Designing effective ventilation strategy for an Airborne infection isolation room (AIIR) using CFD
Tuesday, July 21, 2020
Designing effective ventilation strategy for an Airborne infection isolation room (AIIR) using CFD
Aaditya Ruiker
Blog Author - Aaditya Ruiker
Written by Aaditya Ruiker
10 Minutes Reading
10 Minutes Reading
This blog shows choosing an efficient ventilation strategy for an isolation room using CFD.
This year 2020 started with the unfortunate news of a new respiratory virus called SARS-COV2 and the disease termed as COVID -19 creating enormous pressure on healthcare infrastructure globally with each passing day.
It has been observed that COVID-19 spreads through direct person-to-person contact. Various researches are going on about virus transmission but to reduce transmission through air and to prepare for worst-case scenarios of community spread, there will be an immediate need for Airborne Infection Isolation Rooms (AIIR) in the areas of high population densities. This blog discusses "how CFD can be useful in designing an efficient ventilation strategy in keeping low contamination levels inside the isolation room". In designing an HVAC system for the isolation rooms, CFD can provide critical insight about airflow patterns and contaminant transport. This blog discusses the effect of the different configuration of supply and exhaust location inside the isolation room on transport and diffusion of contaminants.
General Strategies of Airborne Infection Isolation Rooms (AIIR)
Airborne Infection Isolation facilities aim to control airflow inside the room so that airborne contamination is reduced to a level that should not allow the cross-infection of other people in the healthcare facility. The strategy in designing ventilation for an isolation room suite should be to extract maximum contaminant as possible while providing thermal comfort for the patient.
Generally, the following strategies are used for the rooms:
Maintaining lower air pressures than adjacent spaces (Negative pressure) to prevent the escape of contaminated air from the room.
Designing airflow patterns for specific clinical procedures to reduce exposure of infectious particles for visiting healthcare staff inside the room.
Diluting infectious particles with large air volumes (ACH) around.
Air filtration to stop contamination with outside air– HEPA filters, etc.
Design Standards
As shown in Table 1, the negative pressure AIIR design varies from country to country. According to the US ASHRAE-170 standard, the pressure difference required for maintaining a negative pressure is a minimum of 2.5 Pa and air exchange rate 12 i.e., AIIRs need to achieve at least 12 air changes per hour to reduce the concentration of the contaminant.
Design Standards for AIIR in various Countries
Figure 1: Design standards for air-borne infection isolation room (AIIR) in various countries
(Source – ASHRAE Journal)
Typical construction of isolation room
The typical isolation rooms are provided with attached bathroom and ante-rooms between the patient room and corridor for additional pressure barriers for the air to reduce the sudden loss of pressure with respect to the outside air. The contamination within the isolation suite is maximized within the patient room, maintaining directional airflow from the anteroom to the patient room by a negative pressure gradient. The following diagram demonstrates pressure differentials achieved in adjacent areas is adjacent to isolation room:
Negative Pressure and Airflow in Isolation Room
Figure 2: Typical Negative Pressure Isolation Room, showing airflows and relative pressure gradients
(Source – ASHRAE Journal)
The actual negative pressure gradient will depend on several factors:
The difference in supply and exhaust air volume
Airflow path inside a room
The physical configuration of the ward.
To maintain a specified pressure level, exhaust air volume needs to be more than the supply volume and sufficient amount of air-tightness.
Air filtration unit
Most of the design standards recommend installing HEPA filters before throwing air using a fan unit outside of the room. Using HEPA 99.97% of particulates with a diameter greater than 0.3 microns can be filtered using the various principles of trapping the particulates like diffusion, interception, impaction with electrostatic attraction. So, the use of HEPA filters reduces the risk of viruses infecting outside individuals and helps in maintaining effective air changes per hour.
Also, it is recommended that the area in front of exhaust air should be kept clear avoiding clear obstructions such as carts and furniture.
HEPA Filter for Ventilation
Figure 3: Air filtration using HEPA filter
Even though the isolation room meets the above specifications, there is still uncertainty whether the room is refreshed uniformly or there are no stagnant, under-ventilated areas in the room where infectious contaminants might be concentrated. To analyze this, Computational Fluid Dynamics (CFD) can be useful in optimizing airflow patterns within the room and obtain a better understanding of contamination of this pathogen dispersion characteristics in designing the HVAC system for the room. It also provides insight into thermal comfort for the patient inside.
Airflow patterns governed by the locations, areas, configurations, and specified velocities/pressures of the air-supply and exhaust vents of the ventilation system. So, the present study analyses the air-flow path and the distribution of contaminants for different supply and exhaust configurations using CFD.
Problem Statement
A typical hospital room modeled for this study consists of a patient laid on a bed and room attached with a bathroom and ante-room. It is assumed that the room walls are adiabatic. To maintain the negative pressure inside the room, the exhaust flow rate is modeled 10% more than supply flow rate.
Isolation Room Goemetry
Figure 4: Isolation room geometry used for the simulations
Case#1: Ceiling supply air diffusers over the right side of the patient’s head and wall-mounted exhaust air grill on the left side. Placed the 0.2 m above the floor.
Isolation Room Exhaust Configuration Case 1
Figure 5(a): Case 1 - Isolation room geometry used for the simulation
Case#2: Relocated patient bed with wall-mounted exhaust air grill over the head of patient and supply diffuser locations kept same as in case#1.
