Purpose
The purpose of this paper is to optimize the structure of plough blades in a ploughshare mixer using the discrete element method (DEM) simulations.
Design/methodology/approach
Using the validated DEM model, three numerical tests are conducted to determine how the mixing performance evolves as structural parameters of blades change. Results from the analysis provide basis for structure optimization of blades. The structural parameters include sweep angle of blade γ, regular axial pitch p and regular circumferential angular offset α. The parameters to evaluate mixing performance include mass flow rate and Lacey index.
Findings
The DEM results show that the mixing performance at γ of 35° is better than 15°, 25° and 45°. The mixer which has a p of less than or equal to 1.11 · b is more efficient than the mixer which has a p greater than 1.11 · b, where b is tail width of blade. The circumferential symmetric distribution of blades (α = 180°) is more beneficial to improve the mixing performance in comparison with the circumferential asymmetric distribution (α < 180°). Based on the results, an optimized mixer with a γ of 35°, a p of 0.61 · b and an α of 180° is proposed, which has a better mixing performance compared to all mixers listed.
Originality/value
The structural parameters of blades, including γ, p and α, are found to be critical for good mixing. From the view angle of structure optimization of plough blades, a new ploughshare mixer with a γ of 35°, a p of 0.61 · b and an α of 180° is investigated and recommended for improving mixing efficiency.
Summary
In the discrete element method (DEM) simulation for wear prediction, structural boundary is now represented extensively by triangular meshes with high resolution, which brings a huge computational cost. A DEM‐based method for predicting the wear evolution of structural boundary has been developed for computational efficiency. The structural boundary subjected to wear is represented by the spherical boundary elements in the DEM simulation in combination with the inside triangles and fitting curved surface in wear prediction. Wear prediction is performed through a series of evolution steps. In each evolution step, the collision energies by particles at structural boundary are collected via the DEM simulation and assigned to the boundary elements. Then, the volume losses of structural boundary are predicted across each relevant boundary element. Finally, the new geometry of structural boundary in response to wear is described by moving the boundary elements along the depths of wear individually. Through converting the contact detection between structural boundary and particles into between spherical boundary elements and particles, our method greatly reduces the computational cost in the DEM simulation. Through two numerical tests, our method has been verified to be an efficient and accurate method for the wear prediction of structural boundaries with different resolutions.
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