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Predicting Surface Wear in Industrial Equipment Using Rocky DEM

Continuous operation of equipment that handles solids causes wear and tear on its components. Maintenance and operation personnel are often faced with challenges such as predicting the equipment life and downtime for replacement parts. This often becomes an important decision making criterion when selecting equipment from different vendors. Surface wear in equipment used in transport and crushing of ores and rocks in the mining industry, and drilling and pipe flow for civil and oil & gas applications,  remains a common challenge. Disruptions in operations can result in substantial losses and reduced profits. For example, downtime of continuously operating mills due to replacement of worn liners can often cause losses over $100,000 per hour. 

The discrete element method (DEM), which is based on first principle physics, is a predictive tool for analyzing the performance of bulk solids equipment. DEM has proven to be very helpful in predicting wear by enabling increased process insight and evaluation of a large number of possible solutions to reduce wear problems. Using Rocky DEM, engineers can easily evaluate how different operating conditions and equipment designs can affect the performance of their machines virtually on their computer. This capability empowers engineers to virtually troubleshoot, optimize processes, and prototype new designs, leading to a significant reduction in losses and maintenance downtime. 

Prediction of stresses on a geometry

Before predicting how a surface wears, the forces and stresses that it is subjected to must be accurately computed. In Rocky, the transient variation of normal and shear stresses on the surface, and its related work, are computed accurately and viewed easily. For example, accurate power draw can be used to predict mill performance, as illustrated in this case study.  

In another study, engineers at Conveyor Dynamics identified areas of high shear stress in a transfer chute, and predicted potential problems of elevated belt wear and blockage using Rocky. Engineers modified the design to include a deflector, which resulted in 40% less wear than observed in the initial design (Figure 1). Also, solid induced loads on the structure were exported from Rocky to Ansys Mechanical to compute von Mises stresses on the chute structures using DEM-FEA coupling. Find out more from the self-explanatory video below.

Figure 1 – Receiving Belt Impact and Shear Intensities

In another example, Rocky DEM was used to successfully troubleshoot material failure and breakage problems with transfer plates at Codelco Chile. Their engineers obtained a quantitative estimate of the normal stresses on the plates in the original design and then tested new designs in Rocky, ultimately increasing the life of these transfer plates.

3D Wear Model in Rocky

In addition to computing accurate forces and stresses, Rocky DEM also implements a validated Archard’s wear model. Rocky provides an accelerated wear model so that months of wear patterns observed in the field can be predicted after a few minutes of virtual simulation. Essentially, shear forces on a boundary element (with area A) cause nodal suppression (dh), so that the loss in volume is related to the shear work (dw). Thus, A.dh = Cmodel.dw, where Cmodel is the wear parameter and is a material parameter that can be calibrated against experimental data.

With calibrated wear rate parameters, these models accurately capture both wear and wear patterns on geometries, such as shown in this video.

The video below shows the simulation of a deflector positioned above a High-Pressure Grinding Roll (HPGR), showing wear over time. Non-spherical particles are used to represent the bulk material with high fidelity, which affects the forces and the positions where particles are colliding with the geometry’s surface.

Dr. Rahul Bharadwaj, author

Rahul Bharadwaj

Vice President of Rocky DEM Business Development and Technical Services

Dr. Rahul Bharadwaj has over a decade of experience in the development, validation, and application of computational tools such as Discrete Element Modeling (DEM), CFD, and FEA. After completing his Ph.D. in Mechanical Engineering at Purdue University, he was a senior scientist at Pfizer R&D and senior engineer at Jenike and Johanson Inc, where he has several years of consulting experience in the field of bulk material handling. He is an active member of the American Institute of Chemical Engineers (AIChE), the American Association of Pharmaceutical Scientists (AAPS), and is the founder and past-chair of AAPS's Process Modeling and Simulation Focus Group.

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