energy release and failure characteristics of coal samples
TRANSCRIPT
Energy Release and Failure Characteristics of Coal Samples: Laboratory Test and Numerical Modelling
Ting Ren, Xiaohan Yang and Lihai Tan
4th International Symposium on Dynamic Hazards in Underground coalmines, 22-23 June 2019, CUMT, Xuzhou China
University of Wollongong, Australia 澳大利亚伍伦贡大学
QS Ranking 2020212
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Coal Burst in Australian U/G Coal Mines
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• Coal ‘Bumps’ have also been reported by other mines in NSW
• Awareness of potential Burst hazards in QLD
Coal Burst in Australian U/G Coal Mines
Structural Geology of Coal Burst Sites
Coal Burst in Australian U/G Coal Mines
Energy AnalysisStatic and Dynamic Load Superposition TheoryCoal burst will occur when the sum of static and dynamic load exceeds the minimum load required for coal burst formation. The energy released during coal burst is provided by static load and dynamic load.
Coal Burst Induced by Static and Dynamic Load superposition (Dou et al)
Energy AnalysisEnergy Sources of Coal Bursts in AustraliaElastic energy accumulation resulted from high mining depth and complicated geological structure is the major contribution of energy sources of coal burst.
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2014/3/2 2014/9/18 2015/4/6 2015/10/23 2016/5/10 2016/11/26 2017/6/14 2017/12/31 2018/7/19
Mag
nitu
de(M
L)
Date
EarthquakeCoal Bursts
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2014/6/10 2014/12/27 2015/7/15 2016/1/31 2016/8/18 2017/3/6 2017/9/22 2018/4/10 2018/10/27
Mag
nitu
de(M
L)
Date
EarthquakeCoal Bursts
Coal Burst of Coal Mine A
Coal Burst of Coal Mine B
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WET is the indicator of the proportion of elastic energy storage of coal when coal is near critical stress.
Coal samples with low KE value will fail gentler as more energy is dissipated by deformation.
The violence of coal burst reflects in the instantaneous of energy releasing as well (WB Zhang et. al, 1986).
According to our analysis, elastic energy storage of coal samples increases with uniaxial compressive strength (UCS) ranges from 0 to 50.
Elastic strain energy index (WET)
Bursting energy index (KE)
Dynamic failure time (DT)
Uniaxial compressive strength (RC)
Coal Burst Propensity Index
Coal Burst Propensity Index
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Sample Preparation1
Sample Measuring2
RC and KE Test3 Calculation4 WET Test5 DT Test6
Loading Machine and Control SystemRadial Coring Drill Machine Coal Sample with Strain
Gauges
Risk Classification7
Coal Burst Propensity Index
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Load/kN
0
5
10
15
20
25
Time/s0 10 20 30 40 50 60 70 80 90 100 110 120
DT Test Curve
Str
ess
/M
pa
0
2
4
6
8
10
12
Time/ s0 20 40 60 80 100 120 140 160 180 200
RC Test Curve
Str
ess
/M
pa
0
2
4
6
8
10
12
Strain/με0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000
KE Test Curve
Str
ess
/M
pa
0
2
4
6
8
10
12
14
16
18
20
Strain/με0 1,000 2,000 3,000 4,000 5,000
WET Test Curve
Intact Sample Failed Sample Intact Sample Failed Sample
Intact Sample Failed Sample Intact Sample Failed Sample
Coal Burst Propensity Index
Coal Burst Propensity Index
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Quantitative Study of Coal Burst Energy
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Energy Accumulation and Releasing of Coal Burst
Quantitative Study of Coal Burst Energy
The relationship between elastic energy and plastic energy of coal samples can be measured by coal burst propensity index. The relationship between the various energy forms of coal samples, in particular the relationship between elastic energy and burst energy, acoustic emission energy and burst energy will need future research.
