The introduction of CFD in the design and re-design phases of hospital environments allows us to estimate air speed, temperature and track the spread of pollutants / pathogens. In particular, it is possible to use these tools to optimize the layout of hospital instrumentation for hybrid ventilated operating rooms according to the ISO 5 standard, in order to maximize their performance in terms of cleaning and sterilization.

The major obstacle to the correct use of ventilating systems with ISO 5 cleaning standards are the surgical lamps, which position themselves constituting an obstacle to the unidirectional incoming flow.
The thermo-fluid dynamics analysis allows to analyze the relationship between the HAVC system and the overall dimensions of the staff and instrumentation, together with the thermal contributions, estimating the comfort level of the medical staff.

Through these analyzes it is possible to verify the number of air changes per hour and evaluate the spread of airborne pathogens emitted by the staff present during the intervention.

The following article is an extract from a project carried out for the Numerical Fluid Dynamics course. Held by Verzicco R & Viola F. for Medical Engineering at the University of Rome Tor Vergata.

## Introduction

From 1960 onwards, CFD analysis has seen itself increasingly integrated with the aerospace industry, design, R&D and the manufacturing industry of aircraft and jet engines. It was subsequently applied to the analysis of combustion engines and turbines and then immediately found application in the design of vehicles by analyzing their aerodynamic performance.

The latest developments see CFD increasingly merging with engineering design becoming a complete and indispensable tool for CAE (computer-aided engineering). This analysis finds its strength in the same reason why it is incredibly complex, namely in the ability to describe the behavior of fluids and the individual fields that describe them. This complexity has been addressed and partly reduced thanks to the development of high performance codes.

These codes follow the exponential development of processors, which has allowed the development of user-friendly software for users who, although far from large computing centers, now have access to sufficiently powerful hardware. In fact, since the 90’s CFD has become widely used in the industrial community and then also found employment in the AEC world [1].

### CFD in surgery room

The ability to describe the temperature, air velocity, pressure fields and flow of pollution has made it possible to speed up and optimize the design and verification phases of buildings and rooms with a controlled environment. In particular, it is widely used in the design of clean rooms [2]. Among them we find hospital operating rooms where temperature, humidity, air velocity require careful control to ensure the best sterilization of the operating environment.

In particular, the introduction of the ISO 5 cleaning standard involves the use of unidirectional inlet flows such as to cover the entire sterile area and ensure continuous ‘washing’ of the surgical area by the incoming air flow. These systems allow to maintain extraordinarily low levels of colony forming units (CFUs) ensuring the best sterile environment, considered essential for more invasive interventions.

However, these ventilation systems require careful design that takes into account the overall dimensions and layout of the medical instrumentation in order to guarantee the best performance. In this regard, the use of CFD software allows you to better analyze and understand the effect of the reorganization of the layout of the operating room and surgical interventions.

## Background

Several CFD solvers are used in the industrial design and verification phases of HAVC systems and the analysis of indoor comfort for entire hospitals or for individual surgical rooms [3].

### Surgical Site Infection

HVAC systems are responsible for generating an internal microclimate and maintaining an appropriate air quality and aseptic conditions, which are fundamental in hospitals. The main purpose is to prevent the risk of patient infection during surgery and to ensure safety and thermohygrometric well-being.

A fundamental parameter is the hourly air change (ACH), which is the number of times the air volume in the room is completely renewed in an hour. This parameter, for current regulations, must be at least 15ACH / h. The air temperature in the range 20 ÷ 24 ° C with a humidity of 30 ÷ 60%. The operating room is maintained at a positive pressure in the range of 2.5 ÷ 5 Pa [4, 5, 6].

These parameters, under regulation, are considered the best standard of ventilation for surgical interventions. More than 300 million surgeries take place each year worldwide and it is estimated that up to 38% report surgical site infections (SSI) [7]. These infections occur after an operation in the part undergoing surgery, they can be limited to the skin only or affect subcutaneous tissues, up to organs and/or implanted material.

The main responsible is the transfer of contaminated particles which, following incorrect ventilation, are deposited on the surgical area. Recent studies [7] associate a great influence with three factors: inclination of the surgical lamps with respect to the incoming flow, heat emitted by the patient and the use of patient heating systems.

### ISO 5 ventilation

Operating room ventilation is currently divided into three main categories [4] such as turbulent mix ventilation, laminar flow ventilation and ventilation with temperature controlled systems.

