Current Issue : January-March Volume : 2025 Issue Number : 1 Articles : 3 Articles
Ships navigating in channels with vortex fields face increased risks. However, these vortex fields can be monitored using acoustic methods. The key is to extract the phase characteristics of sound signals passing through the vortices. Using time-reversal mirrors, this paper studied the extraction method of characteristics both numerically and experimentally, aiming to verify the effectiveness of the numerical simulation method. Starting from this point, the impact of different movement forms and scale changes in vortex fields on the acoustic signal extraction method was further investigated. The results indicate that with the iterations of time reversal (N < 6), the method is effective for uniformly moving vortex fields, when the vortex center moving speed Vw < 2.2 × 10−3 m/s and the radius diffusion speed Vr < 2.5 × 10−3 m/s. On the other hand, for oscillating vortex fields, it is effective when the oscillation amplitude LD < 0.15 m and the radius diffusion speed Vr < 2.4 × 10−3 m/s; meanwhile, the dynamic characteristics of the vortex field can be ignored by the phase extraction method based on time-reversal mirrors....
Since 1966, China has been using apparent resistivity observation to forecast strong aftershocks of the Xingtai earthquake. Retrospective studies of subsequent strong earthquakes have shown that anomalies in apparent resistivity observation before earthquakes usually exhibit anisotropic characteristics. In addition to the anisotropic changes in apparent resistivity before earthquakes, factors such as subway operation near the observation area, metal pipeline networks, and changes in water levels have also been found to cause anisotropic changes. These factors are called environmental interference factors. Therefore, distinguishing between anisotropic changes before earthquakes and anisotropic changes caused by interference and eliminating the effects of interference is crucial for using apparent resistivity observations for forecasting. Taking the observation of Hefei seismic station in Anhui Province as an example, a model is constructed using the finite element method to try to establish a method for analyzing anisotropy in apparent resistivity before earthquakes, and the data from other provincial stations are used for verification. In the modeling process, the influence coefficient is a measure of the relationship between the variation in apparent resistivity and the changes in the medium of the measurement area. The following results are obtained by calculating the influence coefficient using the finite element method: the influence coefficient between the power supply electrode and the measuring electrode of the apparent resistivity observation is negative, and the rest are positive, and the distribution of the influence coefficient shows obvious symmetry, with the axis of symmetry being the line connecting the electrodes and its midline, and the absolute value of the influence coefficient is inversely proportional to the distance from the electrodes. In addition, according to the constructed finite element model, the amplitude of anisotropic changes caused by interference can be quantitatively calculated. Given that interference is ubiquitous in various regions of the world, this study can provide a reference for international earthquake forecasters to quantitatively remove environmental interference in anisotropy. Moreover, when building apparent resistivity stations in seismic areas for earthquake prediction, it is best to avoid areas with larger local influence coefficients to ensure that the anomalous data before the earthquake is true and reliable....
Accurately obtaining the geological characteristic digital model of a coal seam and surrounding rock in front of a fully mechanized mining face is one of the key technologies for automatic and continuous coal mining operation to realize an intelligent unmanned working face. The research on how to establish accurate and reliable coal seam digital models is a hot topic and technical bottleneck in the field of intelligent coal mining. This paper puts forward a construction method and dynamic update mechanism for a digital model of coal seam autonomous cutting by a coal mining machine, and verifies its effectiveness in experiments. Based on the interpolation model of drilling data, a fine coal seam digital model was established according to the results of geological statistical inversion, which overcomes the shortcomings of an insufficient lateral resolution of lithology and physical properties in a traditional geological model and can accurately depict the distribution trend of coal seams. By utilizing the numerical derivation of surrounding rock mining and geological SLAM advanced exploration, the coal seam digital model was modified to achieve a dynamic updating and optimization of the model, providing an accurate geological information guarantee for intelligent unmanned coal mining. Based on the model, it is possible to obtain the boundary and inclination information of the coal seam profile, and provide strategies for adjusting the height of the coal mining machine drum at the current position, achieving precise control of the automatic height adjustment of the coal mining machine....
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