Cleanrooms represent a specialist area of HVAC design where the goal is often to quickly remove as much contamination as possible. Using CFD, engineers can quickly achieve this along with a number of other design objectives thanks to the ability to visualize airflow patterns and particle migration paths for a proposed or existing design. In this article, we explore how CFD can work alongside the most common contaminant characterization parameters and provide a number of other benefits in the design and optimization of a cleanroom HVAC system.
Cleanrooms are essential to minimize the contamination of products made in many manufacturing industries, as well as preventing microbial infection in hospitals and medical facilities. The cleanliness in a cleanroom is classified by use of ISO 14644-1 (2001), and carried out by ensuring that the concentration of airborne particles does not exceed the limits laid down in this standard.
The Primary Functions of Cleanroom HVAC Systems
There are a number of basic functions required of cleanroom HVAC systems. Essentially, they must provide filtered supply air at a sufficient flow rate and with effective flow patterns to reach a specified class of cleanliness. In addition, they must provide filtered outdoor air for occupants and equipment, and exhaust effectively any unwanted chemicals. Maintaining a specified cleanroom pressure is essential, and the ability to add or remove moisture to regulate cleanroom humidity is also important. Finally, the system should be able to efficiently and effectively add or remove thermal energy to regulate room temperature. Each and every cleanroom is unique, and the cleanroom requirements vary between industry sectors. Here, we focus more on the medical sector, but a recent article explores the requirements of those in the medicinal cannabis world.
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Cleanrooms have evolved into two major types which are also known as non-unidirectional (turbulent) and unidirectional (laminar) clean rooms.
Fig 1. Two major types of cleanrooms, a) non-unidirectional, and b) unidirectional
CFD simulation is widely used in the HVAC systems design process to combat many common problems in cleanroom design, including:
- Insufficient air flow
- Inadequate laminarity
- Fail to pressurize to specified pressure level
- Local stagnation near point of service
- Large stagnation zones
- Ineffective chemical vapor exhaust
- Temperature variation above specifications
- Humidity variation above specifications
Contaminant Sources & Dispersion
The most common cleanroom contaminant sources include (a) carbon dioxide, (b) carbon monoxide, and (c) volatile organic compounds or VOC's. Many sources have an impact on the dispersion of these contaminant sources and should all be factored into a cleanroom analysis.
One of the first sources to consider are the thermal effects within a cleanroom. These include transmission of heat by conduction through walls or floors, radiation between solid surfaces within the cleanroom, solar radiation through glazing and heat gains from equipment such as lights. In addition, infiltration or air leakage and sensible and latent heat gains from occupants must be assessed.
Any cracks around windows or doors can have a major effect on infiltration and heat loss. These air leaks also impact the flow pattern in the cleanroom which in turn affects the contamination dispersion and overall indoor air quality. Movement, whether from people or an object such as a door, can also affect dispersion.
Turbulence and humidity play a key role in contaminant dispersion. As the Reynolds number of the flow increases, the flow does not remain streamline and small fluctuations become amplified. Unstable flow is created as a result, finally becoming turbulent and resulting in an increased dispersion. Any change in humidity can have an impact on air density, resulting in the creation of a buoyancy effect. As a result of this, air circulation becomes more powerful within the cleanroom and contaminant dispersion is increased.
Contaminant Distribution Characterization
Here, we take a look at three commonly used parameters to characterize and analyze contaminant distribution.
Local Mean Age of the Air (LMA)
Used to evaluate the efficiency of a cleanroom ventilation system in relation to changing old air to new, the Local Mean Age (LMA) of the Air is a common technique to characterize contaminant dispersion. In brief, the lesser the age of air, the better the cleanroom air quality. Tommaso (1999) defined the local mean age of the air as “the average time it takes for air to travel from the inlet to any point P in the room.”
CFD allows the LMA of the air in a cleanroom to be analyzed within a virtual model before any expensive ventilation system changes take place.
In this example, Adamu et al. (2011) carried out CFD simulations on the Natural Personalized Ventilation (NPV) in three different hospital cleanroom designs to identify the optimal solution.
The study found that the age of the air in design a is confined to a small packet area and above the large duct, whereas for design b, it is mostly near the ceiling over a larger area. The most noticeable difference is the age of the air at bed level, which is nearly 305 s in the case of design a compared to design b, which has a value of 353 s. In this instance, designs a and b are identified as good choices by the author.
Fig. 3 - Simulation Results of Conceptual Designs
Contaminant Removal Effectiveness (CRE)
Contaminant Removal Effectiveness or CRE is commonly used to assess how effective a ventilation system is within an occupied zone. Essentially, the parameter measures how effective an installed system in a particular zone or location is at removing existing contaminants. Ho et al. (2008) states that the formulation of CRE arises from the mean contaminant concentration at the supply and exhaust openings and the breathing zone.
