Data-driven model order reduction for granular media
Tóm tắt
We investigate the use of reduced-order modelling to run discrete element simulations at higher speeds. Taking a data-driven approach, we run many offline simulations in advance and train a model to predict the velocity field from the mass distribution and system control signals. Rapid model inference of particle velocities replaces the intense process of computing contact forces and velocity updates. In coupled DEM and multibody system simulation, the predictor model can be trained to output the interfacial reaction forces as well. An adaptive model order reduction technique is investigated, decomposing the media in domains of solid, liquid, and gaseous state. The model reduction is applied to solid and liquid domains where the particle motion is strongly correlated with the mean flow, while resolved DEM is used for gaseous domains. Using a ridge regression predictor, the performance is tested on simulations of a pile discharge and bulldozing. The measured accuracy is about 90% and 65%, respectively, and the speed-up range between 10 and 60.
Tài liệu tham khảo
Algoryx Simulations. AGX Dynamics, Sept. 2019
Antoulas AC (2005) Approximation of large-scale dynamical systems. Soc Ind Appl Math
Boukouvala F, Gao Y, Muzzio F, Ierapetritou M (2013) Reduced-order discrete element method modeling. Chem Eng Sci 95:12–26
Brunton SL, Noack BR, Koumoutsakos P (2020) Machine learning for fluid mechanics. Ann Rev Fluid Mech 52(1):477–508
Feng YT, Owen DRJ (2014) Discrete element modelling of large scale particle systems—I: exact scaling laws. Comput Part Mech 1(2):159–168
Forrester A, Sobester A, Keane A (2008) Engineering design via surrogate modeling. Wiley, Hoboken
Gan J, Zhou Z, Yu A (2016) A GPU-based dem approach for modelling of particulate systems. Powder Technol 301:1172–1182
He Y, Evans T, Yu A, Yang R (2018) A GPU-based dem for modelling large scale powder compaction with wide size distributions. Powder Technol 333:219–228
Ihmsen M, Wahl A, Teschner M (2013) A lagrangian framework for simulating granular material with high detail. Comput Graph 37(7):800–808
Kutz JN (2017) Deep learning in fluid dynamics. J Fluid Mech 814:1–4
Lee C-H, Chen J-S (2013) Proper orthogonal decomposition-based model order reduction via radial basis functions for molecular dynamics systems. Int J Numer Methods Eng 96(10):599–627
McKyes E (1985) Soil cutting and tillage. Developments in agricultural engineering. Elsevier, Amsterdam
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
Preclik T, Rüde U (2015) Ultrascale simulations of non-smooth granular dynamics. Comput Part Mech 2(2):173–196
Radjai F, Richefeu V (2009) Contact dynamics as a nonsmooth discrete element method. Mech Mater 41(6):715–728
Rakotonirina AD, Wachs A (2018) Grains3d, a flexible dem approach for particles of arbitrary convex shape part II: parallel implementation and scalable performance. Powder Technol 324:18–35
Rogers A, Ierapetritou M (2014) Discrete element reduced-order modeling of dynamicparticulate systems. AIChE J 60:3184–94
Rycroft CH, Bazant MZ, Grest GS, Landry JW (2006) Dynamics of random packings in granular flow. Phys Rev E 73:051306
Servin M, Wang D (2016) Adaptive model reduction for nonsmooth discrete element simulation. Comput Part Mech 3(1):107–121
Servin M, Wang D, Lacoursière C, Bodin K (2014) Examining the smooth and nonsmooth discrete element approach to granular matter. Int J Numer Meth Eng 97:878–902
Steuben J, Mustoe G, Turner C (2016) Massively parallel discrete element method simulations on graphics processing units. J Comput Inf Sci Eng 16(3):031001
Tian Y, Zhang S, Lin P, Yang Q, Yang G, Yang L (2017) Implementing discrete element method for large-scale simulation of particles on multiple gpus. Comput Chem Eng 104:231–240
Utter B, Behringer RP (2004) Self-diffusion in dense granular shear flows. Phys Rev E 69:031308
Williams JR, Rege N (1997) Coherent vortex structures in deforming granular materials. Mech Cohes Fric Mater 2(3):223–236
Zhong X, Sun W (2018) An adaptive reduced-dimensional discrete element model for dynamic responses of granular materials with high-frequency noises. Int J Multiscale Comput Eng 16(4):345–366