Abstract
This paper presents a multi-step DEM calibration procedure for cohesive solid materials, incorporating feasibility in finding a non-empty solution space and definiteness in capturing bulk responses independently of calibration targets. Our procedure follows four steps: (I) feasibility; (II) screening of DEM variables; (III) surrogate modeling-based optimization; and (IV) verification. Both types of input parameter, continuous (e.g. coefficient of static friction) and categorical (e.g. contact module), can be used in our calibration procedure. The cohesive and stress-history-dependent behavior of a moist iron ore sample is replicated using experimental data from four different laboratory tests, such as a ring shear test. This results in a high number of bulk responses (i.e. ≥ 4) as calibration targets in combination with a high number of significant DEM input variables (i.e. > 2) in the calibration procedure. Coefficient of static friction, surface energy, and particle shear modulus are found to be the most significant continuous variables for the simulated processes. The optimal DEM parameter set and its definiteness are verified using 20 different bulk response values. The multi-step optimization framework thus can be used to calibrate material models when both a high number of input variables (i.e. > 2) and a high number of calibration targets (i.e. ≥ 4) are involved.
Highlights
To simulate, design, and optimize processes and equipment for handling bulk solids, such as iron ore and coal, the discrete element method (DEM) is a suitable computational method
The calibration can be done by finding an optimal combination set of DEM input parameters that replicates the captured bulk response [5]
We develop a reliable multi-step DEM calibration procedure to capture the cohesive and stress-history-dependent behavior of bulk solids
Summary
Design, and optimize processes and equipment for handling bulk solids, such as iron ore and coal, the discrete element method (DEM) is a suitable computational method. DEM simulations can only predict bulk level responses (e.g. shear strength) accurately if their input parameters are selected appropriately. The calibration can be done by finding an optimal combination set of DEM input parameters that replicates the captured bulk response [5]. By setting multiple targets for the DEM calibration, more than a single bulk response can be considered. This prevents the ‘‘ambiguous parameter combinations” problem in the DEM calibration procedure, which is discussed in detail in [11].
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