Isolation Room Exhaust Configuration Case 2
Figure 5(b): Case 2 - Isolation room geometry used for the simulation
Case#3: Single exhaust was split into two exhaust located on either side of patient by retaining patient and supply diffuser locations same as in case#2.
Isolation Room Exhaust Configuration Case 3
Figure 5(c): Case 3 - Isolation room geometry used for the simulation
Numerical simulation procedure
The diffusion of contamination inside the room is analyzed according to the types, location of supply and exhaust after the single cough. To investigate, the dynamics of the ventilation flow and the airborne contamination in the conditions of coughing of a patient was performed on the three CFD models. The steady-state ventilation flows were computed till the convergence and then the time-accurate algorithm used to obtain the contamination travel and diffusion 5 sec after the occurrence of a cough. The numerical model of cough is discussed in the next paragraph.
For air supply, the inlet flow was specified to have a constant volumetric flow rate of 300m3/hr from each diffuser and was calculated using an air-changing rate of 12 ACH. The exhaust of the ventilation system was set at a constant outflow rate to maintain -7.5 Pa negative pressure within the isolation room.
Modelling of exhalation air while coughing
Experimentally Obtained Cough Characteristics
Figure 6 (a) & (b) : Experimentally obtained velocity characteristic of cough (Reference – liu et al.) and approximate coughing characteristic modeled for the simulation
Ideal coughing is a transient phenomenon with a flow rate similar to a skewed triangular pulse for the duration of coughing. Coughing characteristics are obtained from “Flow dynamics and characterization of a cough”. The source of pathogen contamination is given from the mouth of the patient with the mole fraction of 0.02 and modeled using the scalar transport equation. Any settling and deposition of contaminant particles on the surface is neglected. The characteristic of for the various researchers is shown (Figure 6a), but for the convenience, coughing was modeled with constant upward velocity of 8 m/s as shown in (Figure 5b). Then the diffusion of contamination is computed for 5 sec after the single cough.
Results and Discussion
The main objective of the CFD simulation is to obtain a diffusion pattern and concentration of the contaminant over the time period after the single coughing cycle. As air behaves as a carrying medium that carries the contaminant it is also important to analyze flow pattern induced inside the room.
In contour plots below shows the concentration of contaminant inside the room 5 sec after the occurrence of cough. The contour plots are obtained at the vertical plane passing through the mouth of the patient. From the results, it can be observed that the region of contaminant concentration of more than 0.5 ppm is greater in case# 1 and case# 3. Also, it is seen that the area of high contaminant concentration is closer to the exhaust in case# 2, From the vector plot and streamlines it is clear that the flow in the region is pushing contaminated air outside the room effectively.
For case# 1 and case# 3 the contaminant is seen to be concentrated at the stagnant area from where it can be circulated throughout the room and might be dangerous for the healthcare professional entering the room.
Case-1 Containment Contours and Velocity Vector
Figure 7 (a) : Distribution of contamination and velocity vectors at the vertical plane passing through patient mouth for case# 1
Case-2 Containment Contours and Velocity Vector
Figure 7 (b) : Distribution of contamination and velocity vectors at the vertical plane passing through patient mouth for case# 2
Case-3 Containment Contours and Velocity Vector
Figure 7 (c) : Distribution of contamination and velocity vectors at the vertical plane passing through patient mouth for case# 3
Diluting effect for the ventilation system
It would be valuable to get the time required to reduce the concentration of contaminants below the specified safety concentration. The following chart shows the diluting effect within the isolation room over 5 sec of time, the air change rate (ACH) for all three cases is the same but the concentration of contaminants varies with the airflow pattern and the ventilation strategies. From which we conclude that different supply and exhaust locations lead to different airflow patterns, and hence have different contaminant effects. Therefore, airflow behavior has a crucial and significant parameter when designing ventilation systems to enhance the efficiency of infection control within an isolation room.
Containment Contraction
Figure 8: Maximum concentration of contaminant w.r.t. time after the occurrence of cough
The properly designed isolation room ensures the safety of healthcare workers providing treatment to the patient. Physical space and the presence of furniture modify the airflow pattern. This means an individual CFD study of each specific case is necessary. As the increase in computational power, CFD is emerging as a powerful design tool for HVAC engineers. But HVAC engineers are facing difficulties in using CFD as a design tool due to lack of CFD expertise, the high licensing cost, and access to computational resources.
We at simulationHub are providing a CFD support to HVAC designers, engineers in designing ventilation systems to reduce the effect of COVID- 19 pandemic.
Blog Author - Aaditya Ruiker
Aaditya Ruiker
Aaditya Ruiker is a CFD support Engineer at Centre for Computational Technologies Private Limited (CCTech), Pune. He loves to work in fields physics and mathematics. Skilled in OpenFOAM, Fluent, C, MATLAB, CAD Modelling. He has completed his M.Tech in Thermal and Fluids Engineering from (Dr. BATU), Lonere, Raigad. His areas of interest are Heat Transfer, Fluid Mechanics, Computational Fluid Dynamics, Numerical Methods, Operation Research modeling. Driving and traveling, playing cricket and chess are his hobbies and he likes to explore historical places.
Blog Author - Aaditya Ruiker
Aaditya Ruiker
Aaditya Ruikar works As a Product Manager at CCTech, He is involved in developing and delivering high-fidelity technologies for various industries at affordable prices and plays role of domain expert. He also has a vision to make these technologies more accessible and user-friendly for better and efficient design outcomes.His interests lie in researching and simulating real world systems, particularly in the domains of engineering, physics and sustainable development. He likes to tackle challenges and work with others to find innovative solutions.