Review: Calibration of the discrete element method

By C. Coetzee. 2017

666 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computational Mechanics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/06b33f8e9036bc56c95afcf5368759b8d5481069

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. O. Al-Amoudi
2. Hongyang Cheng
3. H. M. Al-Hashemi
4. Haruyuki Yamamoto
5. Stefan Luding
6. V. Magnanimo
7. Klaus Thoeni
8. T. Shuku

In Computational Mechanics

1. O. Al-Amoudi
2. Hongyang Cheng
3. H. M. Al-Hashemi
4. Haruyuki Yamamoto
5. Stefan Luding
6. V. Magnanimo
7. Klaus Thoeni
8. T. Shuku

In Mechanical Engineering

1. O. Al-Amoudi
2. Hongyang Cheng
3. H. M. Al-Hashemi
4. Haruyuki Yamamoto
5. Stefan Luding
6. V. Magnanimo
7. Klaus Thoeni
8. T. Shuku

Citations in the corpus

• An iterative Bayesian filtering framework for fast and automated calibration of DEM models (2019, methodology): “Among a handful of optimization methods for the calibration of DEM models [14,15], the design of experiments (DOE) is efficient in searching for possible solutions with a manageable size of DEM model evaluations [16–20].”
• Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter (2018, methodology): “In future research, it will be worth investigating the applicability of the SQMC and SIS to DEM calibrations against more complex behavior of granularmaterials, such as rockfalls [11], avalanches [51] and silo discharges [8].”
• A review on the angle of repose of granular materials (2018, methodology): “Moreover, a number of other studies that utilized the angle of repose, especially in a DEM, have been reported [92].”