The cleaning standard for operating rooms intended for high-risk interventions such as implantation of organs or prostheses, neurosurgery and in general interventions lasting more than 60 minutes is the ISO 5 cleaning class with unidirectional flow ventilation systems (LAF).

The inlet area, positioned above the critical area (operating table) must be such as to cover its surface 8 ÷ 10 m2. The input speed must be such as to maintain an average speed higher than 0.2 m/s in correspondence with the critical area, even in the presence of thermal sources [8].

Furthermore, great attention must be paid to the positioning of the operating light and to keeping the shooting areas located in the lower corners free.

The optimal solution for ventilation is therefore made up of ceiling lights with unidirectional air flow thanks to the use of HEPA filters. These systems ensure continuous and effective washing of the critical area and the absence of cold air currents that can cause discomfort to the surgical team, at least in the absence of physical barriers to the flow of air. This ensures that colony forming units (CFU) can be maintained at a very low concentration, ≤ 1 CFU / m3.

### Hybrid operating theatre

It is therefore considered one of the most modern operating room configurations, namely the C-arch hybrid room. The hybrid operating room is a room that meets the definition of an operating room but has equipment permanently installed to allow diagnostic imaging before , during and after surgical procedures.

These rooms meet the new needs in the surgical field such as the need to perform and support highly accurate interventions and support traditional interventions as well as favoring the choice of minimally invasive techniques that typically require more sophisticated instruments. High-end imaging equipment is therefore included, meaning high-resolution imaging such as C-arms, CT and MRI scanners.

The list of devices and instrumentation present within a hybrid operating room is very long, so only some of them are reported, of interest for this CFD analysis, or devices such as to constitute an interesting footprint for the flow or a considerable thermal load. The floor plan from the room is shown in fig. 1a is a three-dimensional view of the CAD model in fig. 1b. The team is made up of the surgeon and a further 3 people. The patient is present on the operating table, within the critical area. The presence of two operating lamps, a robotic C-arm, an anesthesia machine, a diaphanoscope, two 40 “medical monitors, a computer, a medical trolley and the instrumentation for medical gases are therefore considered, in addition to the presence of lateral dimensions consisting for example of cabinets and / or supports.

### CFD

A computational fluid dynamics (CFD) analysis analyzes, using a computer and numerical algorithms, systems that involve the flow of fluids and heat transfer, possibly also with associated phenomena such as chemical reactions [1].

This report describes the use of this analysis to study the flow inside a surgical room in such a way as to obtain an estimate of the temperature and air speed, once a particular configuration has been set. The analysis requires the choice of boundary conditions, discussed in the following section, and a preprocessing of the geometries in order to achieve an acceptable computational cost.

This preprocessing, which typically also concerns the production of an ad hoc screening for the considered problem, is greatly simplified using the software Vento CFD [11]. The software uses the immersed contours method and allows you to easily handle complex geometries, significantly reducing preprocessing times.

### Physical model

Once the domain, the mesh and the boundary conditions have been defined, the solver takes care of appropriately solving the governing equations of the considered physical phenomena. In particular, the continuity equation is involved:

$$\frac{\partial \rho}{\partial t}+\operatorname{div}(\rho \boldsymbol{u})=0$$

The balance of momentum on the three main directions:

\begin{aligned} &\frac{\partial(\rho u)}{\partial t}+\operatorname{div}(\rho u u)=-\frac{\partial p}{\partial x}+\operatorname{div}(\mu \operatorname{grad} u)+S_{M x} \\ &\frac{\partial(\rho v)}{\partial t}+\operatorname{div}(\rho v u)=-\frac{\partial p}{\partial y}+\operatorname{div}(\mu \operatorname{grad} v)+S_{M y} \\ &\frac{\partial(\rho w)}{\partial t}+\operatorname{div}(\rho w u)=-\frac{\partial p}{\partial z}+\operatorname{div}(\mu \operatorname{grad} w)+S_{M z} \end{aligned}

And the energy balance, derived from the first law of thermodynamics:

$$\frac{\partial(\rho)}{\partial t}+\operatorname{div}(\rho u)=-p \operatorname{div} u+\operatorname{div}(k \operatorname{grad} T)+\Phi+S_{i}$$

Taking into account the descriptive equation of state of the properties of the considered fluid.

The formulation implemented within Vento CFD is resolved iteratively and a good numerical analysis is also judged by the progressive reduction of the residue [1]. A more detailed discussion can be found in the following section.

