With the continuous development of aerospace industry, the load transfer structure of spacecraft is getting more and more complicated. In practical engineering, how to distribute the loads in a reasonable way is of great significance for the lightweight design of spacecraft and the guarantee of structural load carrying capacity, and it is also necessary to take into account the non-ideal boundary conditions of the overall structure to the local structure in the process of dynamic load transfer structure design. Based on this, this paper proposes a dynamic topology optimization design method for dynamic load transfer structure based on structural boundary condition equivalence, which can fully consider the influence of the overall structure on the local structure while designing the load transfer structure. The method firstly simplifies the connection boundary between the local structure and the overall structure into a spring unit and a centralized mass unit, optimizes the unit parameters through genetic algorithm to achieve the equivalence of boundary conditions, and finally establishes a topology optimization model of the load transfer structure based on the unit density variable in conjunction with the design objective of the dynamic compliance of the structure. Numerical examples verify the effectiveness of the new method and obtain the optimization design results with the variation of volume fraction, external load frequency and load constraint interval.

Considering the random uncertainty of landing gear parameters in design and manufacturing, this study conducts a quantitative study on the random response of carrier-based aircraft landing impact and the buffering performance of landing gear. This study established a dynamic model of the landing vibration of a carrier-based aircraft’s main landing gear. Based on the statistical characteristics of some filling parameters of the landing gear buffer (such as the initial volume of the air chamber, pressure oil area, oil shrinkage coefficient, etc.), the representative point set is divided by the direct probability integration method (DPIM), and the deterministic structural equation and probability density integration equation on the representative point set are solved. At the same time, the accuracy of DPIM application in the landing gear stochastic model is demonstrated by using the Monte Carlo Simulation (MCS) method. By outputting the buffer stroke of the landing gear, the vertical tire force, and the axial force of the strut, the mean value, standard deviation, probability density function, and related features of these responses are obtained. It is found that although the distribution of these responses are concentrated near the mean value, there is still a possibility of significant responses leading to system failure. Therefore, a functional function is defined using the buffer stroke, vertical tire force, and the axial force of the strut, and a reliability evaluation study is conducted on the landing gear structure under different threshold values.

With the advantages of small size, light weight, flexible design and excellent frequency selectivity, surface acoustic wave devices are widely used in radar, communication, non-destructive testing, electronic countermeasures, TV signal processing and other fields. For exploring the application of surface acoustic wave (SAW) devices in mass sensing, the Love wave propagation in a piezoelectric layered structure is systematically investigated from perspectives of theoretical analysis and numerical examples. As for the theoretical model consisting of an additional mass layer, a piezoelectric sensing layer and a semi-infinite elastic half-space, the exact solution that simultaneously satisfy the dynamic governing equations and the continuous conditions between layers is established, and the phase velocity of Love waves is obtained.Then,the three-layer structure is degenerated into two-layer structure by stepwise degradation method, and the correctness of the theory is verified by comparing with the results of previous paper. After validation, the influence of structural and material parameters of the additional mass layer on Love wave phase velocity is conducted, including the thickness, shear modulus, density, and dielectric coefficient. Finally, an approximate method with only consideration of the inertial effect of the additional mass layer is developed, with its applicable condition demonstrated. It is revealed via numerical examples that the Love wave is very sensitive to the thickness of the additional mass layer, while the dielectric coefficient has minimal influence on the phase velocity. Additionally, the phase velocity decreases linearly when the density of the additional mass layer increases. The approximate method proposed in this paper exhibits good universality, which simplifies the wave solving, and can possess high computational accuracy when the additional mass layer is thin. The results and methods in this paper can provide guidance for the application of SAW devices in mass sensing.

Bearing fault diagnosis is an important research topic in aviation engine prediction and health management. Signal processing algorithms and deep learning models in this field rely on datasets. However, publicly available datasets generally cover narrow speed ranges, large speed intervals, single loads, and a lack of composite fault data, making it difficult to support the practical development of fault diagnosis methods. This article discloses a vibration dataset of aircraft main shaft bearings with a wide speed range. In addition to providing single fault data, this dataset also provides multiple composite bearing fault data, covering multi-channel bearing vibration signals with a wide speed range under different loads. The dataset well supports the research of classic fault diagnosis algorithms, and due to the large speed range covered by the data and high-speed sampling rate, it is more conducive to training deep learning fault diagnosis models.

In order to effectively control the vibration transmitted by the elastic supports of the aero-engine rotor, an active magnetic dry friction damper (AMDFD) is employed to tune the support damping. On the basis of the traditional dynamics model of the dual rotor system, an AMDFD-dual rotor-bearing seat dynamics model that can characterize the transmitted vibration of the support is established. The effectiveness of AMDFD in suppressing the transmitted vibration of the rotor supports is simulated by using a speed interval switching controller and a model-free adaptive controller, and the intrinsic principle in realizing suppression is elucidated. Using the AMDFD-twin-rotor system test rig, the test of transmitted vibration control when rotor passes through the multi-order critical speeds was carried out. The results show that the AMDFD controlled by aforementioned two controllers can effectively reduce the transmitted vibration at each bearing position, and the reduction is more than 52%.