Fig. 4 - Formulation of CRE
Ho et al. (2008) demonstrates how CFD can help to maximize CRE within a hospital operating room where there are three surgeons and a patient. The study considered a light over the patient, and the supply and exhaust were kept at a similar distance from the roof and floor respectively. The main contaminant generator in this case is the patient, and the study was performed to to minimize the contaminant concentration in the operating room.
The results below show a) contaminant concentration, b) temperature distribution, and c) relative humidity in the room.
Fig. 6 - Simulation Results
Within the areas of high and low temperature and humidity respectively, the value of contaminant concentration is high, for example, between the patient and the light and above the light. This problem can therefore be resolved by installing an additional exhaust just above the light to efficiently and effectively remove the contaminants.
Air Change Efficiency (ACE)
Air Change Efficiency or ACE is another important parameter available to engineers wishing to monitor the air change efficiency of a cleanroom. It is often used alongside simulation to measure the effectiveness of a ventilation system in replacing the fresh air with old air.
Cleanroom CFD for Energy Efficiency
Since HVAC systems of bio-cleanrooms operate continuously, it is vital and significant to consider energy-efficient strategies as well as to achieve an acceptable performance for environmental parameter contamination control. CFD simulation provides a unique opportunity to extensively investigate the temperature and airflow distribution as well as to identify any potential energy savings.
Wang et al. (2009) recognized the limited quantitative information available around the compromise of contamination control and energy saving potential, and carried out a study to highlight the strategic approach on performance improvement of a HVAC system in a bio-cleanroom for vaccine production. Both numerical simulation and field measurement of a full-scale bio-cleanroom was carried out in a cold room at the vaccine production plant in Taiwan.
The research showed how the different velocity and temperature of supply air could be assessed extensively not only by airflow and temperature distribution, but also by transient simulation to achieve the design specification of 4°C in a vaccine storage facility. Results in this study also provide valuable information to the facility engineer facing the compromise between energy saving strategy and environmental control consideration in the bio-cleanroom. This is just one real-world example of how CFD simulation is helping to identify strategies for best practice at design stage, as well as reducing running costs at full operation.
Conclusion: Advantages, Usability & Accessibility
There are a number of advantages to using computational fluid dynamics (CFD) modeling rather than physical modeling, including:
- Less expensive compared to physical modeling.
- May predict potential design flaws that can be altered and remedied before the facility or system is constructed.
- May explore possible opportunities for improved performance.
- Can model a variety of options for planned and operating designs so that the most effective and economical solutions can be pursued with a high degree of confidence in their validity.
It should be noted that in some applications, physical modeling may still be required in some capacity, albeit in often reduced numbers thanks to simulation.
A number of factors have been responsible for limiting the use of CFD in the HVAC sector including lengthy setup times, expensive software licenses, and resource or training requirements. Software developers such as Envenio are helping to overcome such challenges with a new generation of cloud-based software - such as EXN/Aero - enabling CFD cloud computing to become a reality. This software enables engineers to easily access and expand their CFD capabilities as and when needed, and for a fraction of the traditional time and cost associated with conventional CFD tools. Envenio introduced a discover
CFD has revolutionized this area of engineering with users able to optimize HVAC designs in less time and at lower costs than previously possible. For this reason, it is hardly surprising that an Aberdeen Group research study found design engineers working for best-in-class companies are 15 per cent more likely than industry average to use CFD design and engineering to make design trade-off decisions.
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We will provide you with all of the visual data (2D, 3D images & animations) but also the engineering data that accompanies the results. We will also provide recommendations based on what we see in the results. We make the project as tangible as possible so that it is meaningful and adding value to your business. What's more, most discovery projects take just 2-3 weeks and cost on average, between $4,000 - $8,000 - considerably cheaper than investing in conventional resources yourself.About EXN/Aero:
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Adamu, Zulfikar A., Malcolm J. Cook, and Andrew DF Price. “Natural Personalised Ventilation-A Novel Approach.” International Journal of Ventilation 10.3 (2011): 263-275.
Boucher, M. (2011). Optimizing product development time by using cfd as a design tool. Available at http://www.solidworks.com/sw/docs/0308-7117-RB-CFD-012-simulation.pdf
Fu-Jen Wang, Yat-Huang Yau, Zhuan-Ru Liu and Yin-Rui Zheng, 2009.Performance Evaluation through CFD Simulation and Field Measurement on a Bio-Cleanroom for Vaccine Storage. Journal of Applied Sciences, 9: 4232-4239.
Ho, Son H., Luis Rosario, and Muhammad M. Rahman. “Three-dimensional analysis for hospital operating room thermal comfort and contaminant removal.” Applied Thermal Engineering 29.10 (2009): 2080-2092.
Tommaso, R. M., E. Nino, and Giovanni Vincenzo Fracastoro. “Influence of the boundary thermal conditions on the air change efficiency indexes.” Indoor air 9.1 (1999): 63-69.