Particle-volume tracking-assisted discrete digital volume correlation for kinematics analysis of particles
Source abstract
Abstract
Accurate tracking of the discrete particle kinematics (i.e., particle displacement and rotation) is helpful for analyzing the macroscopic mechanical behavior of granular materials. When particle motion/rotation is small, existing discrete digital volume correlation (discrete DVC) or particle tracking method can correctly measure the particle kinematics by matching particle intensity information or geometric information under different states. However, when the particles move acutely (i.e., the displacement and rotation are large), these methods become inefficient, inaccurate or even fail in the calculation. To enhance the robustness and accuracy of kinematics analysis of particles, we propose a generalized particle-volume (PV) tracking-assisted discrete DVC method. In this method, the geometric information (i.e., centroid position, volume and principal axes’ orientation) of discrete particles in reference and deformed volume images is first extracted through image processing. Then based on the obtained geometric information, the PV tracking method is used to find all the potential moving particles and corresponding initial guesses for each reference particle. Finally, a discrete DVC algorithm using the state-of-the-art 3D inverse compositional Gauss-Newton (IC-GN) algorithm is employed to determine the true target particle from all potential moving particles and refine the true initial guess using the intensity information for each reference particle. Compared with existing methods, the proposed method makes full use of both the geometric information and intensity information of discrete particles, thus it can achieve efficient and accurate motion measurement of particles even with large motion and rotation. The effectiveness of the proposed method is validated using numerical and real experiments.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Granular flow and fluidized beds · Soil Mechanics and Vehicle Dynamics · Landslides and related hazards
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (44)
Show source-provided references
1. Bell, N., Yu, Y., Mucha, P.J.: Particle-based simulation of granular materials. in: Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation 77–86 (2005)10.1145/1073368.1073379 ↗
2. Karatza, Z., Ando, E., Papanicolopulos, S.A., Viggiani, G., Ooi, J.Y.: Effect of particle morphology and contacts on particle breakage in a granular assembly studied using X-ray tomography. Granul. Matter. 21, 21–44 (2019). https://doi.org/10.1007/s10035-019-0898-210.1007/s10035-019-0898-2 ↗
3. Cheng, Z., Wang, J.: A particle-tracking method for experimental investigation of kinematics of sand particles under triaxial compression. Powder Technol. 328, 436–451 (2018). https://doi.org/10.1016/j.powtec.2017.12.07110.1016/j.powtec.2017.12.071 ↗
4. Rorato, R.: Linking shape and rotation of grains during triaxial compression of sand. Granul. Matter. 22, 1–21 (2020). https://doi.org/10.1007/s10035-020-01058-2Toledo, M.Andò, E., Gens, A., Viggiani, G.10.1007/s10035-020-01058-2 ↗
5. Chen, Y., Ma, G., Zhou, W., Wei, D., Zhao, Q., Zou, Y.: An enhanced tool for probing the microscopic behavior of granular materials based on X-ray micro-CT and FDEM. Comput. Geotech. 132, 103974 (2021). https://doi.org/10.1016/j.compgeo.2020.10397410.1016/j.compgeo.2020.103974 ↗
6. Munoz, H., Kiyota, T.: Deformation and localisation behaviours of reinforced gravelly backfill using shaking table tests. J. Rock. Mech. Geotech. 12, 102–111 (2019). https://doi.org/10.1016/j.jrmge.2019.06.00810.1016/j.jrmge.2019.06.008 ↗
7. Mollon, G., Zhao, J.: Generating realistic 3D sand particles using Fourier descriptors. Granul. Matter. 15, 95–108 (2013). https://doi.org/10.1007/s10035-012-0380-x10.1007/s10035-012-0380-x ↗
8. Cheng, Z., Wang, J.: Experimental investigation of inter-particle contact evolution of sheared granular materials using X-ray micro-tomography. Soils Found. 58, 1492–1510 (2018). https://doi.org/10.1016/j.sandf.2018.08.00810.1016/j.sandf.2018.08.008 ↗
9. Bruchon, J.F., Pereira, J.M.: Full 3D investigation and characterisation of capillary collapse of a loose unsaturated sand using X-ray CT. Granul. Matter. 15, 783–800 (2013). https://doi.org/10.1007/s10035-013-0452-610.1007/s10035-013-0452-6 ↗
