Dry Gas Filter CFD Simulation Tutorial| ANSYS Fluent with UDF and DPM

Dry Gas Filters are essential systems in many industrial processes, including power generation, chemical manufacturing, and air purification. Their main job is to remove solid particles, like dust, ash, or other contaminants, from a gas stream without using any liquids. This process is critical for protecting downstream equipment, meeting environmental regulations, and ensuring product purity. From an engineering perspective, the performance of a filter is defined by two key factors: its filtration efficiency (how well it catches particles) and the pressure drop it creates (how much it resists the gas flow). As a filter traps more particles, it becomes more clogged, which increases the pressure drop and requires more energy to operate.

Predicting this behavior is a major challenge. This is why a Dry Gas Filter CFD simulation is such a powerful tool. It allows engineers to see inside the filter, track particle movement, and predict how the filter’s performance will change over time. This project presents a detailed Filtration CFD analysis using ANSYS Fluent. We use the Discrete Phase Model (DPM) to track individual particles. More importantly, we use a custom User-Defined Function (UDF) to create a smart, dynamic model of the filtration process. This UDF DPM CFD approach allows us to simulate how particles get trapped and how that trapping affects the entire system, making it a highly realistic and valuable analysis. Our methodology is guided by the pore-scale study in the reference paper:

Reference [1]: Parvan, Amin, et al. “Insight into particle retention and clogging in porous media; a pore scale study using lattice Boltzmann method.” Advances in Water Resources 138 (2020): 103530.

Figure 1: schematic diagram of the dry gas filter system analyzed in this Filtration Fluent simulation.

Simulation Process | Filtration CFD Modeling with DPM and UDF

The simulation process began with the creation of the 3D geometry of the industrial filter system in ANSYS Design Modeler (Figure 2). A high-quality mesh was then generated to accurately capture the gas flow and particle trajectories. The core of this Dry Gas Filter Fluent simulation was built inside ANSYS Fluent by combining several advanced models. The DPM (Discrete Phase Model) was activated to perform particle tracking. We used a surface injection at the inlet to release sulfur-solid particles of varying sizes, representing typical industrial contaminants.

The most critical part of the setup was modeling the filter cartridge itself and the trapping mechanism. The filter was defined as a Porous Zone, with initial resistance values controlling how easily the gas could pass through. To make the simulation dynamic, a custom User-defined function (UDF) was written in C code. This UDF checks the diameter of each particle as it enters the porous zone. If the particle is large enough, the UDF “traps” it. The UDF then updates the properties of the porous media in real-time, increasing its resistance based on the number of particles it has trapped. This advanced UDF DPM CFD method creates a feedback loop where particle collection directly impacts the gas flow, mimicking real-world filter clogging.

Figure 2: The 3D CAD geometry of the dry gas filter designed for this CFD analysis.

Post-processing |CFD Analysis of Filtration Performance

The simulation provides powerful quantitative insights into the filter’s performance over time. The primary achievement of the simulation was its ability to trap DPM particles CFD accurately. The custom UDF successfully captured a total of 2465 sulfur-solid particles within the porous filter media. This particle count is not just a number; it is the driver of filter degradation. As these particles accumulated, the UDF dynamically recalculated the permeability of the filter media. The simulation showed that the permeability decreased dramatically from its initial clean value of k₀ = 1.00×10⁻⁶ m² down to a final clogged value of k = 3.90×10⁻⁸ m². This reduction directly led to an increase in the viscous resistance to 2.57×10⁷ 1/m², quantifying how the filter becomes harder for gas to flow through as it gets dirty.

k = k₀/(1 + 0.01 × 2465)

Figure 3: Particle trajectories inside dry gas filter

Pressure drop is arguably the most critical performance metric for any industrial filter. The pressure contour in Figure 3 provides a clear and quantitative analysis of this parameter. We can see high pressure at the inlet, which is necessary to drive the gas into the housing. As the gas is forced to pass through the resistive porous filter element, there is a sharp and significant drop in pressure. The clean gas on the inside of the filter is at a much lower pressure (green/blue). The simulation quantifies this perfectly: the pressure drops from a maximum of 55.9 Pa at the inlet to as low as -27.0 Pa at the outlet. This represents a total pressure drop of 82.9 Pa across the filter, a key performance indicator that directly relates to the operational energy cost. This Dry Gas Filter CFD analysis allows engineers to balance high filtration efficiency with an acceptable pressure drop, which is the fundamental challenge in filter design.

Figure 4: Pressure contour from the Dry Gas Filter Fluent analysis, clearly showing the significant pressure drop across the filter element.