## Methods and results

The CAD files of the different geometries are loaded into Vento CFD, making sure there are no holes in the closed surfaces and that they are recognized correctly, leaving the software itself with the burden of pre-processing unless some precautions have been simplified. previously using modeling software (e.g. elimination of unnecessary details).

Once the geometries and the fluid domain of interest have been defined, the boundary conditions for the different surfaces and various parameters necessary and / or useful for the simulation are defined. Different static analyzes are then conducted observing how some design parameters can influence the parameters of interest such as those typically considered in a performance test for operating rooms [8]. Subsequently, some non-stationary analyzes are carried out to observe the temporal trend of some interest flows.

### Boundary conditions

Among the most important parameters for a CFD analysis there are certainly the edge conditions that represent the behavior of the wall with respect to the fluid, and vice versa. All the geometries mentioned and shown in fig. 1b are represented, within the numerical formulation, as boundary conditions.

#### Ventilation

In the room there are the ventilation device and the return vents. The ventilation device consists of an area of 4 x 4.4 m with a flow velocity 0.25 m/s at a temperature of 21°C [12]. The return vents consist of 4 outlets placed at the corners with a pressure drop of 5 Pa compared to the ambient pressure.

#### Medical staff

To take into account the average temperature of the bodies of the medical staff and the patient, the heat exchange of the human body with the environment is considered [13]. The human body regulates the thermal equilibrium through its nerve centers of thermoregulation [14] to adapt the needs of the organism to the external thermohygrometric environment. The internal body temperature is estimated at 36.6°C but the external temperature of the different body areas can undergo variations and reach up to 25°C before the onset of pathological situations.

In general, the main factors that affect body temperature and the sensation of comfort are air speed, relative humidity, clothing and physical activity. In the appendix a mathematical model is proposed to estimate an equivalent temperature taking into account the thermal exchanges of the body in an environment with an average temperature. A condition is applied to the edge with a fixed temperature therefore equal to the equivalent temperature, the values of which are present in tab. 1.

However, it should be noted that this calculation brings with it uncertainty as different materials and clothing can lead to increases in body temperature up to 2 ° C compared to physiological conditions [15].

#### Devices

Being a hybrid operating room there is a lot of electromechanical instrumentation which provides a substantial thermal contribution. As described in the geometry pre-processing section, several simplifications have been made. From the thermal point of view, these apparatuses have thermal contributions known in literature [10, 16, 17], present in tab. 1.

### Mesh sensitivity

A first mesh sensitivity analysis is then conducted. By exploiting the potential of Vento CFD in considering complex geometries, an initial sensitivity analysis to the grid is carried out considering different surface refinements in the range 0.1 ÷ 0.01 m.

Starting from a larger refinement dimension, in addition to leading to poor precision from the point of view of the physical phenomenon, leads to a considerable increase in the computational cost due to the instability of the software that has to approximate very complex geometries with a mesh that is too loose to hold individual information.

This problem also persists by activating the Extra Robustness (ER) option within Vento CFD, that is a setting that facilitates the geometric approximation in the passage from the triangles of the STL file, descriptive of the different surfaces, to the descriptive mesh of the numerical domain for analysis. On the other hand, denser meshes would lead to unjustifiable computational costs.

The individual convergences are therefore analyzed, considering the residual energy, and further information on the thermal balance. These data are present in fig. 2 and further information can be found in the appendix.

#### Convergence in CFD analysis

The following results refer to the 2021.1 version of Vento CFD installed on Windows 11 with 32GB of RAM (2933 MHz) and Intel i7-10850H CPU at 2.7 GHz of which the maximum number of cores is dedicated to the single analysis. The first analyzes are carried out on 1200 iterations with a CFL of 350, which proved to be a good compromise between the speed of convergence and accuracy. The Spallart-Allmaras model is used for turbulence, more information on this choice can be found in the appendix.

In this CFD analysis the thermal flux is also considered, which covers its importance, also given the different boundary conditions set, so not only the convergence in the strict sense is analyzed but also an attempt is made to observe whether the thermal equilibrium has been reached. To do this, a deadlock is considered as regards the heat flows of three surfaces to which a heat exchange has been set without using a constant heat flow, i.e. patient, door and surgeon.

#### Average value

The minimum and average temperature remain constant and the divergence showing the maximum temperature value is neglected (it can be observed how this value is actually concentrated in a few cells and is attributed to a local divergence, strictly condensed around the geometry of the C arm).

