AUTHOR=Zhao Lijuan , Han Liguo , Zhang Haining , Ge Man , Yang Shijie , Jin Xin TITLE=Influence of particle characteristics on dynamic characteristics of tail beam under coal rock caving impact JOURNAL=Frontiers in Energy Research VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.976210 DOI=10.3389/fenrg.2022.976210 ISSN=2296-598X ABSTRACT=

In order to improve the accuracy of simulation parameters used in the discrete element simulation test of a fully mechanized top-coal caving process and further explore the intelligent fully mechanized coal caving technology, this research work studies the influence of particle characteristics on the dynamic response of tail beams under the impact of caving coal rock in the process of coal caving. Based on the interface technology, the EDEM–RecurDyn–AMESim multi-domain collaborative simulation top-coal caving support is a built-model of a hydraulic mechanical integration system for scraper conveyors, which is used to simulate the coal caving process of the top-coal caving support to obtain the vibration signal of the tail beam of the top-coal caving support. This model can also be used to convert it into a two-dimensional time-spectrum image using the short-time Fourier transform (STFT) algorithm. Several groups of simulation tests were carried out on different particle radii, standard deviation of particle normal distribution, and particle slenderness ratio. The time-domain information and frequency-domain information obtained from the simulation were analyzed and compared. Combined with the vibration signal of the tail beam measured on the spot, the optimal setting parameters of the multi-field collaborative virtual prototype simulation were obtained. Compared with the data measured in the coal mine, the relative error of the maximum vibration value of the tail beam is only 3.8%, the minimum relative error is 5.5%, and the relative error of the root mean square value is 14%, which verifies the method and simulation results. This method solves the problems of difficult on-site sampling, high risk coefficient, and high test cost and promotes the development of an intelligent process of coal mining.