P0238
Image-based Shape Characterization and Three-dimensional Discrete Element Modeling of a Granular Martian Regolith Simulant Qiushi Chen1*, Zhengshou Lai1, Stephen Moysey2, Mengfen Shen1 1Glenn Department of Civil Engineering, Clemson University, Clemson SC 29634 USA 2Environmnetal Engineering and Earth Science, Clemson University SC 29634 USA *
[email protected] https://cecas.clemson.edu/geomechanics/ CHARACTERIZATION
ABSTRACT
Proposed framework: • X-ray CT scan to obtain raw images • Machine learning approach to segment raw CT images • Level set method to accurately reconstruct 3D particle shapes (a) raw CT images
(b) particular slice
100
100
Sieve analysis X-CT image analysis
80
Sieve analysis PFC simulated
10 mm/s 80 Percent finner (%)
PFC3D 5.00 ©2016 Itasca Consulting Group, Inc. Academic Model
60 40
≈20 mm 20
60 40 20
0 1 10
0
-1
10 10 Particle size (mm)
10
20 mm
-2
Characterization of particle shapes: • Aspect ratio
0 101
100 10-1 Particle size (mm)
10-2
Results Angle of repose
4
realistic shapes
sphere Probability density
FRAMEWORK
Angle of repose test • Realistic particle size distribution and particle shapes • Overlapping discrete element cluster (ODEC) to represent irregular particles
Characterization of particle sizes: • Equivalent diameter
Percent finer (%)
This work describes an image-based approach to obtain the particle size distribution and three-dimensional particle shape information of an intact granular regolith simulant captured in the X-ray computed tomography (CT). A machine learning and level set-based framework to extract realistic particle shapes is firstly introduced. The extracted realistic particle shapes are then characterized by various shape descriptors (e.g. the particle size distribution, the sphericity, and the aspect ratio). The characterized morphology information is incorporated into the development of three-dimensional discrete element models, which are capable of simulating the grading- and shapedependent behavior of granular regolith simulant.
NUMERICAL EXAMPLES
3
2
Force chain
realistic shapes
sphere
1
0 0.4
0.6
0.8 Aspect ratio a 21
1
1.2
3
Contact force distribution
2
0.3 1
0.6 0.8 Aspect ratio a 31
1
10 8 6
0.1
0.05
0.05
0
0
1 2 3 Contact shear force (N)
4
0
10-4
0.2
0.4 0.6 0.8 Contact normal force (N)
1 10-3
2
ONGOING WORK AND REFERENCES
0.6
0.7 0.8 Sphericity
0.9
1
8
Probability density
0.1
0.15
4
2
(g) initial level set
(i) reconstructed particle
0.15
0.2
A machine-learning and level-set based framework is introduced to extract 3D realistic particle shapes from X-ray CT images of a granular regolith simulant. The extracted particle shapes are then characterized by the particle size distribution, sphericity, and aspect ratios. A 3D grading- and shape-dependent DEM model is then developed based on the characterized morphology information. Angle of repose tests are simulated to study the behavior of the granular regolith. Technical findings are: 1. The particle size distribution results evaluated from CT image analysis and laboratory sieve analysis are pretty consistent, proving the accuracy and efficiency of the introduced particle extraction methodology. 2. The sphericity of the particular granular regolith used in this work is about 0.85, and the average aspect ratios are about 0.77 and 0.61, respectively. 3. The shape irregularity attributes to the significant shear resistance of the specimen. The shear resistance of the irregular-shape model is related to the microscopic features, such as the coordinate number and contract force.
4
Characterization of particle shapes: • Roundness
(h) level set evolution
0.25
0.2
0
irregular-shape spherical-shape
CONCLUSIONS
0 0.5
(f) target particle
0.4
Characterization of particle shapes: • Sphericity
Probability density
(e) level set edge indicator
irregular-shape spherical-shape
Probability
0 0.2
(d) machine-learning based image segmentation
0.3
0.25 Probability
(c) compute domain
Probability density
4
6
0 0.4
0.5
0.6 0.7 Roundness
0.8
0.9
ACKNOWLEDGEMENTS The authors would like to acknowledge the financial support provided by the NASA SC Space Consortium Grants (NNX15AK53A and NNX15AL49H).
Ongoing work: • A new spherical harmonics-based DEM to model irregular particles and to efficiently detect particle contacts • Systematic investigation of particle shape effects on the mechanical behavior under more complicated loading conditions and on the various microscopic features. References Lai and Chen (2017). Characterization and discrete element simulation of grading and shapedependent behavior of JSC-1A Martian regolith simulant”, Granular Matter, 19(4):69.
