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HVAC Analysis Of An Operating Room

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Currently, between 5 and 10 per cent of patients admitted to acute care hospitals acquire one or more infections, and the risks have been on an increasing trend during the past three decades. Controlling contamination in the hospital operating room environment is vital for the safety of patients and hospital personnel, and in this article we assess how computational fluid dynamics is helping to better analyze sensitive rooms and optimize HVAC systems. We also highlight a recent case study where EXN/Aero helped Faure QEI to do just that, through our popular Onboarding Program.

CFD HVAC Analysis in the Operating Room

The hospital operating room is a unique environment. Multiple sources of infection include:

  • The operating room staff from skin flakes or hair strands.
  • The patient, self infection into the body from patient's own skin flakes or particles on the body.
  • Clothing worn by operating room staff.
  • The room itself or equipment used within it.
  • Air supplied to the room.
  • Adjacent rooms.

For the reasons listed above, CFD can be used to analyze and optimize the operating room environment to ultimately control contamination and limit incidences of patient infection. Learn more about using CFD for HVAC challenges. We recently explored contamination control using CFD in an articleConsiderations when assessing HVAC requirements within the operating room include:

      • Air supplied to the room.
      • Patient comfort.
      • Comfort of surgeons and personnel.
      • Control of the concentration of chemical pollutants (anesthetic gases or volatile substances)
      • Control of the physical pollutants (aerosols or particulates)
      • Ability to quickly raise or lower the temperature without any large overshoot.
      • Air velocity at wound site should not exceed 0.2 (m/s) to prevent excess drying of the wound.
      • Age of air, particularly around the patient.
      • Thermostat controlling overall air temperature by measuring the air at a distant wall will not reflect the temperature around the surgical table. 

CFD can also be used to analyze and provide information on:

  • Airflow direction and mixing.
  • Contaminant or bacteria transport and propagation.
  • Room pressurization.
  • Temperature distribution including hot spots or cold zones.
  • Velocity profiles and stagnation areas.
  • Air changes over time.
  • Convective forces.
  • Heat losses through physical objects.
  • Heat sources such as lights, humans, or equipment and their impact on temperature distribution.
  • HVAC equipment effects on all the above.

Learn more about HVAC Simulations

Other Sensitive Areas

There are numerous other environments which CFD can be used to model or improve the performance of:

  • Cannabis production facilities: even temperature and airflow velocities have a big impact on cultivation and crop consistency. Reduction in HVAC footprint can lead to larger grow spaces and better energy efficiency.
  • Vertical farms and green-houses: similar scenario as cannabis along with thermal stratification and solar radiative heating through the canopy causing unwanted effects in the grow space.
  • Dairy farms and livestock farms: thermal comfort of animals plays a key role in production levels and consistency, especially in highly varying climates.
  • Biotech cleanrooms: contaminant infiltration can impact R&D or production processes.

Benefits of a CFD Approach

There are several advantages to using computational fluid dynamics (CFD) modeling rather than physical modeling in for hospital operating room HVAC systems, including:

  • Less expensive compared to physical modeling and requires a fraction of the time.
  • No scale-up problems.
  • Some phenomena can be nearly impossible to model and evaluate in physical environment.
  • Opportunity to predict potential design flaws that can be altered and remedied before the facility or system is constructed.
  • Provides detailed local information.
  • 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. EXN/Aero allows a user to run up to 7 simulations in parallel, should they wish to evaluate a complete parameter set.
  • Could ultimately improve patient and OR personnel safety, reducing contamination or complications.

Several factors have been responsible for previously 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. 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. 

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Case Study: Faure QEI


For over 25 years, Faure QEI has been designing, building and qualifying controlled atmosphere zones and other risk environments. From the most complex industrial processes to the most constrained environments that house them, the French company covers the various stages of an engineering project, notably consulting, design, implementation monitoring and commissioning of facilities. Their services take the form of project management assignments, and they are always seeking ways to exceed client expectations and deliver effective and efficient solutions. In line with this, they used the Envenio Onboarding Program to leverage on-demand CFD simulation software, EXN/Aero, alongside support and training from Envenio engineers.

The Project 

The project set out to assess the performance of a hospital operating room, with the aim of identifying and suggesting any overall improvements that could be made.

