Solid rocket motors (SRMs) are widely used in the aerospace and missile industries. The interface debonding between propellant/insulator/case determines the life of an SRM. Therefore, monitoring and further locating interface debonding are crucial to prevent catastrophic structural failures of an SRM. The electromechanical impedance (EMI) technique based on piezoelectric wafer transducers is a simple and sensitive structural health monitoring (SHM) method. In this study, a high-precision probabilistic imaging method of EMI for the debonding monitoring of the SRM case–insulator interface is proposed. In this method, the Gaussian window function is used to describe the variation of debonding damage index with the position. In addition, an experimental procedure based on simulated damage is presented to determine the parameter in the Gaussian window function. Finite element models of the steel case–insulator structure based on Rayleigh damping are established to analyze the effects of debonding and simulated damage on EMI signals. An experiment is conducted to verify the proposed probabilistic imaging method and the finite element analysis results. It is proved that the proposed probability method can achieve high-precision imaging of debonding of SRM case–insulator, even after several months of storage.
The stress wave generated by impact or dynamic load will produce significant reflection and transmission at the rock coal or rock interface during the propagation process. This will produce dynamic effects such as dynamic tensile, stress superposition and mutation. These dynamic effects will lead to obvious vibration at the interfaces, which is a key factor leading to dynamic damage and the failure of coal and rock mass. In the process of underground engineering excavation, the dynamic damage of a series of layered rock masses is one of the important factors causing geological disasters. Based on the two–dimensional similar material simulation experiment, the coal and rock mass combined of five layers of fine sandstone, medium sandstone, coal, coarse sandstone and mudstone was taken as the research object, and single and multi-point excitation (synchronous/step-by-step) were used to test the time–history vibration curves of rock–coal and rock–rock interfaces under impact load. It was concluded that the change of extreme value of vibration amplitude presented two stages: first increase, and then attenuation. Most of them required 2.25 cycles to reach the peak value, and the dynamic attenuation of amplitude conformed to the law of exponential. Based on Fast Fourier transform (FFT), the spectrum structures of the amplitude–frequency of interface vibration were studied, and the two predominant frequencies were 48.9~53.7 Hz and 92.4 Hz, respectively. Based on the Hilbert-Huang transform and energy equation, 5~7 vibration modes (IMF) were obtained by decomposing the time–history curves. The three modes, IMF1, IMF2, and IMF3, contained high energy and were effective vibration modes. IMF2 accounted for the highest proportion and was the main vibration mode whose predominant frequencies were concentrated in 45.6~50.2 Hz. Therefore, IMF2 played a decisive role in the whole vibration process and had an important impact on the dynamic response, damage and failure of coal and rock mass. In real conditions, the actual predominant frequencies can be converted according to the size and mechanical properties of the coal and rock mass, and the vibration response characteristics of the interfaces between coal and rock mass under impact load were preliminarily revealed. This study can provide reference for monitoring and early warning of coal and rock dynamic disasters, prevention and control of coal and gas outburst and technical development.
As a critical part of prognostics and health management (PHM), remaining useful life (RUL) prediction can provide manufacturers and users with system lifetime information and improve the reliability of maintainable systems. Particle filters (PFs) are powerful tools for RUL prediction because they can represent the uncertainty of results well. However, due to the lack of measurement data, the parameters of the measurement model cannot be updated during the long-term prediction process. Additionally, for complex systems, the measurement model of a system often cannot be obtained in an analytical form. In this paper, a fusion prognostic method based on an extended belief rule base (EBRB) and a PF is designed to solve these problems. In the proposed framework, a double-layer maximum mean discrepancy-extended belief rule base (DMMD-EBRB) model with time delay is adopted to estimate and predict the hidden behavior of a degrading system. The unknown parameters of the degradation model are identified by the PF using the output of the EBRB. Afterwards, the system state is further predicted by the PF. The effectiveness of the proposed method is validated with the NASA-PCoE and CALCE lithium-ion battery degradation experiment datasets. In addition, several other related fusion methods are investigated for comparison with the proposed method. The experiments show that the proposed method yields better performance than the existing methods.
The mass density of normal acoustic materials is usually a real number. Energy dissipation is induced by the volume or shape change of the materials, which relates to the imaginary parts of complex moduli. Here, we show that by using acoustic metamaterials, complex dynamic mass density can also be realized. In this case, energy dissipation is induced by the change of momentum of the material. We analyze the physical origin of such complex mass density and provide a theoretical approach to calculate the effective complex dynamic mass density for acoustic metamaterials with dissipation. The obtained effective complex mass density is verified by finite element simulations, including both transmission studies and realization of coherent perfect absorption. Our work shows a way to realize complex mass density, which has important applications in enhancing absorption of acoustic waves.
Reinforced concrete (RC) bent structures are widely used in single-story industrial buildings, with a considerable portion of them located in high-intensity seismic areas. Therefore, it is significant to study the seismic performance of the RC bent column while keeping construction costs in mind. To evaluate the seismic performance of the RC bent column from an economic perspective, pseudo-static tests combined with construction cost analysis were first carried out on three RC bent columns designed according to different versions of Chinese codes. A numerical simulation method based on the extended finite element method (XFEM) for the RC bent column was then established and verified. Subsequently, the relationship between the local damage index related to the apparent damage characteristics and the overall damage index was established by conducting numerical analysis on RC bent columns with different design parameters. Based on the established relationship between the local and overall damage indexes, a calculation method for the residual bearing capacity based on apparent damage was developed. The results show that, when compared to the constructional requirements in the Chinese code for seismic design of industrial and civil buildings published in 1978 (TJ11-78), the constructional requirements in the Chinese code for seismic design of buildings published in 1989 (GBJ11-89) can effectively reduce the stress concentration at the column bottom and delay the formation of the plastic hinge with minimal cost increase, while the constructional requirements in the Chinese code for seismic design of buildings published in 2016 (GB50011-2010) can further effectively reduce the damage to the upper column. Besides, the constructional requirements in GBJ11-89 and GB50011-2010 can improve the energy dissipation and maximum bearing capacity of the RC bent column while decrease the cost/ductility coefficient by more than 5.0%. The proposed calculation method can effectively predict the residual bearing capacity of earthquake-damaged RC bent columns by comparing the calculation results to the test results.
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