Development of a window system with acoustic metamaterial for air and noise control

To improve window performances in reducing noise and allowing for air exchange, most current approaches focus on techniques such as double glazed and ducted designs, generally leading to bulky designs, visually non-optimised, and with narrow-banded frequency. In this research, window systems based on acoustic metamaterials (AMMs) are developed, and both natural air ventilation and acoustic performances are evaluated. The systems incorporate bistable auxetic metamaterials and acoustic origami metacage designs which are particularly interesting for their reconfigurable and deployable nature. Several design cases with different design features are examined, and a specific design is then selected for a parametric analysis using Finite Element Method (FEM) aiming to optimise the acoustical performance. It is demonstrated that significant improvement in acoustic performance can be obtained in terms of Transmission Loss (TL). The use of AMMs could lead to designs with manifold merits over traditional windows, including compact size with deployability, easy reconfigurability and installation, and thus paving new direction in ventilation window design

Modelling and simulation of the expansion of a shape memory polymer stent

Purpose – The purpose of this paper is to demonstrate the feasibility of using SMP for developing vascular stent. In particular the expansion performance is analyzed through extensive modeling and simulation. Design/methodology/approach –Firstly, we construct the model geometry and propose a constitutive model to describe the deformation of the stent due to the expansion process. We then simulate the expansion process under varying conditions, including different heating rates and recovery temperatures. Finally, we analyze the radial strength of the SMP stent. Findings –A less invasive and stable expansion performance of the SMP stent is confirmed by the simulation method. A fitting function of the expansion process is proposed based on the characteristics of the SMP. Research limitations/implications – The effects of dynamic blood flow on the SMP stent is ignored. A fluid-structure interaction analysis may need to be considered to give a more accurate description of the behavior of the SMP stent. Social implications – Our findings will provide guidance for the rational design and application of SMP stents. Originality/value – This is the first time that the expansion performance of a SMP stent has been analyzed qualitatively and quantitatively through modelling and simulation

Total Focusing Method for Imaging Defect in CFRP Composite with Anisotropy and Inhomogeneity

Fiber reinforced polymers (FRPs) are increasingly used in thick primary load-bearing structures, while manufacturing and in-service defects occur with a higher chance as the composite thickness increases, which entails the nondestructive detection and evaluation of potential structure defects. This study focuses on the imaging qualities of defects at different depth in thick FRPs via total focusing method (TFM), aiming at determining the optimum imaging strategy for thick FRPs (25 mm for discussion). Dynamic homogenization based on Floquet theory and numerical finite element analysis are performed to interrogate the wave propagation characteristics. The Frequency-dependent time correction method for TFM imaging (F-TFM) is proposed for accurate defect imaging in periodically layered crossply FRP. Finally, the results show that the proposed F-TFM method is able to detect and locate the defects of 2 mm size at all possible depth

A Bridge Vibration Measurement Method by UAVs based on CNNs ‎and Bayesian Optimization‎

A bridge vibration measurement method by Unmanned Aerial Vehicles (UAVs) based on a Convolutional Neural Network (CNN) and Bayesian Optimization (BO) is proposed. In the proposed method, the video of the bridge structure is collected by a UAV, then the reference points in the background of the bridge and the target points on the bridge in the video are tracked by the Kanade-Lucas-Tomasi (KLT) optical flow method, so that their coordinates can be obtained. The BO is used to find the optimal hyper-parameter combination of a CNN, and the CNN based on BO is used to correct the bridge displacement signal collected by the UAV. Finally, the natural frequency of the bridge is extracted by processing the corrected displacement signals with Operational Modal Analysis (OMA). Moreover, a steel truss is used as the experimental model. The number of reference points and the shooting time of the UAV with the optimal correction effect of the BO-based CNN are obtained by two groups of comparative experiments, and the influence of the distance between structure and reference points on the correction effect of the BO-based CNN is determined by another group of comparative experiment. The static reference points are not required for the proposed method, which evidently enhances the applicability of UAVs; the conclusion of this paper has great guiding significance for the actual bridge vibration measurement

