Data-Driven Modeling of Granular Column Collapse
Publication: Geo-Extreme 2021
ABSTRACT
Data-driven methods have recently shown their potential in engineering problems. In this paper, a data-driven model is proposed and tested for 2D granular column collapse, a typical problem involving flow-like behavior of granular material. Although many theoretical and numerical models have been developed to investigate granular flows, data-driven methods have yet to be explored. This model can predict granular flows based on the sequential relations over time with data collected from numerical solutions. Two frameworks, a Lagrangian framework and an Eulerian framework, are proposed to give rules for collecting data, and radial basis function networks are used to infer the sequential relations. It is shown that the data-driven model gives reasonable predictions compared to results obtained through direct solution. When the size of data in both frameworks are close, the performance in the Eulerian framework is better, especially for predicting states of columns with different geometric parameters. A sensitivity analysis to explore the influence of time increment and grid spacing is also conducted.
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REFERENCES
J. L. Baker, T. Barker, and J. M. N. T. Gray. A two-dimensional depth-averaged -rheology for dense granular avalanches. Journal of Fluid Mechanics, 787:367–395, Jan. 2016. Publisher: Cambridge University Press.
H. H. Bui, R. Fukagawa, K. Sako, and S. Ohno. Lagrangian meshfree particles method (SPH) for large deformation and failure flows of geomaterial using elastic–plastic soil constitutive model. International Journal for Numerical and Analytical Methods in Geomechanics, 32 (12):1537–1570, 2008.
E. J. Fern, and K. Soga. The role of constitutive models in MPM simulations of granular column collapses. Acta Geotechnica, 11(3):659–678, June 2016.
Y. Forterre, and O. Pouliquen. Flows of Dense Granular Media. Annual Review of Fluid Mechanics, 40(1):1–24, 2008.
J. Hesthaven, and S. Ubbiali. Non-intrusive reduced order modeling of nonlinear problems using neural networks. Journal of Computational Physics, 363:55–78, June 2018.
O. Hungr, and S. McDougall. Two numerical models for landslide dynamic analysis. Computers & Geosciences, 35(5):978–992, May 2009.
R. R. Kerswell. Dam break with Coulomb friction: A model for granular slumping? Physics of Fluids, 17(5):057101, Apr. 2005. Publisher: American Institute of Physics.
T. Kirchdoerfer, and M. Ortiz. Data-driven computational mechanics. Computer Methods in Applied Mechanics and Engineering, 304:81–101, June 2016.
C. Y. Kuo, Y. C. Tai, F. Bouchut, A. Mangeney, M. Pelanti, R. F. Chen, and K. J. Chang. Simulation of Tsaoling landslide, Taiwan, based on Saint Venant equations over general topography. Engineering Geology, 104(3):181–189, Mar. 2009.
I. Luca, K. Hutter, Y. C. Tai, and C. Y. Kuo. A hierarchy of avalanche models on arbitrary topography. Acta Mechanica, 205(1):121–149, June 2009.
R. Lueptow, A. Akonur, and T. Shinbrot. Piv for granular flows. Experiments in Fluids, 28(2):183–186, 2000.
A. Mangeney-Castelnau, J.-P. Vilotte, M. O. Bristeau, B. Perthame, F. Bouchut, C. Simeoni, and S. Yerneni. Numerical modeling of avalanches based on Saint Venant equations using a kinetic scheme. Journal of Geophysical Research: Solid Earth, 108(B11), 2003.
U. Niethammer, M. R. James, S. Rothmund, J. Travelletti, and M. Joswig. UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results. Engineering Geology, 128:2–11, Mar. 2012.
C. Ouyang, H. An, S. Zhou, Z. Wang, P. Su, D. Wang, D. Cheng, and J. She. Insights from the failure and dynamic characteristics of two sequential landslides at Baige village along the Jinsha River, China. Landslides, 16(7):1397–1414, July 2019.
J. Park, and I. W. Sandberg. Universal approximation using radial-basis-function networks. Neural computation, 3(2):246–257, 1991.
O. Pouliquen. Scaling laws in granular flows down rough inclined planes. Physics of Fluids, 11(3):542–548, Feb. 1999. Publisher: American Institute of Physics.
M. Raissi, P. Perdikaris, and G. E. Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378:686–707, Feb. 2019.
S. B. Savage, and K. Hutter. The motion of a finite mass of granular material down a rough incline. Journal of Fluid Mechanics, 199:177–215, Feb. 1989. Publisher: Cambridge University Press.
S. Utili, T. Zhao, and G. T. Houlsby. 3D DEM investigation of granular column collapse: Evaluation of debris motion and its destructive power. Engineering Geology, 186:3–16, Feb. 2015.
K. Wang, and W. Sun. A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning. Computer Methods in Applied Mechanics and Engineering, 334:337–380, 2018.
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Geo-Extreme 2021
Pages: 79 - 88
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© 2021 American Society of Civil Engineers.
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Published online: Nov 4, 2021
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