Simulation Test System for Coal and Gas Outburst Triggered by Tunneling under Gas-Filling Condition and Its Application

2021 ◽  
Vol 45 (1) ◽  
pp. 20210048
Author(s):  
Hanpeng Wang ◽  
Wei Wang ◽  
Liang Yuan ◽  
Guofeng Yu ◽  
Jing Wang ◽  
...  
Keyword(s):  
Test System ◽  
Coal And Gas Outburst ◽  
Simulation Test ◽  
Gas Outburst

Test system for the visualization of dynamic disasters and its application to coal and gas outburst

2019 ◽  
Vol 122 ◽  
pp. 104083 ◽  
Author(s):  
Bin Zhou ◽  
Jiang Xu ◽  
Shoujian Peng ◽  
Jiabo Geng ◽  
Fazhi Yan
Keyword(s):  
Test System ◽  
Coal And Gas Outburst ◽  
Gas Outburst

Intensity of Coal and Gas Outburst Prediction Model Based on the Improved BP Neural Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 1190-1193
Author(s):  
Long Kong
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Forecast Accuracy ◽  
Coal And Gas Outburst ◽  
Mine Safety ◽  
Simulation Test ◽  
Gas Outburst ◽  
Major Disaster

Coal and gas outburst has become one of the major disaster hazard of coal mine safety, Staff on gas outburst disaster prevention is now important research project. The gas outburst prediction work, different degrees of factors has some impact on forecast accuracy, such as logical reasoning efficiency is low. This paper, by using the BP neural network combined with gas outburst samples a prediction model is established, According to the data of a certain coal mine as a sample, Using MATLAB software to simulation test, have been predicted and actual values fitting degree is higher, Can reflect the realities of the coal and gas outburst.

Deformation Analysis for Coal and Gas Outburst Cavity

2011 ◽  
Vol 121-126 ◽  
pp. 2607-2613
Author(s):  
Qian Ting Hu ◽  
Wen Bin Wu ◽  
Guo Qiang Cheng
Keyword(s):  
Damage Zone ◽  
Coal And Gas Outburst ◽  
Gas Content ◽  
Combination Method ◽  
Deformation Analysis ◽  
Failure Zone ◽  
Finite Element Code ◽  
Cavity Volume ◽  
Gas Emission ◽  
Gas Outburst

Outburst cavity formed during coal and gas outburst can be pear shaped, elliptical, or just like an irregularly elongated ellipsoid, its capacity is always smaller than the volume of ejected coal. And the gas emission quantity is almost 4 to 10 times as gas content in ejected coal. These are two different expressions of the same problem. To find the reasons for the decrease of outburst cavity volume and the increase of gas emission quantity per ton, by using the finite element code ANSYS, the damage zone and the failure zone of the outburst cavity were determined based on the static and dynamic combination method. In this paper, the reason for the decrease of the outburst volume was explained.

scholarly journals Mechanical criterion for coal and gas outburst: a perspective from multiphysics coupling

2021 ◽  
Author(s):  
Ting Liu ◽  
Baiquan Lin ◽  
Xuehai Fu ◽  
Ang Liu
Keyword(s):  
Control Method ◽  
Risk Identification ◽  
Controlling Factors ◽  
Gas Pressure ◽  
Critical Values ◽  
Coal And Gas Outburst ◽  
Strong Interactions ◽  
Gas Outburst ◽  
Multiphysics Coupling ◽  
Coal Strength

AbstractAlthough a series of hypotheses have been proposed, the mechanism underlying coal and gas outburst remains unclear. Given the low-index outbursts encountered in mining practice, we attempt to explore this mechanism using a multiphysics coupling model considering the effects of coal strength and gas mass transfer on failure. Based on force analysis of coal ahead of the heading face, a risk identification index Cm and a critical criterion (Cm ≥ 1) of coal instability are proposed. According to this criterion, the driving force of an outburst consists of stress and gas pressure gradients along the heading direction of the roadway, whereas resistance depends on the shear and tensile strengths of the coal. The results show that outburst risk decreases slightly, followed by a rapid increase, with increasing vertical stress, whereas it decreases with increasing coal strength and increases with gas pressure monotonically. Using the response surface method, a coupled multi-factor model for the risk identification index is developed. The results indicate strong interactions among the controlling factors. Moreover, the critical values of the factors corresponding to outburst change depending on the environment of the coal seams, rather than being constants. As the buried depth of a coal seam increases, the critical values of gas pressure and coal strength decrease slightly, followed by a rapid increase. According to its controlling factors, outburst can be divided into stress-dominated, coal-strength-dominated, gas-pressure-dominated, and multi-factor compound types. Based on this classification, a classified control method is proposed to enable more targeted outburst prevention.

Ground Flutter Simulation Test Based on Reduced Order Modeling of Aerodynamics by CFD/CSD Coupling Method

2019 ◽  
Vol 11 (01) ◽  
pp. 1950008
Author(s):  
Binwen Wang ◽  
Xueling Fan
Keyword(s):  
Aerodynamic Force ◽  
Test System ◽  
Simulation Test ◽  
Robust Controller ◽  
Test Technique ◽  
Reduced Order ◽  
Aerodynamic Model ◽  
Flutter Boundary ◽  
Computational Fluid Dynamics Cfd ◽  
Good Agreement

Flutter is an aeroelastic phenomenon that may cause severe damage to aircraft. Traditional flutter evaluation methods have many disadvantages (e.g., complex, costly and time-consuming) which could be overcome by ground flutter test technique. In this study, an unsteady aerodynamic model is obtained using computational fluid dynamics (CFD) code according to the procedure of frequency domain aerodynamic calculation. Then, the genetic algorithm (GA) method is adopted to optimize interpolation points for both excitation and response. Furthermore, the minimum-state method is utilized for rational fitting so as to establish an aerodynamic model in time domain. The aerodynamic force is simulated through exciters and the precision of simulation is guaranteed by multi-input and multi-output robust controller. Finally, ground flutter simulation test system is employed to acquire the flutter boundary through response under a range of air speeds. A good agreement is observed for both velocity and frequency of flutter between the test and modeling results.

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