 Table 1 - Simulation Outcomes

Reporting Item


Mesh Topology 

Tetrahedral Mesh

Data Type


Total Control Volumes

16 Million

Steady State Simulation 

600 Steps

4.7 hours (16822 seconds)

28.04 sec/step

Steady State Simulation Cost


Transient Simulation 

721 Steps

10.1 hours (36331 seconds) - Transient simulated runtime of 6 minutes (0.5 seconds per time step)

50.39 sec/step

Transient Simulation Cost


Turbulence Model

Shear Stress Transport

Energy Equation On
Buoyancy On
Double Precision All
Total Compute Cost  14.8 hr * $13.5/hr = $199.80


Airflow patterns, thermal distribution, and spacial distribution of average age-of-air were modeled and predicted for the hospital operating room. The simulation was first run steady-state, and then run as a transient simulation which demonstrated the highly unsteady nature of the flow and gave a good indication of the range of variability likely to be observed in the immediate vicinity of the operating room table and patient.

Clean air enters through a configuration of inlets above the operating table creating an air-wash that flows downward about the operating table. Air exits the room through two square ducts along one of the walls. The simulated patient and operating room table were heat sources with a specified heat flux applied on these boundaries. Similarly, the operating room lamps above the patient were sources of heat, again using a prescribed heat flux. 

Buoyant forces were quite significant; heated air in the vicinity of the patient and lamps participated with a topological recirculation of flow created by the inlet configuration to create significant reversals in flow from the primary flow-direction created by the air-wash. This positioning of the lamps acted to divert the downward air-wash outward which further promoted upward flow in the region immediately above the operating table and patient.  The strong upward flow pattern, driven by flow topology and buoyancy, significantly disrupted the curtain effect of the air-wash and resulted in large scale unsteadiness in the region of the table and patient. This unsteadiness resulted in fluctuations in average-age-of-air in the vicinity of the patient which were the key results from this simulation.


Fig 1. Operating room simulation flooded by velocity magnitude. The flow is coming down from the ceiling and fanning out about the patient. The red area in the middle is relatively high velocity flow.


Fig 2.

Fig. 2 appears to be flooded by Velocity_Z or the vertical component of velocity.  It is used to illustrate the clearly "reversed flow" in the middle of the room above the patient.  This red upward flow is contrasted with the blue downward flow from the clean air entering through the inlets in the ceiling.  These are the primary features in terms of vertical flow.


Fig 3.


Fig 4.

The above images (Fig 3 & Fig 4) are flooded by average-age-of-air as determined by production of a constant scalar source at every location in the room. These slices are a snapshot from a particular instant in time towards the end of the transient simulation. At this instant, clean air enters the room with zero age as represented by the color blue. As the air ages, the color trends towards red. The air continually ages at all locations in the room and is able to mix, including regions of older air mixing with younger air. What is illustrated is the average age of air resulting from the turbulent flow and mixing. Eventually the average age of air integrated over the entire room reaches a quasi-steady state, where old air leaving through the exits is balanced with the new air entering such that the mean does not continue to evolve.


"The on-demand, pay-as-you-go nature of Envenio's EXN/Aero allows us to react quickly and effectively to the ever-changing needs of our clients. Being able to access such high-performance simulation tools on the fly, allows us to have full confidence in designs and exceed client objectives, in a cost-effective way that works for our business" 

Pierre Bombardier, Faure QEI


It is desirable to have only young air surrounding the patient to reduce risk of contamination and the continual flow of clean air into the room from above is designed to ensure the patient is continually washed in clean air. The simulation indicates the effectiveness of this air-wash, indicates the typical flow patterns and average-age-of-air at various locations in the room, and the transient simulation also indicates the variation seen in these patterns and distributions due to unsteadiness in the flow.

This study demonstrates the capability of determining the effectiveness of clean-air-wash systems and designs in preventing old and potentially contaminated air from encountering the patient. We were able to assess the performance of the current design and suggest several improvements based on the simulation as follows:

1) The air-wash inlet configuration consisted of inlets that surround a large central panel on the ceiling from with the lamps and armatures for the lamps are hung. This solid panel is undesirable because it sets up a recirculating flow topology with a node at the ceiling directly above the patient.   Hypothesized, is that it would be far better to have an air inlet directly above the patient blowing downward, though no alternative design was simulated or tested.

2) The shape, positioning, and heat produced by the lamps significantly impacts the overall flow and the efficacy of the air-wash system. The positioning may not be easily alternated given the need for specific light sources. However, there may be scope for improvement in the size, shape, and energy efficiency/consumption (as it impacts heat) of the lamps.


Hathway. A “CFD modeling of a hospital ward: assessing risk from bacteria produced from respiratory and activity sources”, The 11th International Conference on Indoor Air Quality and Climate. Indoor Air, paper ID: 45 (2008) 

2018-10-11 | Categories: CFD, simulations, HPC

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