Energy Analysis
Schematic Diagram of Energy Accumulation before Peak Strength
Stress versus Strain Curve of Coal Samples
Load/kN
0
5
10
15
20
25
Time/s0 10 20 30 40 50 60 70 80 90 100 110 120
Fitting Functions of Fragment Size DistributionCoal Ejection Test
Energy AnalysisKinetic Energy EstimationThe estimated kinetic energy by ejected coal is between 16.24 and 20.35 MJ. Considering the total mass of ejected coal, the average initial speed of ejected coal particles ranges from 24.98 to 27.96 m/s.
Estimated Value of Kinetic Energy of Rib Burst
Developing a protective structure on CM
Energy Analysis – A Protective Structure for CM
Numerical modellingNumerical Modelling of Dynamic Load
Numerical model of SHPB test system
Numerical model of Pre-load SHPB test system
Drop hammer test system
Influence of beddings on dynamic behaviour of coal- Numerical Simulation of SHPB Test with particle flow code (PFC)
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Numerical model of SHPB test system
Numerical models of specimen (red represents beddings in coal specimen)
(a) S1 (b) S2 (c) S3 (d) S4
Split Hopkinson Pressure Bar (SHPB)
Stress wave propagation in bars with specimen S1 (no beddings): the red denotes tensile wave and the black denotes compressive wave
Stress Wave Propagation (resulting from dynamic load)
Failure Mode of Specimen
(a) S1 (b) S2 (c) S3 (d) S4
Failure evolution of different specimensFragment (fracture) pattern and failure
mode of each specimen at 1000us
• Specimen S1 and S2 have the most severe damage and similar failure modes, both characterised by shear failure.
• Specimen S3 basically remains intact with a little tensile cracks perpendicular to beddings.
Strain energy changes vs time for different specimens
Strain Energy
§ Beddings in a coal specimen lead to the degradation of its dynamic mechanical properties. This influence is closely associated with the angle between bedding and loads direction. When dynamic loads are inclined to beddings, specimen is most vulnerable with bedding breaking and sliding.
§ Strain energy and failure are effected by beddings. For specimen containing inclining beddings, coal bump and burst are not likely to appear in such coal as its instability is gradual and its storage capacity of strain energy is limited. Coal specimens with beddings parallel to dynamic loads is more vulnerable to burst.
Modelling of Water (moisture) influence Numerical model
Nuclear magnetic resonance (NMR)-images of sandstone disk with different water contents: a
saturation process; b drying process (Zhou, 2016)
NMR-images of sandstone disk in saturation condition
The relationship between saturation degree and distance ratio: (a) saturation
distribution; (b) evaporation distribution
The water distribution curve and numerical model (sc=0.3); the blue patterns represent water-weakened contacts and the green patterns represent normal contacts.
Comparison between experimental results of dry specimen and saturated specimen under uniaxial compression
Modelling of Water (moisture) influence Numerical simulation
Sketch of the numerical experimentFlow chart for the simulation procedure
Stress evolution versus ks in different cases
Instability water saturation coefficient for specimens in high-stress conditions
vs evolution curves with ks increasing
Initial stress coefficient:
65%~80% UCS: Lower instability point and higher coal burst risk.
40%~65% UCS: Water infusion is an effective approach to reduce rock burst risk as having been reported by many literatures.
≤40% UCS: Water has limited effect on releasing stress and energy for coal at such a low stress level.
Modelling of Water (moisture) influence Numerical simulation
Final failure patterns of all damaged specimens
Failure evolution of specimen in Case 5, ks=0.8
• Failure patterns were dominated by shear failure through the specimens.
• Higher initial axial stress indicates more severely with more cracks and fragments.
• Failure intensity highly depends on the release of strain energy.
Modelling of Water (moisture) influence Numerical simulation
• Fundamental studies of mechanisms
• A new risk assessment methodology for coal/rock burst;
• Monitoring and mitigation technologies.
Numerical modelling: Coal and Rock Burst
Questions？