Strong interlayer coupling in monoclinic GaTe

Recently, emerging intriguing physical properties have been unraveled in anisotropic layered semiconductors, with their in-plane anisotropy often originated directly from the low crystallographic symmetry. However, little has been known in the case where interlayer couplings dominate the anisotropy of electronic band structures in them. Here, by both experiment and theory, we show rather than geometric factors, the anisotropic energy bands of monoclinic gallium telluride (GaTe) are determined by a subtle bulk-surface interaction. Bulk electronic states are found to be the major contribution of the highest valence band, whose anisotropy is yet immune to surface doping of potassium atoms. The above peculiar behaviors are attributed to strong interlayer couplings, which gives rise to an inverse of anisotropy of hole effective masses and a direct-indirect-direct transition of band gap, depending on the number of layers. Our results thus pave the way for future applications of anisotropic layered semiconductors in nanoelectronics and optoelectronics.Comment: 3 figure

Vibration Suppression for Beam-Like Repeating Lattice Structure Based on Equivalent Model by a Nonlinear Energy Sink

Based on the fully deployed space beam-like truss, the vibration reduction of the lattice structure is studied by using the local NES (nonlinear energy sink) attachment in this paper. The beam-like lattice structure is modeled as an equivalent linear continuous system (a finite length beam) by the equivalent method and validated with the finite element results. The dynamic vibration equations for the equivalent cantilever beam are established and the governing equations for the equivalent beam with NES are approximated by the Galerkin method. The displacement responses of the beam with and without NES attached under shock excitation are obtained. With NES at different positions, the amplitude responses of the coupled system under the external excitation at different positions are calculated to evaluate the suppression effect of the NES attachment to the structure. And with different masses of the NES, the amplitude responses of the coupled structure subject to the external excitation at different positions are also investigated to get the influence of the mass of the NES attachment to the vibration reduction. It can be seen from the results that the NES attachment can attenuate the response of the beam-like truss under transient excitation efficiently. And with the mass of NES attachment increasing, the vibration amplitude of the coupled system declines more rapidly, and the energy consumption efficiency of the NES attachment is higher. Moreover, the attenuation effect of the NES with different masses is experimentally analyzed. The experimental results are in good accord with the theoretical calculation

Structural Damage Features Extracted by Convolutional Neural Networks from Mode Shapes

This paper aims to locate damaged rods in a three-dimensional (3D) steel truss and reveals some internal working mechanisms of the convolutional neural network (CNN), which is based on the first-order modal parameters and CNN. The CNN training samples (including a large number of damage scenarios) are created by ABAQUS and PYTHON scripts. The mode shapes and mode curvature differences are taken as the inputs of the CNN training samples, respectively, and the damage locating accuracy of the CNN is investigated. Finally, the features extracted from each convolutional layer of the CNN are checked to reveal some internal working mechanisms of the CNN and explain the specific meanings of some features. The results show that the CNN-based damage detection method using mode shapes as the inputs has a higher locating accuracy for all damage degrees, while the method using mode curvature differences as the inputs has a lower accuracy for the targets with a low damage degree; however, with the increase of the target damage degree, it gradually achieves the same good locating accuracy as mode shapes. The features extracted from each convolutional layer show that the CNN can obtain the difference between the sample to be classified and the average of training samples in shallow layers, and then amplify the difference in the subsequent convolutional layer, which is similar to a power function, finally it produces a distinguishable peak signal at the damage location. Then a damage locating method is derived from the feature extraction of the CNN. All of these results indicate that the CNN using first-order modal parameters not only has a powerful damage location ability, but also opens up a new way to extract damage features from the measurement data

Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network

The traditional methods of structural health monitoring (SHM) have obvious disadvantages such as being time-consuming, laborious and non-synchronizing, and so on. This paper presents a novel and efficient approach to detect structural damages from real-time vibration signals via a convolutional neural network (CNN). As vibration signals (acceleration) reflect the structural response to the changes of the structural state, hence, a CNN, as a classifier, can map vibration signals to the structural state and detect structural damages. As it is difficult to obtain enough damage samples in practical engineering, finite element analysis (FEA) provides an alternative solution to this problem. In this paper, training samples for the CNN are obtained using FEA of a steel frame, and the effectiveness of the proposed detection method is evaluated by inputting the experimental data into the CNN. The results indicate that, the detection accuracy of the CNN trained using FEA data reaches 94% for damages introduced in the numerical model and 90% for damages in the real steel frame. It is demonstrated that the CNN has an ideal detection effect for both single damage and multiple damages. The combination of FEA and experimental data provides enough training and testing samples for the CNN, which improves the practicability of the CNN-based detection method in engineering practice