10. Landry, J., Grest, G., Plimpton, S.: Discrete element simulations of stress distributions in silos: crossover from two to three dimensions. Powder Technol. 139, 233–239 (2004). https://doi.org/10.1016/j.powtec.2003.10.01610.1016/j.powtec.2003.10.016 ↗
11. Zhou, B., Huang, R., Wang, H., Wang, J.: DEM investigation of particle anti-rotation effects on the micromechanical response of granular materials. Granul. Matter. 15, 315–326 (2013). https://doi.org/10.1007/s10035-013-0409-910.1007/s10035-013-0409-9 ↗
12. Cheng, Y.P., Bolton, M.D., Nakata, Y.: Crushing and plastic deformation of soils simulated using DEM. Géotechnique. 54, 131–141 (2004). https://doi.org/10.1680/geot.2004.54.2.13110.1680/geot.2004.54.2.131 ↗
13. Jiang, M.J., Yu, H.S., Harris, D.: A novel discrete model for granular material incorporating rolling resistance. Comput. Geotech. 32, 340–357 (2005). https://doi.org/10.1016/j.compgeo.2005.05.00110.1016/j.compgeo.2005.05.001 ↗
14. Tordesillas, A., Walsh, D.C.S.: Incorporating rolling resistance and contact anisotropy in micromechanical models of granular media. Powder Technol. 124, 106–111 (2002). https://doi.org/10.1016/S0032-5910(01)00490-910.1016/S0032-5910(01)00490-9 ↗
15. Andò, E., Hall, S.A., Viggiani, G., Desrues, J., Bésuelle, P.: Grain-scale experimental investigation of localised deformation in sand: A discrete particle tracking approach. Acta Geotech. 7, 1–13 (2012). https://doi.org/10.1007/s11440-011-0151-610.1007/s11440-011-0151-6 ↗
16. Hall, S.A., Bornert, M., Desrues, J., Pannier, Y., Lenoir, N., Viggiani, G., Bésuelle, P.: Discrete and continuum analysis of localised deformation in sand using X-ray µCT and volumetric digital image correlation. Géotechnique. 60, 315–322 (2010). https://doi.org/10.1680/geot.2010.60.5.31510.1680/geot.2010.60.5.315 ↗
17. Zhou, B., Wang, J., Wang, H.: A novel particle tracking method for granular sands based on spherical harmonic rotational invariants. Geotechnique. 68, 1116–1123 (2018). https://doi.org/10.1680/jgeot.17.t.04010.1680/jgeot.17.t.040 ↗
18. Pannier, Y., Lenoir, N., Bornert, M.: Discrete volumetric digital image correlation for the investigation of granular type media at microscale: accuracy assessment. In: EPJ Web of Conferences: ICEM 14-14th International Conference on Experimental Mechanics, EDP Science, 6 35003 (2010). https://doi.org/10.1051/epjconf/2010063500310.1051/epjconf/20100635003 ↗
19. Pan, B., Wang, B.: Some recent advances in digital volume correlation. Opt. lasers Eng. 135, 106189 (2020). https://doi.org/10.1016/j.optlaseng.2020.10618910.1016/j.optlaseng.2020.106189 ↗
20. Cheng, Z., Wang, J.: An investigation of the breakage behaviour of a pre-crushed carbonate sand under shear using X-ray micro-tomography. Eng. Geol. 293, 106286 (2021). https://doi.org/10.1016/j.enggeo.2021.10628610.1016/j.enggeo.2021.106286 ↗
21. Cheng, Z., Zhou, B., Wang, J.: Tracking particles in sands based on particle shape parameters. Adv. Powder Technol. 31, 2005–2019 (2020). https://doi.org/10.1016/j.apt.2020.02.03310.1016/j.apt.2020.02.033 ↗
22. Xiong, W., Wang, J.: Gene mutation of particle morphology through spherical harmonic-based principal component analysis. Powder Technol. 386, 176–192 (2021). https://doi.org/10.1016/j.powtec.2021.03.03210.1016/j.powtec.2021.03.032 ↗
23. Bay, B.K., Smith, T.S., Fyhrie, D.P., Saad, M.: Digital volume correlation: Three-dimensional strain mapping using X-ray tomography. Exp. Mech. 39, 217–226 (1999). https://doi.org/10.1007/BF0232355510.1007/BF02323555 ↗
24. Wang, B., Pan, B.: Incremental digital volume correlation method with nearest subvolume offset: An accurate and simple approach for large deformation measurement. Adv. Eng. Softw. 116, 80–88 (2018). https://doi.org/10.1016/j.advengsoft.2017.12.00410.1016/j.advengsoft.2017.12.004 ↗
25. Pan, B., Wang, B.: A flexible and accurate digital volume correlation method applicable to high-resolution volumetric images. Meas. Sci. Technol. 28, 105007 (2017). https://doi.org/10.1088/1361-6501/aa7e8a10.1088/1361-6501/aa7e8a ↗
26. Pan, B., Wang, B., Wu, D., Lubineau, G.: An efficient and accurate 3D displacements tracking strategy for digital volume correlation. Opt. lasers Eng. 58, 126–135 (2012). https://doi.org/10.1016/j.optlaseng.2014.02.00310.1016/j.optlaseng.2014.02.003 ↗
