A validated co-simulation of grab and moist iron ore cargo: Replicating the cohesive and stress-history dependent behaviour of bulk solids

Mohajeri, M. Javad; Kluijver, Wilbert de; Helmons, Rudy; Rhee, Cees van; Schott, Dingena L.

doi:10.1016/j.apt.2021.02.017

Cited by 16 publications

(6 citation statements)

References 26 publications

(45 reference statements)

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“…Therefore, the effectiveness of their method has not been confirmed for the GTSU system. In another piece of research, M. Javad Mohajeri et al proposed a study titled 'A Validated Co-simulation of Grab and Moist Iron Ore Cargo: Replicating the Cohesive and Stress-history Dependent Behavior of Bulk Solids' [28]. In their study, the researchers utilized point cloud data to capture the surface of bulk material, employing the Screened Poisson Surface Reconstruction method developed by Michael Kazhdan et al [29].…”

Section: Results and Discussion Of A Working Point Extraction Modelmentioning

confidence: 99%

Design of an AI Model for a Fully Automatic Grab-Type Ship Unloader System

Ngo,

Lee,

Kim

et al. 2024

JMSE

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In seaports, the automatic Grab-Type Ship Unloader (GTSU) stands out for its ability to automatically load and unload materials, offering the potential for substantial productivity improvement and cost reduction. Developing a fully automatic GTSU, however, presents a unique challenge: the system must autonomously determine the position of the cargo hold and the coordinates of the working point and identify potential hazards during material loading and unloading. This paper proposes AI models designed to detect cargo holds, extract working points, and support collision risk warnings, utilizing both the LiDAR sensor and the camera in the GTSU system. The model for cargo hold detection and collision warning was developed using image data of the cargo hold and grab, employing the You Only Look Once model. Concurrently, the model responsible for extracting the coordinates of working points for the GTSU system was designed by integrating the cargo hold detection and point cloud processing models. After testing the AI models for the lab-scale GTSU, the results show that the cargo hold detection and collision warning models achieve an accuracy of approximately 96% and 90%, respectively. Additionally, the working point coordinates extracted from the sensor system show a deviation of 5–10% compared to traditional measurements.

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Section: Results and Discussion Of A Working Point Extraction Modelmentioning

confidence: 99%

Design of an AI Model for a Fully Automatic Grab-Type Ship Unloader System

Ngo,

Lee,

Kim

et al. 2024

JMSE

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“…For the ring shear and ledge angle of repose tests, the particle-particle coefficient of friction was the most significant parameter, followed by the shear modulus (stiffness), surface energy and plasticity ratio. These results were then used to optimise a full-scale industrial grab design employed for unloading iron ore cargo from bulk carrier ships [119,120].…”

Section: Methodology 7: Ring Shear Ledge Consolidation and Penetratio...mentioning

confidence: 99%

Review: The Calibration of DEM Parameters for the Bulk Modelling of Cohesive Materials

Coetzee

Scheffler

2022

Processes

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Granular materials are abundant in nature, and in most industries, either the initial constituents or final products are in granular form during a production or processing stage. Industrial processes and equipment for the handling of bulk solids can only be improved if we can understand, model and predict the material behaviour. The discrete element method (DEM) is a numerical tool well-suited for this purpose and has been used by researchers and engineers to analyse various industrial applications and processes. However, before any bulk scale modelling can be undertaken, the input parameters must be carefully calibrated to obtain accurate results. The calibration of parameter values for non-cohesive materials has reached a level of maturity; however, the calibration of cohesive materials requires more research. This paper details the most prevalent contact models used to model cohesive materials—presented in a consistent notation. Moreover, the significant differences between the models are highlighted to provide a reference for engineers and researchers to select the most appropriate model for a specific application. Finally, a critical review of calibration experiments and methodologies often used for cohesive materials is also presented. This provides a solid basis for DEM practitioners to select the most appropriate calibration methodology for their application and for researchers to extend the current state-of-the-art practices.

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“…In large scale applications like the grab validation study on spherical iron ore pellets by Lommen resulted in a 2% error for the payload with three repetitions [13]. Another grab validation study for cohesive iron ore reported a standard deviation of the average payload of 5,74% for three repetitions [17]. Combined with the confidence intervals this leads to 95% confidence intervals of up to +/− 10,5% around the mean in the discussed cases.…”

Section: Introductionmentioning

confidence: 99%