Sliding wear is a common phenomenon in the iron ore handling industry. Large-scale handling of iron ore bulk-solids causes a high amount of volume loss from the surfaces of bulk-solids-handling equipment. Predicting the sliding wear volume from equipment surfaces is beneficial for efficient maintenance of worn equipment. Recently, the discrete element method (DEM) simulations have been utilised to predict the wear by bulk-solids. However, the sensitivity of wear prediction subjected to DEM parameters has not been systemically investigated at single particle level. To ensure the wear predictions by DEM are accurate and stable, this study aims to conduct the sensitivity analysis at the single particle level.
In this research, pin-on-disc wear tests are modelled to predict the sliding wear by individual iron ore particles. The Hertz–Mindlin (no slip) contact model is implemented to simulate interactions between particle (pin) and geometry (disc). To quantify the wear from geometry surface, a sliding wear equation derived from Archard’s wear model is adopted in the DEM simulations. The accuracy of the pin-on-disc wear test simulation is assessed by comparing the predicted wear volume with that of the theoretical calculation. The stability is evaluated by repetitive tests of a reference case. At the steady-state wear, the sensitivity analysis is done by predicting sliding wear volumes using the parameter values determined by iron ore-handling conditions. This research is carried out using the software EDEM® 2.7.1.
Numerical errors occur when a particle passes a joint side of geometry meshes. However, this influence is negligible compared to total wear volume of a wear revolution. A reference case study demonstrates that accurate and stable results of sliding wear volume can be achieved. For the sliding wear at steady state, increasing particle density or radius causes more wear, whereas, by contrast, particle Poisson’s ratio, particle shear modulus, geometry mesh size, rotating speed, coefficient of restitution and time step have no impact on wear volume. As expected, increasing indentation force results in a proportional increase. For maintaining wear characteristic and reducing simulation time, the geometry mesh size is recommended. To further reduce simulation time, it is inappropriate using lower particle shear modulus. However, the maximum time step can be increased to 187% TR without compromising simulation accuracy.
The applied coefficient of sliding wear is determined based on theoretical and experimental studies of a spherical head of iron ore particle. To predict realistic volume loss in the iron ore-handling industry, this coefficient should be experimentally determined by taking into account the non-spherical shapes of iron ore particles.
The effects of DEM parameters on sliding wear are revealed, enabling the selections of adequate values to predict sliding wear in the iron ore-handling industry.
The accuracy and stability to predict sliding wear by using EDEM® 2.7.1 are verified. Besides, this research accelerates the calibration of sliding wear prediction by DEM.
The authors are grateful to China Scholarship Council-CSC (No. 201206170158) for funding this research.
Chen, G., Schott, D.L. and Lodewijks, G. (2017), "Sensitivity analysis of DEM prediction for sliding wear by single iron ore particle", Engineering Computations, Vol. 34 No. 6, pp. 2031-2053. https://doi.org/10.1108/EC-07-2016-0265Download as .RIS
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