Particle Life Cycle and Motion in DPM: A Complete Guide

In many engineering simulations, from engine sprays to cyclone separators, we need to understand what happens to individual particles. Where do they go? How fast do they move? Do they hit a wall or fly out of the system? This entire journey is called the particle life cycle.

ANSYS Fluent uses the Discrete Phase Model (DPM) to calculate this journey. Understanding how DPM works is crucial for setting up an accurate simulation. This guide will explain the core concepts of DPM particle tracking. We will cover:

• The complete particle life cycle, from injection to its final fate.
• The basic motion equations that control how a particle moves.
• The difference between steady and unsteady particle tracking and how to choose the right one.

This blog is a continuation of our DPM series. For a basic understanding of what a DPM parcel is, we recommend reading our previous blog first. Mastering these concepts is key to performing reliable DPM CFD simulations.

 

Understanding the Particle Life Cycle in DPM

Before we look at complex equations, let’s understand the particle’s journey. Think of it as a story with a beginning, a middle, and an end. In a DPM simulation, this story is broken down into clear stages as the particle moves from one mesh cell to the next.

Here are the main stages:

1. Injection State: This is the “birth” of the particle. Here, we define its starting properties like size, velocity, and temperature.
2. Entry State: This happens every time the particle enters a new computational cell. Its properties are updated based on its position.
3. Current State: This is the journey through the current cell. As it moves, the solver calculates the forces acting on it and any heat or mass it exchanges with the surrounding fluid.
4. Exit State: This is the moment the particle leaves the current cell and prepares to enter the next one.

5. Final Fate: This is the end of the particle’s journey. It occurs when the particle reaches a boundary. It can escape the domain, get trapped on a wall, or reflect off a surface. We explain these options in our complete guide to DPM boundary conditions in ANSYS Fluent.

This step-by-step process is repeated for every particle in the simulation, allowing us to accurately track its entire path and behavior.

Figure 1: The stages of a particle’s life cycle in a DPM simulation, from its injection to its final destination.

Particle Motion: The Fundamental Equations

The movement of every particle in a DPM simulation follows a basic law of physics: Newton’s Second Law. This law says that the particle’s acceleration is caused by the sum of all forces acting on it. The solver calculates these forces at every step to predict the particle’s path, or trajectory.

Particle Acceleration = Drag Force + Gravity/Buoyancy Force + Other Forces

Let’s break down the most important forces:

1. The Drag Force (F_D) This is usually the most significant force. It is the resistance the particle feels as it moves through the fluid. A particle moving faster than the fluid will be slowed down, and a particle moving slower will be sped up. The drag force depends on the difference in velocity between the fluid (u) and the particle (u_p).
2. The Gravity and Buoyancy Force (F_g) This force represents the particle’s weight pulling it down (gravity) and the fluid pushing it up (buoyancy). The solver combines these into a single term. This force is very important when there is a large density difference between the particle (ρ_p) and the fluid (ρ).
3. Other Forces For more complex simulations, Fluent can include many other forces, such as:

• Pressure Gradient Force: Caused by pressure differences in the fluid around the particle.
• Virtual Mass Force: The force required to accelerate the fluid that surrounds the particle.
• Saffman’s Lift Force: A lift force that acts on small particles in shear flows.

Figure 2: Saffman Lift force applied on a particle

So, how does the solver use these forces to find the particle’s new position? It calculates all the forces on the particle at one moment, which gives its acceleration. It then uses this acceleration to find the particle’s new velocity and, finally, its new position after a very small amount of time (Δt). This process is called integration. It is repeated over and over, creating the full particle trajectory.

Particle Trajectory Equation (Newton’s Law):

• Where u_p is the particle velocity, u is the fluid velocity, F_D is the drag function, g is gravity, ρ_p is particle density, ρ is fluid density, and F_other are additional forces.

Particle Position Update:

dx/dt= u_p

• This simply means the change in position (dx) over a change in time (dt) is equal to the particle’s velocity.

Figure 3: Different motions a particle experience during its movement

 

Steady vs Unsteady Particle Tracking: Choosing the Right Method

After setting up the physics, you must tell the solver how to calculate the particle trajectories. This is a critical choice that affects both the accuracy of your results and the time it takes to get them. ANSYS Fluent gives you two main options: Steady and Unsteady particle tracking. The best choice depends on your simulation goals. We`ve got 3 main approaches:

• Steady tracking with steady flow
• Unsteady tracking with steady flow
• Unsteady tracking with unsteady flow

Let’s look at each one.

Figure 4: The main particle tracking options available in the ANSYS Fluent DPM panel.

Steady Tracking with Steady Flow

This is the simplest and fastest method. Think of it as calculating the final, time-averaged path of particles in a stable, unchanging flow.

