Using CFD to Reveal How Equipment Configuration Can Affect Air Cleanliness
Operating rooms (ORs) come in many varieties depending on the type of surgeries performed in them. A hybrid OR, for example, is home to large imaging equipment, whether that’s a CT scanner or an MRI machine.
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| A hybrid OR at Temple Women and Families Hospital in Philadelphia, PA, complete with a CT scanner C-arm and a laminar diffuser array installed in the ceiling (photo by Kendon Photography) |
All ORs are conditioned with a laminar diffuser system over the surgical table and low wall returns – and this system needs to maintain a sterile environment regardless of the equipment mounted in the space. From an air distribution standpoint, hybrid ORs are more challenging to design because the imaging equipment located over the surgical table can disrupt the laminar airflow field, which can affect the cleanliness of the air in the sterile zone.
The design of OR HVAC systems follows ASHRAE Standard 170, Ventilation of Health Care Facilities, which stipulates how the air distribution system should be designed in terms of supply/return location, supply airflow rate, diffuser sizing and room pressurization, among other requirements. Although rigorous, these requirements alone do not guarantee optimal air cleanliness in the critical sterile zone where the surgical procedures take place.
This is where a design verification tool, like computational fluid dynamics (CFD), can be deployed to assess the airflow patterns and air cleanliness (via particle count) for a specific design.
A CFD model for a proposed hybrid OR for an Australian hospital is shown in figure 1. The critical equipment consists of the surgical table, surgical lamps and a pendant, various displays, and the CT scanner C-arm and carriage. In this case, the surgical lamps and pendant are located directly over the surgical table, while the C-arm for the CT scanner is mounted on structural framing directly below the laminar diffuser array and can slide in and out of the sterile zone as required. The CT scanner itself sits on the floor on the left-hand side of the room in figure 1.
The goal of the CFD model was to evaluate the air cleanliness under different equipment configurations, which are summarized in table 1. The position of the various pieces of equipment changes for each simulation, with some of them protruding directly into the supply airstream from the laminar diffusers.
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| Figure 1: The CFD model geometry for a hybrid OR, with the room layouts for cases 1 and 2 (left) and case 3 (right); note that case 1 has no occupants and serves as a baseline |
| Table 1: Studied Equipment Configurations |
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The results for the temperature and velocity measured at a vertical plane in the OR are shown in figures 2 and 3, with the accompanying average variable values summarized in tables 2 and 3 for each case. Note that for our purposes, the reference plane is a horizontal plane located 1cm above the patient and is the same size as the surgical table. The “ideal” scenario is in case 1, which has no occupants, a low heat gain (due to inactive equipment) and where most of the equipment is placed out of the path of the supply air. This case serves as a useful baseline.
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| Figure 2: Temperature contour plots at a vertical plane for case 1 (top), case 2 (middle) and case 3 (bottom) |
| Table 2: Average Air Temperatures |
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Disruption in the airflow patterns is evident in cases 2 and 3, as more equipment is brought online (generating heat) and is in the path of the supply air. Case 3 shows the most disruption to the unidirectional airflow pattern due to the CT scanner C-arm being directly over the surgical table and to the heat generated from the scanner carriage (shown as a thermal plume on the right-hand side of case 3 in figure 2). It should be noted that case 3 has the largest temperature differential, as seen in figure 3, which further contributes to the higher air velocities observed in table 3.
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| Figure 3: Velocity contour plot at a vertical plane for case 1 (top), case 2 (middle) and case 3 (bottom) |
| Table 3: Average Air Speeds |
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Figure 4 shows the particle concentration plots for cases 2 and 3 (remember, case 1 has no occupants and therefore no particles), while the average values are shown in table 4. In both cases, most of the particles are outside of the sterile zone, with case 3 showing higher particle counts within the sterile zone as compared to case 2. This is a direct effect of the airflow patterns that are shown in figure 3, which disrupt the laminar airflow pattern and thereby move more particles into the sterile zone. The average values in table 4 tell a similar story, where the particle counts at the various returns decrease in case 3, implying less particle removal and more airborne particles in general.
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| Figure 4: Particle concentration contour plot at a vertical plane for case 2 (top) and case 3 (bottom) |
| Table 4: Average Particle Counts |
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One interesting aspect is the decrease in particle counts at the reference plane, which, at first glance, is counterintuitive. The reason for this has to do with the specific layout of the equipment and the occupants in the model ( pictured in figure 5). Essentially, a jet is created by the CT scanner C-arm, which pushes the particles generated by the surgeon at the head of the table toward the occupant. This causes a higher particle count above the occupant in case 2 and is not an effect that is expected to happen in general.
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| Figure 5: Local particle movement caused by the air jet from the C-arm in case 2 |
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Overall, the results show that large equipment, especially if it’s located below the laminar diffuser array, can impact the cleanliness of the sterile zone by generating unfavorable airflow patterns. The magnitude of the impact depends strongly on the specific design of the OR and the type of equipment in question. It is therefore very useful to investigate these effects with a CFD model.
To learn more about Predict, or if you come across a project you believe is well suited for a CFD analysis, reach out to our team at info@PredictCFD.com.
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Mike Koupriyanov is Manager of the Price Predict team. He is based out of Price’s headquarters in Winnipeg, MB. Click here to connect with him on LinkedIn. |
