4.6 Article

Assimilation of Dynamic Combined Finite Discrete Element Methods Using the Ensemble Kalman Filter

Journal

APPLIED SCIENCES-BASEL
Volume 11, Issue 7, Pages -

Publisher

MDPI
DOI: 10.3390/app11072898

Keywords

FDEM; parameter estimation; data assimilation; Kalman filter

Funding

  1. Laboratory Directed Research and Development program from Los Alamos National Laboratory, LDRD [20170103DR]

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Simulation of fracture initiation, propagation, and arrest is a problem of interest in the scientific community, with combined finite-discrete element method (FDEM) being widely accepted for fracture modeling. In this work, a data assimilation method was implemented to estimate key model parameter values and improve calibration processes for FDEM fracture simulations. Calibration experiments showed a steady convergence of assimilated parameter values towards observed time/stress curves, with tensile and shear strengths converging faster than other parameters.
Simulation of fracture initiation, propagation, and arrest is a problem of interest for many applications in the scientific community. There are a number of numerical methods used for this purpose, and among the most widely accepted is the combined finite-discrete element method (FDEM). To model fracture with FDEM, material behavior is described by specifying a combination of elastic properties, strengths (in the normal and tangential directions), and energy dissipated in failure modes I and II, which are modeled by incorporating a parameterized softening curve defining a post-peak stress-displacement relationship unique to each material. In this work, we implement a data assimilation method to estimate key model parameter values with the objective of improving the calibration processes for FDEM fracture simulations. Specifically, we implement the ensemble Kalman filter assimilation method to the Hybrid Optimization Software Suite (HOSS), a FDEM-based code which was developed for the simulation of fracture and fragmentation behavior. We present a set of assimilation experiments to match the numerical results obtained for a Split Hopkinson Pressure Bar (SHPB) model with experimental observations for granite. We achieved this by calibrating a subset of model parameters. The results show a steady convergence of the assimilated parameter values towards observed time/stress curves from the SHPB observations. In particular, both tensile and shear strengths seem to be converging faster than the other parameters considered.

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