The present paper proposes an hybrid base isolation system referred to as BRB+NFVD+BIS, consisting of the buckling restrained braces (BRB), nonlinear fluid viscous dampers (NFVD), and base isolation system (BIS) to study both the damping and isolation effects of the hybrid base isolation system on prefabricated high-rise buildings. Defined are the ratios of both BRB yield strength to base isolation yield strength and the total damping index of NFVD to base isolation yield strength, respectively designate as BIR and NIR. Based on the dynamic elastic-plastic seismic response analysis of the corresponding systems, the effects of BIR, NIR, and NFVD parameters on the seismic performance of BRB+NFVD+BIS tall buildings have been revealed, and the ranges of BIR, NIR, and NFVD parameters are suggested. Results demonstrate that with respect to the non-isolated prefabricated high-rise buildings, the BRB+NFVD+BIS system can significantly enhance the seismic performance of beam-column connections, reduce both the inter-story drift ratios and floor accelerations of the superstructure. Compared with the base-isolated prefabricated high-rise structures, the BRB+NFVD+BIS system substantially reduces base isolation layer displacement while maintaining almost the same seismic performance to each other in terms of the beam-column connections, inter-story drift ratios, and floor accelerations Therefore, the BRB+NFVD+BIS system processes better displacement control ability of isolation layer. Simultaneously, the results show that the BRB+NFVD+BIS system has better robustness of both the seismic mitigation and isolation.

There exists the nonlinear failure correlation among the multiple monitoring points of bridge components. Considering the influence of this factor on the reliability indices of the bridge, this paper adopts the Bayesian optimized long short-term memory (BO-LSTM) network model in machine learning to dynamically predict the monitoring data of the bridge, and establishes a three-dimensional Gaussian Copula model based on Copula theory to calculate the time-varying reliability indices and failure probability of the bridge construction. The rationality of the model and method is verified by applying the monitoring data of Fumin Bridge in Tianjin.

The wedge-shaped oil film in sliding bearings induces uneven heating effects on the journal of a rapidly rotating rotor, resulting in circumferential temperature variations in the journal. The thermal bending caused by these temperature differences exacerbates rotor vibration, leading to a phenomenon known as ‘Morton effect’ or rotor thermal instability. This effect is particularly severe in cantilevered rotors. Initially, an elliptical bearing’s thermal fluid lubrication model is established, and its dynamic coefficients and oil film temperature field are calculated. Subsequently, based on Fourier heat conduction theory, using the obtained oil film temperature as a boundary condition, a finite element method is employed to solve the three-dimensional transient temperature field of the journal to determine the thermal deformation and thermal stress. The thermal stress is then integrated to obtain an equivalent moment for rotor dynamic analysis. Additionally, the sliding bearing oil film thickness is updated based on thermal deformation. Repeating these steps completes the fluid-solid-thermal multi-field coupling analysis of the rotor-bearing system, and the effectiveness of the simulation model is validated against experimental data. Finally, parameter analysis is conducted on the rotor-bearing system with the rotor’s cantilever length and suspended mass as variables. The results indicate that reducing the cantilever length or decreasing the suspended mass effectively reduces system vibration.

A novel data-driven method for simulating non-Gaussian stochastic processes is proposed in this paper. The sample conversion model and power spectrum conversion model are established by using artificial neural network models respectively. A neural network model is constructed based on sample data to transform Gaussian samples into non-Gaussian samples. The distribution function of the samples is modeled using the shifted generalized lognormal distribution, and the latent Gaussian power spectrum is directly obtained through the backpropagation neural network model. The Gaussian stochastic process samples are generated using the spectral representation method, and then transformed into non-Gaussian process samples using the sample conversion neural network model. This method is capable of generating non-Gaussian stochastic process samples based on limited sample data, addressing the challenge of determining latent Gaussian power spectrum, and solving the problems such as poor accuracy and limited application range of the central moments-based transformation models. Through numerical simulations and validation in turbulent wind fields, the accuracy and effectiveness of the proposed method are further demonstrated.

This study extends the filtered white noise model by proposing a time-frequency hybrid dimensionality reduction model for fully nonstationary seismic ground motion random fields, thereby overcoming the limitation of simulating only ground motion processes without capturing spatially distributed ground motion fields. Specifically, to address the difficulty in directly representing the spatial coherence of seismic random fields within the impulse response function of the filtered white noise model, a proper orthogonal decomposition (POD)-based dimensionality reduction simulation method is introduced. This approach enables a frequency-domain representation of spatially coherent white noise random vector processes. By applying the impulse response functions and modulation functions corresponding to different locations within the seismic random field to filter and modulate the respective white noise components, an efficient time–frequency hybrid dimensionality reduction representation of fully nonstationary seismic random fields is achieved. Numerical examples validate the accuracy and engineering applicability of the proposed model by comparing mean values, standard deviations, auto-/cross-correlation functions, as well as response spectra and coherence functions.