Sensing Mechanism and Real-Time Computing for Granular Materials

Research output: Contribution to journalArticle

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Abstract

The discrete element method (DEM) has been widely used to study the mechanical behavior of granular materials. However, potential error accumulation over the required large number of time steps due to the explicit time integration in DEM simulations may undermine the simulation accuracy. In this paper, a computing scheme based on real-time data fusion between a sensing mechanism and traditional DEM is developed and investigated. The developed sensing mechanism and real-time (SMART) computing consists of: (1) real-time data acquisition of particle kinematics through a wireless instrumentation called SmartRocks that are embedded at discrete locations in a granular assemblage, and (2) a built-in data-fusion-based algorithm using the Kalman filter to integrate the prediction generated by DEM and the measurements reported by SmartRocks. The performance of the SMART computing algorithm is investigated by simulating a series of ball collision experiments consisting of two-ball center-to-center, two-ball off-center, and multiball collisions. It is concluded that SMART computing can improve the accuracy of DEM simulations. The results of this study suggest that the location and number of SmartRocks, whose recordings are fused into DEM simulations to recondition the particle movements, are important to the accuracy of SMART computing.

Original languageEnglish (US)
Article number04018023
JournalJournal of Computing in Civil Engineering
Volume32
Issue number4
DOIs
StatePublished - Jul 1 2018

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Granular materials
Finite difference method
Data fusion
Kalman filters
Data acquisition
Kinematics
Experiments

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Computer Science Applications

Cite this

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title = "Sensing Mechanism and Real-Time Computing for Granular Materials",
abstract = "The discrete element method (DEM) has been widely used to study the mechanical behavior of granular materials. However, potential error accumulation over the required large number of time steps due to the explicit time integration in DEM simulations may undermine the simulation accuracy. In this paper, a computing scheme based on real-time data fusion between a sensing mechanism and traditional DEM is developed and investigated. The developed sensing mechanism and real-time (SMART) computing consists of: (1) real-time data acquisition of particle kinematics through a wireless instrumentation called SmartRocks that are embedded at discrete locations in a granular assemblage, and (2) a built-in data-fusion-based algorithm using the Kalman filter to integrate the prediction generated by DEM and the measurements reported by SmartRocks. The performance of the SMART computing algorithm is investigated by simulating a series of ball collision experiments consisting of two-ball center-to-center, two-ball off-center, and multiball collisions. It is concluded that SMART computing can improve the accuracy of DEM simulations. The results of this study suggest that the location and number of SmartRocks, whose recordings are fused into DEM simulations to recondition the particle movements, are important to the accuracy of SMART computing.",
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Sensing Mechanism and Real-Time Computing for Granular Materials. / Liu, Shushu; Huang, Hai; Qiu, Tong; Shen, Shihui.

In: Journal of Computing in Civil Engineering, Vol. 32, No. 4, 04018023, 01.07.2018.

Research output: Contribution to journalArticle

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