How to simulate Clean Room with CFD
Simulating a clean room using Computational Fluid Dynamics (CFD) is a powerful way to evaluate airflow patterns, contamination control, and overall performance before physical implementation. Because clean rooms rely heavily on precise airflow and filtration, CFD allows engineers to visualize how air moves, how particles are transported, and whether the design meets required cleanliness standards such as ISO classifications.
The first step in clean room CFD simulation is geometry creation. This includes modeling the room layout, equipment, workstations, personnel, and especially the location of HEPA/ULPA filters, return grilles, and airlocks. Even small geometric details can significantly affect airflow behavior, so it is important to capture critical features that influence flow paths and contamination zones.
Next is mesh generation, where the computational domain is discretized into small cells. A high-quality mesh is essential, particularly near filters, walls, and critical working zones where gradients in velocity and particle concentration are high. In clean room simulations, finer meshes are often required in regions of laminar flow to accurately capture uniform velocity profiles.
Boundary conditions are then defined based on the HVAC design. Supply air from HEPA filters is typically modeled as uniform velocity or flow rate with low turbulence intensity to represent laminar flow. Return outlets are assigned pressure or flow boundary conditions. Walls are usually treated as no-slip surfaces, and heat sources such as equipment and occupants can be included to account for buoyancy effects.
One of the most important aspects of clean room CFD is turbulence modeling. Depending on the airflow regime, different models may be used. For laminar flow clean rooms (e.g., ISO Class 5), low-Reynolds-number or laminar models may be appropriate. For less stringent environments with mixed airflow, turbulence models such as k-ε or k-ω are commonly applied. Selecting the right model is critical to obtaining accurate results.
Particle tracking is a key feature in clean room simulations. Engineers use Lagrangian particle tracking or scalar transport models to simulate how contaminants move within the space. This helps identify areas where particles may accumulate or recirculate, which could compromise cleanliness levels. Particle size, density, and source locations (e.g., from personnel or processes) are important inputs in this step.
Another critical parameter is pressure distribution. Clean rooms often operate under positive pressure to prevent contamination from adjacent spaces. CFD can be used to verify pressure gradients across rooms, airlocks, and doors, ensuring that airflow direction is maintained as intended.
Post-processing of CFD results provides valuable insights. Engineers analyze velocity fields, streamlines, temperature distribution, and particle concentration maps to evaluate performance. Key outcomes include airflow uniformity, identification of dead zones, contamination risk areas, and verification of ISO cleanliness levels.
Finally, CFD results are used to optimize the design. Adjustments can be made to diffuser placement, airflow rates, filter coverage, and return locations to improve performance. This iterative process reduces the need for costly physical testing and helps ensure that the clean room meets both regulatory and operational requirements.
The Tensor team provides professional CFD simulation services to support engineering design and performance optimization across a wide range of applications, including HVAC systems, clean rooms, data centers, and urban environments. With extensive experience since 2013 and strong expertise in advanced simulation tools, the team delivers high-fidelity analysis of airflow, heat transfer, and particle behavior to help clients make informed design decisions.