The difference in these trends is therefore analyzed for the different meshes considered (table 2) and in the temperature and air velocity values in two points chosen as control points: near the patient’s chest and near the surgeon’s head, coordinates S1 = {0; 0.4; 1.2} and S2 = {0.6; 0.58; 1.7}. These points refer to areas typically used in the performance verification tests of ventilation systems [8].

#### Optimal mesh for CFD analysis

From the results it is possible to identify how a good compromise between the cost of the analysis and the accuracy is the mesh # 5 around the 600 iterations. This mesh allows to have an error of less than 1% on the fields where there is a considerable difference (maximum temperature and speed in the control points S1 and S2) compared to the denser mesh. Furthermore, the computational cost would be reduced by about 40%, with the same iterations. The choice to limit the iterations to 600 is dictated by the fact that the solution has a good degree of convergence. Although the residue is around E-3 it was also observed how the quantities remain stable and this error, with respect to iteration no. 1200, is still lower (about 50% less) than the error made by choosing a different turbulence model (see appendix).

For the variable time analyzes listed below, mesh # 4 is chosen, slightly reducing the accuracy to the advantage of an acceptable analysis time, considering the greater computational effort required. It should also be borne in mind that these differences are present at the first or second decimal place while the average quantities remain constant. For employee time analysis aimed at studying the air exchange or the movement of a particular mass of air, these errors were considered acceptable.

### First results

Once the spatial discretization level has been set, the results of air velocity (fig. 3), temperature range (fig. 4) and turbulent viscosity (fig. 5) are analyzed.

From the results it is evident that the unidirectional flow of the inlet is strongly deflected by the obstacles present. In particular, the surgical lamps divert the flow laterally, strongly reducing the ventilation in the sterile area. This reduces the local effectiveness of ISO 5 ventilation systems and can lead to a local increase in the concentration of CFUs.

#### Temperature

The temperature remains slightly higher near the major thermal sources, i.e. surgical instruments. The average room temperature remains at 21 ° C, clearly around the staff it remains slightly higher due to the heat exchange. Two sections are also shown, with a color map, enlarged on the staff in the appendix (fig. 20).

Furthermore, this effect of reducing the flow caused by the obstacles leads to a more turbulent flow in the area above the patient, as can be seen in fig. 5. These effects are undesirable from the point of view of the sterility of the surgical area and this obstacle of the lamps is further investigated in the following sections.

In addition, it is also possible to observe how on the areas near the walls the air velocity becomes very low and how the air exchange in these areas will take place more slowly (with the exception of the areas near the intake vents). The discussion on air exchange is expanded in the following section.

### Ventilation velocity

The air inlet speed is therefore varied through four different speed steps, referring to the ventilation systems typically used: v inlet = {0.2; 0.25; 0.35; 0.45} m / s.

Increasing the entry speed proportionally increases the speed of the air in the room, as can be seen from fig. 8. However, the increase in entry speed also leads to an increase in the difference between the speed in the area following the inlet and in the low speed areas (following obstacles). This greater difference, present both above the patient and in the upper corners of the room, results in an increase in turbulent viscosity. The increase reaches up to an order of magnitude at the corners and a doubling in the areas around the patient and at points S1 and S2.

#### Thermohygrometric comfort and CFD

The operating room is clearly a workplace for the entire surgical team and the thermohygrometric well-being has a great influence on the well-being and performance of the team itself, ie on the reduction of stress and intraoperative accidents [18].

The predicted mean vote (PMV) is therefore calculated, ie a comfort index based on the Fanger model [19]. This index is based on 7 values and is a function of the different terms of the energy balance, such as air temperature and speed, average radiant temperature, relative humidity, activity of the subject and thermal resistance of the clothing. More information on numerical computation can be found in the appendix.

As the speed of entry varies, and therefore the speed on the subjects (fig. 9c), the sensation of perceived well-being varies. Referring therefore to the PMV index, the values of which are present in fig. 9a, we see how the higher the entry speed, the more the sensation tends towards the perception of a colder environment. In addition, the team on average perceives greater discomfort and this is linked to the fact that the surgeon is hired with a greater production of metabolic heat as he is more active in carrying out activities.

The index for the patient is not indicative of a real perception as it is under anesthesia. It remains necessary to monitor the patient’s temperature to avoid hypothermic conditions, however there are no correlations in the literature between the PVW index and the risk of hypothermia for patients.

