The quality of the air inside buildings is vital for our health and comfort. In office environments, dust and other small particles can easily enter through open windows, especially on windy days, and get spread around by the ventilation system. Understanding how these particles move is a major challenge for engineers designing HVAC systems. It is impossible to see these tiny particles with the naked eye, so we need a powerful tool to predict their behavior. A Particles Surface Injection Using DPM CFD simulation is the perfect method for this. Using the Discrete Phase Model (DPM) in ANSYS Fluent, we can track thousands of individual particles as they travel through the air, providing critical insights for creating healthier indoor spaces.

Simulation Process: Modeling the Surface Injection DPM CFD Simulation

To analyze the particle movement, a detailed 3D geometry of an office with two rooms was created. The simulation was performed using ANSYS Fluent, with the Discrete Phase Model (DPM) enabled. This is a powerful multiphase approach that treats the problem in two parts: a continuous phase (the air) and a discrete phase (the dust particles). The airflow itself was solved using a steady-state model to find the stable air currents in the room.

The most important part of this Surface Injection DPM CFD setup is the particle injection. The dust particles were released into the simulation from the surface of the window, perfectly mimicking how dust enters from the outside. The particles were not all the same size; instead, a Rosin-Rammler distribution was used to realistically model a range of diameters from 1e-6 to 1e-5 meters. The simulation then tracked these particles in an unsteady manner, allowing us to see their exact paths and how long they stayed in the office.

Figure 1: Particle residence time from the Particles Surface Injection Using DPM Fluent simulation, showing areas where particles linger for up to 55 seconds.

Post-processing: CFd Analysis of Airflow and Particle Dispersion

The dust particles enter the office through the window and create fascinating flow patterns throughout the space. Looking at the colored particle tracks, we can see how these tiny particles dance through the rooms, swirling and changing direction based on air currents. Our tracking revealed some particles stay trapped in the office for up to 55 seconds before finding an exit path. This long residence time matters because longer exposure to indoor dust directly impacts air quality for office occupants. The DPM simulation shows particles following distinct pathways – some take direct routes to exits while others get caught in recirculation zones, especially near corners and furniture. When particles hit walls or objects, they don’t just stop – they bounce off and find new paths, creating complex interaction patterns that simple airflow models miss.

Figure 2: Particle residence time after the surface injection inside the office

Figure 3: Particle pathways after a Surface Injection DPM CFD event, visualizing how airflow dictates particle travel and creates complex patterns.

The Surface Injection DPM Fluent model then shows the direct “effect” of this airflow on the dust particles. As shown by the particle track visualization, particles that get caught in the main high-velocity jet travel quickly through the office. However, many particles are pushed into the low-velocity recirculation zones. Once inside these zones, they become trapped, swirling around for a long time before they can escape. The residence time data provides the direct proof of this trapping effect, showing that some particles remain suspended in the office air for up to 55 seconds. This long residence time is the direct consequence of the room’s airflow patterns and is the main factor contributing to poor indoor air quality. The most significant achievement of this DPM simulation is the clear, visual link it establishes between the airflow patterns created by the room’s geometry and the resulting long particle residence times in specific stagnation zones, providing actionable data for optimizing HVAC vent placement.