increasing airspeed into the system modeling via the FP technique, the global framework provides a more sophisticated identification results compared with the traditional nonparametric Welch-based spectral estimation. Flutter occurrence is indicated by the stability margin of the aeroelastic system evaluated by the estimated FP-AR parameters based on Jury's stability criterion. For online application purpose, an iterative state-based FP-AR process is also proposed in this paper. Compared with the standard FP-AR modeling, the iterative realization is a developing process that provides a global approximation of the system dynamics at each flight state. Experimental evaluation demonstrates the feasibility and effectiveness of the proposed global framework in aeroelastic modeling while facilitating an accurate flutter boundary prediction.

The variable camber wing can significantly improve the aerodynamic characteristics of the aircraft and is an important form of morphing aircraft. Flexible skin technology is one of the key technologies. According to the skin deformation features of the variable camber wing, a flexible skin form is proposed in this paper. The fishbone-shaped reinforcing structure (FBRS) is applied as the main component of the flexible skin to bear aerodynamic loads. Rubber material with excellent deformation ability wraps the FBRS to obtain a smooth and flat skin surface. Thorn-shaped branches on adjacent FBRSs are arranged in a staggered manner. In order to increase the out-of-plane stiffness of the flexible skin, the flexible skin needs to be used in combination with the corrugated structure. Each wave crest of the corrugated structure is connected with the FBRS of the flexible skin. By setting the wave crest of the corrugated structure into a platform shape, a stable connection between the FBRS and the corrugated structure is maintained. In this paper, the stiffness expressions of FBRS and corrugated structure are derived. The chordwise deformation capacity and out-of-plane bearing capacity of the flexible skin are verified by the method of finite element simulation. The results show that the FBRS can transmit aerodynamic loads well and maintain the smoothness and flatness of the rubber surface. Supported by a corrugated structure, this type of flexible skin has good chordwise deformation ability and high out-of-plane bearing capacity.