27. Yang, J., Huang, J., Jiang, Z., Dong, S., Tang, L., Liu, Y.: 3D SIFT aided path independent digital volume correlation and its GPU acceleration. Opt. lasers Eng. 136, 106323 (2021). https://doi.org/10.1016/j.optlaseng.2020.10632310.1016/j.optlaseng.2020.106323 ↗
28. Mao, L., Liu, H., Wang, Y., Ding, L., Ju, Y., Chiang, F.: 3-D strain estimation in sandstone using improved digital volumetric speckle photography algorithm. Int. J. Rock. Mech. Min. 141, 104736 (2021). https://doi.org/10.1016/j.ijrmms.2021.10473610.1016/j.ijrmms.2021.104736 ↗
29. Mao, L., Liu, H., Lei, Y., Wu, J., Ju, Y., Chiang, F.: Evaluation of global and local digital volume correlation for measuring 3D deformation in rocks. Rock. Mech. Rock. Eng. 54, 4949–4964 (2021). https://doi.org/10.1007/s00603-021-02517-910.1007/s00603-021-02517-9 ↗
30. Li, C., Kong, L., Shu, R., An, R., Jia, H.: Dynamic three-dimensional imaging and digital volume correlation analysis to quantify shear bands in grus. Mech. Mater. 151, 103646 (2020). https://doi.org/10.1016/j.mechmat.2020.10364610.1016/j.mechmat.2020.103646 ↗
31. Li, H., Chai, H., Xiao, X., Huang, J., Luo, S.: Fractal breakage of porous carbonate sand particles: microstructures and mechanisms. Powder Technol. 363, 112–121 (2020). https://doi.org/10.1016/j.powtec.2020.01.00710.1016/j.powtec.2020.01.007 ↗
32. Zhou, B., Wang, J., Zhao, B.: Micromorphology characterization and reconstruction of sand particles using micro X-ray tomography and spherical harmonics. Eng. Geol. 184, 126–137 (2015). https://doi.org/10.1016/j.enggeo.2014.11.00910.1016/j.enggeo.2014.11.009 ↗
33. Yu, B., Fan, W., Fan, J., Dijkstrac, T., Wei, Y., Wei, T.: X-ray micro-computed tomography (µ-CT) for 3D characterization of particle kinematics representing water-induced loess micro-fabric collapse. Eng. Geol. 279, 105895 (2020). https://doi.org/10.1016/j.enggeo.2020.10589510.1016/j.enggeo.2020.105895 ↗
34. Pan, B., Wu, D., Wang, Z.: Internal displacement and strain measurement using digital volume correlation: a least-squares framework. Meas. Sci. Technol. 23, 45002 (2012). https://doi.org/10.1088/0957-0233/23/4/04500210.1088/0957-0233/23/4/045002 ↗
35. Buljac, A., Jailin, C., Mendoza, A., Neggers, J., Taillandier-Thomas, T., Bouterf, A.: Digital volume correlation: Review of progress and challenges. Exp. Mech. 58, 1–48 (2018). https://doi.org/10.1007/s11340-018-0390-710.1007/s11340-018-0390-7 ↗
36. Zou, X., Pan, B.: Full-automatic seed point selection and initialization for digital image correlation robust to large rotation and deformation. Opt. lasers Eng. 138, 106432 (2021). https://doi.org/10.1016/j.optlaseng.2020.10643210.1016/j.optlaseng.2020.106432 ↗
37. Lin, A., Li, R., Jiang, Z., Dong, S., Liu, Y., Zhou, L.: Path independent stereo digital image correlation with high speed and analysis resolution. Opt. lasers Eng. 149, 106812 (2022). https://doi.org/10.1016/j.optlaseng.2021.10681210.1016/j.optlaseng.2021.106812 ↗
38. Andò, E., Hall, S.A., Viggiani, G., Desrues, J., Bésuelle, P.: Experimental micromechanics: grain-scale observation of sand deformation. Géotech Lett. 2, 107–112 (2012). https://doi.org/10.1007/s11440-011-0151-610.1007/s11440-011-0151-6 ↗
39. Pan, B.: Bias error reduction of digital image correlation using Gaussian pre-filtering. Opt. lasers Eng. 51, 1161–1167 (2013). https://doi.org/10.1016/j.optlaseng.2013.04.00910.1016/j.optlaseng.2013.04.009 ↗
40. Huang, L., Wang, M.: Image thresholding by minimizing the measures of fuzziness. Pattern Recogn. 28, 41–51 (1995). https://doi.org/10.1016/0031-3203(94)E0043-K10.1016/0031-3203(94)E0043-K ↗
41. Fonseca, J.: The Evolution of Morphology and Fabric of a Sand During Shearing (PhD thesis) Imperial College London, (2011)
42. Li, C., Shu, R.: Accurate and simple digital volume correlation using pre-interpolation. Meas. Sci. Technol. 31, 095201 (2020). https://doi.org/10.1088/1361-6501/ab85b010.1088/1361-6501/ab85b0 ↗
43. Pan, B., Wang, B.: Digital image correlation with enhanced accuracy and efficiency: A comparison of two subpixel registration algorithms. Exp. Mech. 56, 1395–1409 (2016). https://doi.org/10.1007/s11340-016-0180-z10.1007/s11340-016-0180-z ↗
44. Slabaugh, G.G.: Computing euler angles from a rotation matrix. Retrieved on August. 6, 39–63 (1999)