A calibration framework for discrete element model parameters using genetic algorithms

Huy Q. Do*, Alejandro M. Aragón, Dingena L. Schott

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

77 Citations (Scopus)


In this research, a universal framework for automated calibration of microscopic properties of modeled granular materials is proposed. The proposed framework aims at industrial scale applications, where optimization of the computational time step is important. It can be generally applied to all types of DEM simulation setups. It consists of three phases: data base generation, parameter optimization, and verification. In the first phase, DEM simulations are carried out on a multi-dimensional grid of sampled input parameter values to generate a database of macroscopic material responses. The database and experimental data are then used to interpolate the objective functions with respect to an arbitrary set of parameters. In the second phase, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the calibration multi-objective optimization problem. In the third phase, the DEM simulations using the results of the calibrated input parameters are carried out to calculate the macroscopic responses that are then compared with experimental measurements for verification and validation. The proposed calibration framework has been successfully demonstrated by a case study with two-objective optimization for the model accuracy and the simulation time. Based on the concept of Pareto dominance, the trade-off between these two conflicting objectives becomes apparent. Through verification and validation steps, the approach has proven to be successful for accurate calibration of material parameters with the optimal simulation time.

Original languageEnglish
Pages (from-to)1393-1403
JournalAdvanced Powder Technology
Issue number6
Publication statusPublished - 2018


  • Discrete element method (DEM)
  • Genetic algorithm (GA)
  • Inverse analysis
  • Multi-objective optimization
  • NSGA
  • Parameter calibration


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