Forward simulation of electromagnetic waves in coal gangue model based on improved bidirectional peak-valley search algorithm
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摘要: 实现放顶煤过程煤矸含量自动识别是综采自动化的重要目标，现有煤矸含量自动识别方法存在准确性、实时性较低等问题。放顶煤过程产生的煤矸混合物是由煤、矸石和空气形成的三相介质，各相介质的电性参数不同，在不同组分的混合三相介质中，电磁波的传播特性也不同。煤和矸石相对介电常数差异明显，通过研究不同含矸率煤矸混合物的电性参数，可为放顶煤工作面含矸率自动识别提供新的思路和方法。为了探究不同含矸率煤矸混合物的电性差异，提出了一种基于分治策略的双向峰−谷搜索算法，基于该算法建立了煤矸多相离散随机介质模型，基于麦克斯韦方程组及其本构关系方程，利用时域有限差分法对所建立的模型进行电磁波正演模拟。分析表明，基于分治策略改进双向峰−谷搜索算法后，煤矸多相离散随机介质模型中的煤、矸石、空气三相之间不仅存在明显的相界面，且各相离散程度更大，不存在聚集现象，因此局部介质也能体现整体的电性参数，能够满足电磁波正演的介质模型需求。正演模拟结果表明：① 激励信号的频率会影响透射波的幅值，在12 GHz范围内，激励信号频率越高，透射波幅值越大；频率过低会降低信号的鲁棒性，激励频率应高于2 GHz。② 煤矸混合物的含矸率与介质整体的等效介电常数呈正相关。含矸率越高，电磁波信号的传播损耗越多，接收平面接收到的信号幅值越小，电磁波信号穿透介质所用的时间越长。不同含矸率之间呈现明显的差异性，可以用作综采放顶煤含矸率识别的依据。Abstract: Realizing automatic recognition of coal gangue content during the top coal caving process is an important goal of fully mechanized mining automation. The existing methods for automatic recognition of coal gangue content have problems such as low accuracy and real-time performance. The coal gangue mixture generated during the top coal caving process is a three-phase medium formed by coal, gangue, and air. The electrical parameters of each phase medium are different. The propagation features of electromagnetic waves are also different in different components of the mixed three-phase medium. There is a significant difference in the dielectric constant between coal blocks and gangue. By studying the electrical parameters of coal gangue mixtures with different gangue contents, new ideas and methods can be provided for automatic recognition of gangue content in top coal caving working faces. In order to explore the electrical differences of coal gangue mixtures with different gangue contents, a bidirectional peak-valley search algorithm based on the divide and conquer strategy is proposed. Based on this algorithm, a multiphase discrete random medium model of coal gangue is established. Based on the Maxwell equations and their constitutive relationship equations, the electromagnetic wave forward simulation of the established model is performed using the finite difference time domain method. The analysis shows that after improving the bidirectional peak-valley search algorithm based on the divide and conquer strategy, there is a clear phase interface between the coal, gangue, and air phases in the coal gangue multiphase discrete random medium model. Moreover, there is a greater degree of dispersion of each phase and no aggregation phenomenon. Therefore, the local medium can also reflect the overall electrical parameters, which can meet the requirements of the medium model for electromagnetic wave forward modeling. The forward simulation results indicate the following points. ① The frequency of the excitation signal will affect the amplitude of the transmitted wave. In the 12 GHz range, the higher the frequency of the excitation signal, the greater the amplitude of the transmitted wave. Low frequency will reduce the robustness of the signal, and the excitation frequency should be higher than 2 GHz. ② The gangue content of the coal gangue mixture is positively correlated with the overall equivalent dielectric constant of the medium. The higher the gangue content, the greater the propagation loss of the electromagnetic wave signal. The smaller the amplitude of the signal received by the receiving plane, the longer the time it takes for the electromagnetic wave signal to penetrate the medium. There is a significant difference between different gangue contents, which can be used as a basis for the gangue content recognition of fully mechanized top coal caving.
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图 1 算法改进前多相离散随机介质模型
Figure 1. Multiphase discrete random medium model before algorithm improvement
图 2 改进双向峰−谷搜索算法流程
Figure 2. Flow of improved bidirectional peak-valley search algorithm
图 3 算法改进后煤矸多相离散随机介质模型
Figure 3. Multiphase discrete random medium model after algorithm improvement
图 4 算法改进前后模型各相分布
Figure 4. Each phase distribution of model before and after algorithm improvement
图 7 不同激励频率下接收平面获取的信号时域图像
Figure 7. Time domain images of signals obtained from the receiving plane under different excitation frequencies
图 8 不同含矸率的煤矸多相离散随机介质模型
Figure 8. Multiphase discrete random medium model of coal gangue with different gangue content
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