#### Air changes

Another very important parameter is the air exchange. Often this value is overestimated by design and the effects that may be produced by any obstructions to the ventilation system are not taken into account.

#### Surgical lamp

The object that most varies the flow near the patient are surgical lamps. These lamps should be positioned close to the operating table and close enough to best illuminate the operating site. At the same time, their position must be such as to compromise the ventilation flow as little as possible.

Different configurations and positions of these lamps are then analyzed. In particular, they are rotated on the y axis with respect to their center in the opposite direction with equal angles, indicated in tab. 3.

##### Rotation

The effect of the rotation of the lamps appears immediately so that the more the lamp tends to a vertical position, the more the high-speed flow on the patient increases. In particular, in fig. 10 it is possible to observe the speed field on the section corresponding to the lamp on the left. The more the lamps tend to be horizontal, thus constituting an obstacle to the unidirectional flow of entry, the more it tends to increase the effect of turbulence above the patient. Clearly this is not desirable in order to maintain a continuous and constant “washing” of the sterile area thanks to the unidirectional flow.

The left lamp has a more pronounced effect, however it should be observed that it is placed slightly further back so its effect has a minor impact on the area of the operation (near the surgeon). This effect is not associated with the distance in the longitudinal direction (along the patient’s major axis) but rather with the distance from the patient’s axis of symmetry, i.e. the ratio between the inlet area and the coverage given by the projection of the lamp on the plane. of the inlet.

#### Airflow

In the appendix there are also the velocity fields for the section passing through the center of the right lamp (fig. 24), the turbulence variable for the two sections described (fig. 22 and fig. 23) and the projection on the descriptive surfaces of the heat flow surgical team (Fig. 21).

The variation of the flow and therefore of the velocity field is not immediately perceptible from the false color maps projected on the different bodies (fig. 12d) however it is possible to extract the data along a plane of symmetry for the single body. In fig. 11 shows such data for the patient and the surgeon, read on the front face and projected along their major axis. In particular, for the patient it is clear that the L4 configuration leads to a considerable increase in speed in the abdomen area but to a reduction in flow in the area of intervention (near the surgeon) and on the head. On the contrary, the L1 configuration to which the maximum speed corresponds to the height of the patient’s head also corresponds to the lower speed in the abdomen area.

For the surgeon, the data are present up to the lower abdomen (going under the plane of symmetry, he does not find values in the space between the legs) and it is possible to observe how, in a similar way to the patient, the L4 configuration corresponds to a speed generally greater than the abdomen height. For the head there are no major differences as the lamp whose effect is greater (the one on the right in the following sections) is displaced and produces a lesser effect (see fig. 24).

##### Thermal flow

The effect on the heat flow is not perceptible from the false color maps but it is possible to extract the data, on the symmetry plane, for the patient, surgeon and the rest of the team. These data, normalized with respect to the value of the default configuration (L2) show how by varying the flow of air that hits the body, the heat exchange will also vary. The average of this heat flow is shown in fig. 12a and shows how the different configurations lead to different results depending on the position of the body with respect to the lamp.

As we tend to a more horizontal position, the heat flow increases for all the bodies. Instead, when tending to a more vertical position, the heat flow increases significantly for the surgeon and the assistant at her side, tends to remain constant for the patient and decrease for the other two assistants. The values have been normalized to eliminate the dependence of the heat flux on the respective temperature, which is different for the different bodies.

These values remain indicative and would require an average on all the cells intersecting the geometry analyzed, and not only on the symmetry plane, to obtain more accurate values.

#### ACH

Through Vento CFD it is also possible to quantitatively evaluate the emptying speed, as well as the number of hourly air changes (ACH).

In particular, a time-dependent simulation is implemented where at time t = 0 a concentration equal to 100% of contaminant is imposed on the inlet. This option does not represent a real contaminant but a way to signal to the software that the incoming air is different from the one already present (stale air). A frozen type contaminant is therefore used, i.e. one that has the same properties as the air already simulated but has a virtual flag recognizable by the software and by the user (for example by means of a concentration isovolume as in fig.13b and fig.13b) .

##### Non-stationary CFD analysis

The transient is then analyzed starting from the stationary conditions previously obtained over 200 seconds with steps of 0.5 seconds (400 simulation steps). This analysis, the results of which are reported in fig. 13a, allows you to measure the air exchange equal to 50% after 21 seconds and greater than 99% after 131 seconds. That is, the number of air changes per hour is less than 28 and is less than what the simple relationship between flow rate and room volume would suggest.

