# Revolving Rice Dryer Using DPM CFD Simulation

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In this project, a revolving rice dryer using one-way DPM has been simulated and the results of this simulation have been investigated.

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## Rice Dryer Using DPM (Revolving ), CFD Simulation Ansys Fluent Training

In this project, a revolving rice dryer device was simulated using Evaporating droplets with a one-way DPM model in Ansys Fluent software, and then the results were investigated. Hot air enters the rice dryer through the holes in a porous tube located at the center of the enormous chamber. About three million rice particles are injected with 15% moisture droplets into a chamber revolving with 100rpm angular speed.

## Geometry & Mesh

The 3D geometry was generated in Design Modeler software. A cylinder with a 4.5m diameter and 1.68m  height.

In addition, an unstructured mesh grid was carried out using Ansys meshing software, and overall, 2703907 elements were generated.

## CFD Simulation

Several assumptions have been considered to simulate Revolving rice dryer, including:

• The simulation is Transient(unsteady) in order to capture the behavior of fluid and particles over time.
• The pressure-based solver type was used due to the incompressibility of the working fluid(air).
• The One-way Discrete Phase Model(DPM) was utilized, so the grains of rice particles were injected with our desired condition into the fluid domain.
• Gravitational acceleration effects were considered 9.81m/s^2 in z-direction.

The following table represents a summary of the solution:

 Models(Viscous) Energy On Viscous k-epsilon Standard Standard Wall Function Species Species Transport Mixture-template Discrete Phase Model Interaction with continuous phase Unsteady particle tracking off on Injections Injection Type Number of streams Surface – wallinlet Particle Type Droplet Material Evaporating species Diameter distribution Water-liquid H2o Uniform Diameter 0.00214m Stop Time 8s Temperature 300K Velocity Magnitude 1m/s Start Time 0s Total Flow Rate 0.05kg/s Materials Mixture-template Definition method Fluent database Material name Air Droplet Particle Definition method Fluent database Material name Water-liquid Volatile component fraction = 15% Cell zone condition Material name Mixture-template Frame Motion Speed 100rpm Boundary condition Inlet Type Velocity inlet Velocity magnitude 0.3m/s Turbulent intensity 10% Hydraulic Diameter 0.0254 Temperature 308K DPM BC Type escape Outlet Type Wall DPM BC Type reflect Tube Type Wall Coupled — DPM BC Type reflect Solver configuration Pressure-velocity coupling Scheme Coupled Spatial Discretization Gradient Least squares cell-based Pressure Second-order Momentum Second-order upwind Turbulent kinetic energy First-order upwind Turbulent dissipation rate First-order upwind H2o Second-order upwind Energy Second-order upwind Initialization Initialization methods Standard Initialization Run calculation Time step size 0.1 Number of time steps 1600 Max iterations per time step 20

## Results

After injecting about three million rice particles through the inlet, they gather around the porous tube in the middle of the chamber. As shown in figure 1, H2o mass fraction concentrated around the tube at first, but after a while, due to the release of hot air from tube holes to the chamber, the evaporating process begins and causes vaporizing material(H2o) to disperse; This may reduce the moisture of rice particles which was 15% at the beginning and as a result, the diameter decrease that depicted in figure 2. In addition, the Rotational speed of the chamber accelerates the circulation of hot air.

## Reviews

1. Prof. Sage Boehm Jr.

I’m impressed with the way the rotational speed of the chamber and the hot air worked together to decrease the moisture content in the rice particles. The detailed introduction of the phases and modeling process delivered straightforward insights into your simulation. It’s always quite fascinating to visualize the drying process and see the interaction between heat and the revolving particles. Outstanding work!

• MR CFD Support

Thank you for your review! We’re elated to hear that the simulation met your expectations and that you found the visualization and results enlightening. If you’re interested in learning more about CFD and our other simulations, we’re always here to assist with high-quality training and detailed explanations. Your positive feedback is immensely appreciated and motivates us to continue providing top-notch simulations. Thank you once again!

2. Twila Ward V

The description of the dryer’s performance is quite comprehensive, but how exactly is the collision behavior of the rice particles with the chamber wall modeled in the simulation?

• MR CFD Support

In the simulation, the collision of rice particles with the chamber wall is captured using a Discrete Phase Model (DPM). The boundary condition set for the wall is reflective, meaning when particles hit the wall, they reflect off it rather than sticking to it or escaping. This ensures the simulation mirrors the physical behavior of rice particles inside a revolving dryer, successfully accounting for the interactions between particles and the revolving chamber wall.

3. Devante Baumbach

What a thorough and data-rich simulation of the rice dryer. The intersection of CFD and practical applications like agriculture assures exciting progress in optimization! The details make for easy visualization of the process.

• MR CFD Support

Thank you for your compliment on the rice dryer simulation! At MR CFD, we value the practical application of CFD in various industries and strive to deliver comprehensive and detailed analyses to aid in optimization and development. We’re glad that you found the information useful and that it helped enhance your understanding of the process involved.

4. Dr. Humberto Jakubowski

The explanation is quite detailed but I couldn’t find the information regarding how the outlet condition is defined for the rice dryer simulation. Could you provide some insights into that?

• MR CFD Support

In this simulation, the outlet boundary condition is typically defined as an ‘escape’ condition for the Discrete Phase Model (DPM) to allow particles to leave the computational domain without any restriction once they reach the outlet. The outlet for the continuous phase, the air, would generally be set up as a pressure outlet where the pressure is assumed to be atmospheric. However, this can vary depending on the specifics of the simulation setup and goals.

5. Paolo Mueller V

This sounds like an extremely complex simulation with many variables. Can you clarify how the DPM model differentiates between evaporating and non-evaporating rice particles in the simulation?

• MR CFD Support

In this simulation, all rice particles start with 15% moisture content, which means they are capable of undergoing evaporation. The one-way Discrete Phase Model (DPM) accounts for the evaporation by calculating the change in mass of the water droplets as they lose moisture (evaporate) due to the hot air. The properties of particles that have undergone evaporation are automatically updated by the model based on the loss of moisture, which results in changes such as a reduction in their diameter. Non-evaporating particles would refer to particles that don’t experience significant moisture loss either due to shorter exposure times or conditions not conducive to evaporation.

6. Daphney Abernathy

The training was highly informative, and I especially appreciated the detailed setup of the DPM conditions. The simulation seemed complex; were there any specific challenges in tracking the three million rice particles, given the high rotational speed and the potential for collisions among particles and with the dryer walls?

• MR CFD Support

The process of tracking a large number of particles, especially in a high-speed revolving environment, certainly poses challenges. However, ANSYS Fluent handles these using the one-way Discrete Phase Model, which simplifies the interactions by not considering feedback on the continuous phase from particles. Collisions and interactions with the walls are managed through defined boundary conditions, ensuring accurate simulation of particle behavior without significantly increasing the computational cost.