Advanced Airflow Modelling: Applying CFD in Cleanroom Design

Advanced Airflow Modelling: Applying CFD in Cleanroom Design
1. Introduction
Computational Fluid Dynamics (CFD) has become an essential tool for engineering cleanrooms that meet stringent performance, contamination-control, and regulatory requirements. While ISO 14644 and GMP Annex 1 provide the performance criteria, CFD enables engineers to predict airflow behavior—velocity fields, turbulence, particle transport, and temperature distribution—before construction or modification of a cleanroom. When properly validated, CFD strengthens design decisions, reduces lifecycle risk, and improves operational reliability.
This article provides a technically grounded, engineer-focused guide to using CFD in modern cleanroom design, from modelling strategy to validation and integration with qualification activities.
2. The Role of CFD in Cleanroom Engineering
CFD supplements traditional engineering calculations by offering a detailed, three-dimensional understanding of airflow patterns. In cleanrooms where unidirectional flow, pressure cascades, and contamination pathways are critical, CFD offers insights that are not achievable through rule-of-thumb design alone.
Primary uses of CFD in cleanroom design include:
- Predicting airflow velocity profiles and identifying turbulence zones.
- Visualizing unidirectional flow uniformity over process-critical areas.
- Simulating particle generation, transport, and deposition.
- Optimizing placement of HEPA filters, returns, and make-up air inlets.
- Assessing temperature, humidity, and buoyancy-driven effects in high-load areas.
- Supporting contamination-control risk assessments and the facility’s Contamination Control Strategy (CCS).
CFD is not a substitute for compliance testing; rather, it improves the likelihood that the constructed facility will meet ISO 14644 performance criteria during OQ/PQ.
3. Modelling Objectives and Boundary Conditions
Accurate CFD results depend on well-defined modelling goals and boundary conditions that reflect real operational expectations.
Typical modelling objectives include:
- Achieving consistent unidirectional airflow ≥0.36–0.54 m/s over ISO 5 zones.
- Maintaining required pressure differentials (generally 10–15 Pa between grades).
- Minimizing recirculation zones above critical process locations.
- Verifying recovery time following simulated particle disturbances.
- Predicting environmental stability near heat-emitting equipment.
Essential boundary conditions:
- Supply airflow: HEPA/ULPA face velocities, FFU performance curves, and uniformity assumptions.
- Exhaust/return flow: Locations, flow rates, and balance settings.
- Thermal loads: People, equipment, lighting, and process heat sources.
- Process barriers: Isolators, RABS, curtains, and equipment footprints.
- Contaminant sources: Personnel particle emission rates and process-specific generation assumptions.
Boundary conditions must be based on engineering calculations, manufacturer data, and documented URS/Basis of Design (BOD) criteria.
4. Turbulence Models and Solver Selection
Selecting an appropriate turbulence model is one of the most critical decisions in cleanroom CFD because the accuracy of particle transport and velocity uniformity predictions depends heavily on it.
Commonly applied models:
- k–ε (standard or realizable): Robust for general room-scale modelling; good balance between accuracy and computation time.
- k–ω SST: Better near-wall resolution; useful for unidirectional flow uniformity and identifying micro-recirculation zones.
- RNG k–ε: Helpful where buoyancy and swirl effects are present.
- LES (Large Eddy Simulation): High accuracy but computationally intensive; typically reserved for research-level or high-risk applications.
For most cleanroom design projects, a realizable k–ε or k–ω SST model achieves the necessary practical accuracy while maintaining reasonable simulation times.
5. Particle Transport and Contamination Modelling
Simulating particle movement allows engineers to assess contamination risks early in design.
Two principal approaches exist:
- Lagrangian (discrete particle) modelling: Tracks individual particles; useful for simulating personnel-generated contamination and verifying whether particles escape critical zones.
- Eulerian (scalar concentration) modelling: Treats particle concentration as a continuum; suitable for evaluating uniformity or dilution in larger volumes.
Key considerations:
- Use iso-kinetic boundary conditions near HEPA inlets to avoid artificial deposition.
- Apply realistic particle size distributions (commonly 0.5–5 µm for viable and 0.3–5 µm for non-viable particles).
- Incorporate gravitational settling and turbulent dispersion when modelling deposition risk.
Particle simulation results should be cross-checked with anticipated ISO 14644-1 class limits and expected PQ operational performance.
6. Modelling Common Cleanroom Configurations
Different room layouts and process arrangements require tailored CFD approaches.
Unidirectional (laminar) airflow zones:
- Evaluate face velocity uniformity and identify edge effects near walls and equipment.
- Examine the influence of obstructions such as robots, filling lines, or microscopes.
- Confirm downward flow continuity to low-wall returns.
Turbulent-mixed airflow rooms:
- Model dilution effectiveness, especially in ISO 7–8 rooms with high heat loads.
- Verify that return locations do not create stagnant corners.
Airlocks and transfer rooms:
- Simulate opening/closing cycles using transient models to predict pressure cascade stability.
- Assess air velocity through door gaps for contamination containment.
RABS and isolator environments:
- Model internal recirculation patterns and assess glove port disturbances.
- Evaluate leakage paths between zones and HEPA supply interactions.
7. CFD Integration in the Cleanroom Design Workflow
CFD should not be an isolated task; it must integrate with the broader engineering design and qualification lifecycle.
Typical workflow alignment:
- URS & DQ: CFD supports design decisions for HEPA placement, supply air volume, and equipment layout.
- IQ: Ensures installation matches the design assumptions used in the model.
- OQ: CFD predictions are verified using airflow visualization, smoke studies, HEPA integrity tests, and velocity measurements.
- PQ: CFD results help interpret operational classification testing and particle behaviour under dynamic conditions.
CFD findings should feed into the facility’s CCS, particularly around critical interventions, airflow protection strategies, and environmental monitoring locations.
8. Validation and Verification of CFD Models
Regulatory expectations require that CFD models used for design or risk assessment be validated against real data.
Core verification steps:
- Compare predicted velocities with measured values during OQ.
- Validate pressure gradients using HVAC commissioning data.
- Confirm predicted flow patterns with smoke visualization.
- Cross-check predicted contamination trends with PQ results.
Documentation should include model setup, assumptions, solver settings, mesh strategy, convergence criteria, and deviations from standard practice.
9. Limitations and Engineering Considerations
Although powerful, CFD is not infallible and must be applied with engineering judgement.
Known limitations:
- Over-simplified boundary conditions can lead to false uniformity.
- Turbulence models vary in accuracy for low-velocity, cleanroom-specific flows.
- Mesh resolution significantly affects results; inadequate meshing may hide recirculation.
- CFD cannot replace ISO 14644 testing, HEPA integrity testing, or real PQ performance data.
Well-designed CFD complements, but never substitutes, field testing.
10. Conclusion
CFD has become a cornerstone of advanced cleanroom design, enabling engineers to visualize airflow behaviour, predict contamination risks, and optimize HVAC performance before construction. When grounded in accurate boundary conditions, suitable turbulence models, and validated assumptions, CFD provides actionable insights that significantly improve the reliability and regulatory robustness of cleanroom design.
By integrating CFD throughout the DQ–IQ–OQ–PQ lifecycle, cleanroom designers and operators can achieve systems that meet ISO 14644 and GMP Annex 1 requirements with greater confidence, efficiency, and long-term performance stability.
Read more here: About Cleanrooms: The ultimate Guide