Furthermore, by analyzing step by step it is possible to observe how the first 50% of emptying is concentrated on the sterile area, in line with the incoming air, ensuring continuous washing of the surgical area. The stale air remains more in contact with the lower area of the patient and subsequently with the walls and corners of the room. The recess on the side wall shows a longer recirculation time than the other zones, second only to the upper edges.

### Contagion by air

Attention is subsequently placed on another major phenomenon present in surgical rooms where the presence of the surgical team as a thermal source, obstacle and source of flow perturbation (breathing) tends to reduce the effectiveness of the ventilation system in terms of room sterilization. In particular, the influence of the human body is such as to alter the thermal microenvironment, as noted above, and this is also reflected in the diffusion of particles carrying bacteria [20, 21].

Simulating the effect of breathing would require an oscillatory input such as to represent the surgeon’s breathing or the patient’s assisted ventilation, with a frequency f <1 Hz and a variable amplitude [22]. However, a cough is simulated whose effect is similar to an impulsive input with a speed of 4 m / s with a temperature equal to the internal body temperature [23].

A small flat ellipsoidal surface is therefore added to simulate the mouth and such conditions are imposed as an inlet. To observe its propagation, in accordance with the measures of Geoghegan et al. [23] and the implementation possibilities of Vento CFD, the cough is simulated as a step with a time interval of 4 seconds with steps of 0.02 seconds.

This analysis is carried out for both the patient and the surgeon.

#### Patient

The analysis is shown to be more qualitative than quantitative and although it is possible to measure the concentration and total introduction of contaminated air, it is more interesting to note the spatial distribution. In particular, it can be observed how the flow direction is deviated from the opposite velocity field present in the area above the patient. In fact, if after 2 seconds the contaminated air mass seems to move upwards (fig. 14b) after 4 seconds this air mass is pushed downwards (fig. 14c).

It appears evident how the emission of contaminant by the patient, thanks to the unidirectional inlet flow, is pushed down and then directed towards the suction vents where it can then be filtered.

#### Surgeon

The surgeon’s emission of contaminant has a more interesting effect. It is analyzed for the surgeon as the body is less influenced by the presence of the lamps, however it is easily re-proposed for the whole team with similar consequences.

The air emitted by the surgeon, despite with horizontal emission (normal to the surface representing the mouth) is immediately deflected downwards by the predominant speed field due to the inlet. This flow, directed towards the floor, collides with the patient and the operating table. This can increase the risk of SSI also in relation to the previous considerations on the effect induced by the presence of surgical lamps.

## Conclusions

This analysis provides several results. Being able to define the accuracy and validate these results is quite complex and would require at least accurate instrumental measurements. However, the use of CFD analysis allows you to estimate the effect of the repositioning of medical equipment.

It is confirmed that surgical lamps and staff can pose a problem for the correct ventilation of the surgical intervention area (area between patient and surgeon). In particular, the most influential element on ventilation are the surgical lamps that are positioned as an obstacle between the entrance of the unidirectional flow and the sterile area, preventing proper ventilation.

The thermal contribution of instruments and medical staff plays an important role in determining any comfort indicator. The estimate presented would require at least a punctual calculation and subsequently a body-by-body medical one, even better if carried out separately on different segments (eg head, arms, trunk, legs). It was not done due to software limitations.

#### Airborne pathogens

This CFD analysis allows you to have an idea also on the spread of pathogens. In particular, it results that the greatest effect is present due to the surgeon and the entire team. Clearly, this effect is reduced by considering the presence of masks. Furthermore, to analyze with greater accuracy the spread of pathogens and airborne contagion, the boundary conditions, for the contaminating entity, should be at least oscillatory, then analyzing the variation and prolonged spread over time and space.

It should also be noted that this analysis can be performed with a rather low computation cost and with a very low pre-processing burden. By exploiting the potential of Vento CFD, it is possible to integrate this tool not only in the design phase but also for the verification and optimization of the layout of the operating room. In particular, the development and lowering of costs of 3D scanning technologies [24] allows the entire room to be digitized in a few hours, resulting in digital objects that can be easily integrated in this analysis phase. These tools would significantly reduce the risks of SSI and the results could be added to control systems in hybrid operating rooms where all machinery is controlled and moved by a single control room.

## References

Refer to the downloadable report at the bottom of the page.

## Appendice

Refer to the downloadable report at the bottom of the page.