Current Issue : January-March Volume : 2023 Issue Number : 1 Articles : 5 Articles
Composite structures are extensively applied because of their high specific stiffness-to-weight ratios and other advantages. In various scenarios, composite structures are elastically coupled and usually exposed to a nonuniform thermal environment. This paper presents numerical simulation studies on the vibroacoustic characteristics of a composite laminated plate with elastic supports subjected to local temperature. The thermal stiffness matrix and stiffness matrix of the elastic boundary are first derived based on the finite element method, and then a coupled vibroacoustic model is developed to calculate the acoustic properties of the structure. A comprehensive study is performed to highlight the effect of the temperature loading position, temperature range, and heated area on the acoustic radiation responses of composite laminated plates with different elastic supports. The results show that the existence of thermal stress has a great influence on the acoustic radiation of the structure at low frequencies, which is affected by the boundary constraints and heated areas....
BAW-based micromixers usually achieve mixing enhancement with acoustic-induced bubbles, while SAW-based micromixers usually enhance mixing efficiency by varying the configuration of IDTs and microchannels. In this paper, bubble-enhanced acoustic mixing induced by standing surface acoustic waves (SSAWs) in a microchannel is proposed and experimentally demonstrated. Significant enhancement in the mixing efficiency was achieved after the bubbles were stimulated in our acoustofluidic microdevice. With an applied voltage of 5 V, 50 times amplified, the proposed mixing microdevice could achieve 90.8% mixing efficiency within 60 s at a flow rate of 240 μL/h. The bubbles were generated from acoustic cavitation assisted by the temperature increase resulting from the viscous absorption of acoustic energy. Our results also suggest that a temperature increase is harmful to microfluidic devices and temperature monitoring. Regulation is essential, especially in chemical and biological applications....
Bedding increases coal seam anisotropy, which leads to significant differences in the evolution laws of mining stress and strata movement. This work analyzed the dip angles of different layers to analyze the mechanical properties of the coal seam under unloading. The coal sample was subjected to triaxial compression and unloading damage acoustic emission testing. The brittleness characteristics of the coal sample failure in different bedding directions differed significantly. Compared with axial parallel bedding coal samples, axial vertical bedding and inclined stratification reached an ultimate strength. The stress–strain curve decreased sharply and showed visible brittle-drop characteristics. The average strengths of the axially inclined bedding and the parallel layered coal sample decreased compared with the axial vertical bedding coal sample and were 10.20 and 16.12 MPa, respectively, which implies a greater susceptibility to failure during unloading confining pressure tests. Acoustic emission monitoring indicated that the axial vertical bedding and inclined bedding showed sudden destruction of different coal samples, a reduction in axial parallel bedding ductility coal sample characteristics, and stronger unloading damage on the axis parallel to the bedding coal sample. Further, using the acoustic emission ring count rate and the cusp catastrophe theory, the unloading failure prediction of coal samples is carried out. The prediction results are not different from the experimental results, which shows that this method is feasible....
To clarify the changes of the magnetic-acoustic features of 45 steel specimens during fatigue damage, an experimental platform was built to carry out magnetic memory and acoustic emission detection. The magnetic memory and acoustic emission signals of specimens in different damage states were collected, and the multi-scale entropy characteristics of magnetic memory signals, as well as the wavelet packet energy spectrum and singularity index characteristics of acoustic emission signals, were further extracted. A magnetic-acoustic feature fusion and damage assessment model was constructed by using Naive Bayes method. Results show that the average value of multi-scale entropy of normal magnetic field intensity Hp (y) increases gradually with the increase of fatigue cycles, and the average value of multi-scale entropy of magnetic field intensity gradient K gradually decreases. The cumulative ringing count and energy spectrum (proportion of frequency band 1) of acoustic emission signals decrease with the increase of fatigue cycles, while the amplitude singularity index gradually increases. The established model has high evaluation accuracy, and the conclusions of this paper can provide basic methods and data support for fatigue damage evaluation of remanufactured components....
Aiming at solving the problem that it is difficult to recognize the quiet period of acoustic emission in rocks, four machine learning algorithms were adopted to develop and improve the recognition method of the quiet period of acoustic emission. In the process of establishing the model, the time domain data of acoustic emission were standardized and processed by box diagram method, so as to clean the abnormal data and reduce the dimension, and the frequency domain data were denoised by wavelet four-layer transform and wavelet packet three-layer energy decomposition, and a group of 8 wavelet packet energy parameters were established as frequency domain characteristic parameters. Based on AE time domain data, frequency domain data, and composite data (time-frequency domain data sets), the grid search traversal parameter technique was used to obtain the optimal parameters of four machine learning models. The accuracy, precision, recall, and F1 score were used to verify and evaluate the recognition performance of the models. The study results show that the recognition effects of the models are good, the model accuracy of the frequency domain data set is the lowest, and the model accuracy of the composite data set is the highest, with an accuracy of more than 90%. The kernel support vector machine model has the best performance, and its average precision is 0.87. The random forest (RF) model is the best model for recognizing quiet period of acoustic emission....
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