• How it works: First, the solver calculates the continuous phase (the fluid flow) until it reaches a steady state. Then, the solver “freezes” this flow field. After that, it injects the particles and calculates their entire trajectory from the injection point to their final fate in one single, continuous step. Each particle is tracked completely along its entire trajectory until it either leaves the domain or hits a boundary. The fluid flow does not change while the particles are being tracked.

Figure 5: Flowchart for Steady Particle Tracking. The fluid flow is frozen before particles are tracked to their final fate.

• When to use it: This method is perfect for steady-state fluid simulations where your main goal is to find the final landing spot of particles. It is ideal when the particles themselves are very small or have a low mass flow rate, meaning they don’t really affect the fluid flow (this is called one-way coupling).
• Practical Example: Simulating a cyclone separator running at a constant speed to see where particles of different sizes are captured. Or, predicting where dust will settle in a cleanroom with a constant ventilation system. Practical examples where this method is ideal include analyzing the efficiency of a Dry Gas Filter, predicting collection in a Cyclone Separator, or simulating a Dry Powder Inhaler.

Figure 6: Practical CFD DPM simulation using steady tracking with steady flow

Unsteady Tracking with Steady Flow

This method is a middle ground. The fluid flow is still stable and unchanging, but we want to see how the particles move and spread out over time.

• How it works: The fluid flow is still calculated first and “frozen” in a steady state. However, instead of calculating the whole trajectory at once, the solver “advances” the particles from their last position for a small, specific particle time step. You define a Particle Time Step Size in DPM panel. You see the particles moving through the domain as if you were watching a movie.
• When to use it: Use this when the fluid flow is steady, but you are interested in the transient behavior of the particles themselves. This is useful for finding out how long it takes for particles to reach a certain location or how the concentration of particles changes over time.
• Practical Example: Determining the residence time distribution (RTD) of a chemical tracer in a mixing tank with a constant flow rate. Or, visualizing how smoke from a cigarette fills a room with a steady, constant ventilation. This approach is perfect for simulations like assessing Operating Room Ventilation or modeling Non-Premixed Combustion with fuel droplets.

Figure 7: Practical CFD DPM simulation using unsteady tracking with steady flow

 

Unsteady Tracking with Unsteady Flow

This is the most comprehensive and computationally expensive method. It is used when the fluid flow itself is changing over time, and this change affects how the particles move.

• How it works: This is a true transient simulation. Both the fluid flow and the particle positions are updated together at each time step. The solver advances the fluid flow for one time step, then it advances the particles through that newly calculated flow field, and then repeats the process.

Figure 8: Flowchart for unsteady particle tracking. The fluid flow and particle positions are updated together at each time step.

•  When to use it: This is essential for any true transient problem where the behavior of the fluid directly influences the particle paths in real-time. This is always used for simulations with two-way coupling, where the particles also exchange momentum and energy back to the fluid.
• Practical Example: Simulating fuel spray injection in an engine cylinder where the piston motion changes the flow. Or, tracking ash and debris from a volcanic eruption where wind patterns are constantly changing. This high-fidelity method is necessary for complex transient cases such as modeling Virus Distribution by Coughing or Spray Cooling on a Heat Sink.

Figure 9: Practical CFD DPM simulation using unsteady tracking with unsteady flow

Comparison Table: Which Method Should You Use?

Choosing the right method is essential for getting the results you need efficiently. Use this table as a guide.

Tracking Method	Fluid Flow Type	Particle Path Calculation	Best For Practical Examples
Steady Tracking	Steady	Calculated once from start to finish.	Quick results for final destinations: Cyclone separator efficiency, erosion patterns on a pipe bend, final deposition of dust.
Unsteady Tracking with Steady Flow	Steady	Calculated in steps over time.	Visualizing particle cloud evolution: Creating an animation of a spray, determining residence time distribution.
Unsteady Tracking with Unsteady Flow	Unsteady	Calculated in sync with the changing fluid flow at each step.	Highest accuracy for transient events: Pulsed injections, particle behavior in vortex shedding, engine combustion.

 

Conclusion

Understanding particle tracking in ANSYS Fluent is about knowing the story of your particles. We’ve walked through that story: from the particle life cycle that defines its journey, to the fundamental motion equations that govern its path.

The most important decision you will make is choosing the right tracking method. Your choice between Steady and Unsteady tracking is a balance between the speed you need and the detail you require. If your goal is to find the final destination of particles in a stable flow, steady tracking is your best tool. If you need to see how a cloud of particles behaves over time or reacts to a changing flow, you must use an unsteady approach.

With these core concepts, you are now better equipped to set up accurate and efficient DPM simulations. This knowledge is the foundation for tackling more advanced topics, like two-way coupling and specific Fluent settings, which we will cover in future blogs.