With the bulk material created, we can now add a particle to it. This can be achieved using the createMultisphereParticleType function. To utilize this function, we need to specify the bulk material and define the particle name. In this case, we will name it as MyParticle. By default, the createMultisphereParticleType function creates a triple-sphere shape.
Once the particle is created, we can adjust the radius and position of the spheres that constitute our particle using the setMultisphereSpherePosition and setMultispherePhysicalRadius functions. Then, we use the setAutoCalculateParticleProperties function to ensure the accurate calculation of the particle's properties.
After creating the bulk material and particles, the next step is to create the equipment material. We will name the equipment material as EquipMaterial. To create the equipment material, we will use the createMaterial function, but this time we need to set the last argument to False. The properties of EquipMaterial are shown in the table below:
Now that all the materials in our simulation have been created we can set the interaction parameters between them. This can be achieved using the createInteraction function. The interaction parameters are specified in the table below:
4. Defining the geometries
To create the geometries, we need to specify the coordinates of each node as well as the connectivity matrix. The connectivity matrix represents the relationships between the vertices (points) and the triangles in the mesh. Each row of the connectivity matrix corresponds to a triangle in the mesh.
In this example, we create two geometries: a virtual square named MySquare and a physical cube named MyCube. After specifying the coordinates and connectivity matrix for each geometry, we can use the createGeometry function to create them. Additionally, if you have an existing geometry, you can import it into your simulation using Python libraries like trimesh. These libraries provide access to the geometry's nodes and connectivity list, allowing you to import the geometry into your simulation seamlessly.
# Define the 4 corners of the square square_coords = np.array([[-0.5, -0.5, 0.5],[0.5, -0.5, 0.5],[0.5, 0.5, 0.5],[-0.5, 0.5, 0.5]]) # Define the 2 triangles that make up the square square_triangles = np.array([[0,3,1], [1,3,2]]) # Define the 8 corners of the cube cube_coords= np.array([[-1, -1, -1], [1, -1, -1], [1, 1, -1], [-1, 1, -1], [-1, -1, 1], [1, -1, 1], [1, 1, 1], [-1, 1, 1]]) # Define the 12 triangles that make up the cube cube_triangles= np.array([[0,3,1],[1,3,2],[0,4,7],[0,7,3],[4,5,6],[4,6,7],[5,1,2],[5,2,6],[2,3,6],[3,7,6],[0,1,5],[0,5,4]]) # Create the square deck.createGeometry('MySquare', 'EquipMaterial', square_coords, square_triangles, geometry_type='Virtual') # Create the cube deck.createGeometry('MyCube', 'EquipMaterial', cube_coords, cube_triangles, geometry_type='Physical')
4.1. Defining geometry motion, particle factory and particle size distribution
Now that the geometries are created, we can add a linear rotation to MyCube and a particle factory to MySquare. To add a linear rotation to MyCube, we use the createKinematic function and specify the parameters listed in the table below:
| | |
|---|
| Name | Box_rotation |
| Type | Linear Rotation |
| Start Time | 2 s |
| Initial Velocity | 10 rpm |
| Axis Start Point | (0, 0, -1) |
| Axis End Point | (0, 0, 1) |
To add a particle factory to MySquare, we use the createFactory function and specify the parameters listed in the table below:
| | |
|---|
| Name | BulkMaterial_Factory |
| Material Name | BulkMaterial |
| Velocity | fixed |
| Fixed Velocity | (0, 0, -1) m/s |
| Start Time | 0 s |
| Total to Create | 30000 particles |
| Creation Rate | 15000 particles/s |
Finally, we can define the particle size distribution using the setParticleUserDefinedSizeDistribution function. Here, we specify the scale and the percentage mass in each size class. The last argument of the function is set to True so that the scales are applied to the particle radius.
# Set geometry motion - linear rotation rotation_in_rpm = 10 rotation_in_rad_s = rotation_in_rpm * (2*np.pi/60) deck.createKinematic('MyCube', kinematic_name= 'Cube_rotation', kinematic_type= 'Linear Rotation', start_time= 2.0, rotation_init_angular_velocity_value= rotation_in_rad_s, rotation_start_point= [0.0, 0.0, -1.0], rotation_end_point= [0.0, 0.0, 1.0]) # Create particle factory deck.createFactory('MySquare', 'BulkMaterial_Factory', 'BulkMaterial', velocity= 'fixed', fixed_velocity=[0.0, 0.0, -1.0], start_time= 0, is_mass_creation= False, total_to_create= 30_000, is_mass_generation= False, creation_rate= 15_000) # Define the particle size distribution scales = np.array([1.0, 1.2, 1.5, 1.7]) mass_percentages = np.array([45, 30, 5, 20]) deck.setParticleUserDefinedSizeDistribution('MyParticle', scales, mass_percentages, is_scaled_by_radius= True)
5. Defining the physics model
Finally, we need to define the physics models that we'll use in our simulation. For particle-particle contacts, we'll use the Hertz-Mindlin model with JKR cohesion. Since the default Hertz-Mindlin model is already present, we first need to delete it using the deletePhysicsModelSurfSurf function. Then, we specify the surface energy value for the particle-particle contacts and add the model to our simulation using the createPhysicsModelSurfSurf function. For the particle-geometry contacts, we'll keep the default Hertz-Mindlin model and add the Relative Wear model. Since the Relative Wear model doesn't require any additional parameters, we can simply add it to our simulation using the createPhysicsModelSurfGeom function.
# Delete the default Hertz-Mindling model deck.deletePhysicsModelSurfSurf('Hertz-Mindlin (no slip)') # Define the JKR surface energy model_data=np.array([('BulkMaterial', 'BulkMaterial', 2)], dtype=([('name1', 'S256'), ('name2', 'S256'), ('value', '<f8')])) # Add the particle-particle contact model deck.createPhysicsModelSurfSurf('Hertz-Mindlin with JKR',{"data": model_data}) # Add the particle-geometry contact model deck.createPhysicsModelSurfGeom('Relative Wear',{})
6. Setting the environment and saving the deck
In the final step of our simulation setup, we need to define the minimum and maximum values of the simulation domain. This can be done using the setDomainMin and setDomainMax functions. The domain represents the bounding box within which the simulation will take place. We also need to save our work using the save function to store the simulation deck for future use.
# Set the minimum values of the simulation domain deck.setDomainMin([-1.01, -1.01, -1.01]) # Set the maximum values of the simulation domain deck.setDomainMax([1.01, 1.01, 1.01]) # Save the simulation deck deck.save()
