(339a) A Systematic Approach to DEM Material Model Calibration Conference: AIChE Annual MeetingYear: 2009Proceeding: 2009 AIChE Annual MeetingGroup: Particle Technology ForumSession: Poster Session: Particle Technology Forum Time: Tuesday, November 10, 2009 - 6:00pm-8:00pm Authors: Favier, J., DEM Solutions Ltd. Curry, D., DEM Solutions Ltd. Processing and handling of particulate material is a significant factor in the cost of production in many industries. Some of the ways in which these costs can be reduced is by increasing production efficiency, reducing product waste, or increasing processing through-put. In each of these cases, new designs and/or operational techniques are required. By achieving a small percentage improvement in performance, a large amount of savings may be gained. To assess the benefits of new designs, testing must be performed in either an experimental or ?virtual? mode. By using virtual testing to minimize the number of design options and hence the number or prototypes and test points required for engineering decisions, huge savings may again be made. Discrete Element Modeling (DEM) is such a tool for modeling particulate flows and processes. One challenge with DEM is to have the virtual experiments match the physical experiments within an acceptable level of accuracy. To achieve this, DEM requires the input of material properties and interaction parameters; e.g., coefficients of friction, shear modulus, etc. The current methods for determining such parameters are based on physical or virtual tests. In many cases, experimentally-derived material properties will not reproduce the desired material behavior in a DEM simulation to the level of accuracy that is required. In addition, simple tests may not be able to provide the insight into which parameters significantly influence the desired result. The purpose of simulating a particulate process with DEM is to achieve an accurate representation of the bulk behavior of the material. This is done by defining material properties that affect the material behavior at the particle scale which consequently create the overall bulk behavior. When the physical particle size is small relative to the volume of bulk material, further assumptions and scaling are needed to make the problem computationally tractable. Since direct experimental measurement of these material properties is not straightforward, and particle scaling reduces the precision of these properties, extracting the correct material properties from one experiment or simulation and applying it to another is a great challenge. In many cases, the direct measurement of particle properties from a bulk process is not possible. Likewise, measuring individual particle properties and applying them to a simulation of a bulk material process may not yield accurate results either. Much of this is because a DEM simulation will usually consist of uniform particles, (with the exception of a possible size distribution), that are intended to approximate the actual particle shapes. For bulk flows, perfectly matching the sizes and shapes of all the particles is not possible and further increases the approximation errors of the simulation. In addition, parameters that can be measured may not have a direct correlation to a variable that is needed in the simulation. The goal of this approach is to devise a system that includes basic experiments and an optimization procedure that calculates the DEM material properties necessary for simulations to produce results within a suitable accuracy range. To achieve this goal, four major program tasks have been defined: 1. Develop experiments suitable for characterizing bulk material for subsequent DEM analysis. 2. Develop advanced contact models, particle shapes, and particle scaling rules for the EDEM software package that have the proper physics to model the materials of interest. 3. Develop an optimization algorithm that can take the experimental data as an input and select the parameters for the contact model to best match the simulation with the experiments. 4. Apply the selected experiments to a system to validate the model.