Numerical analysis of an isolation room to prevent the spread of COVID-19
Journal of Thermal Engineering 2022, Vol. 8, Issue 5, pp. 632-641; doi.org/10.18186/thermal.1190529
Abstract
Keywords: CFD; Isolation Room; Numerical Analysis; COVID-19
Introduction
The coronavirus (COVID 19) disease, which is thought to have emerged in Wuhan, China at the end of 2019 and spread all over the world in a short time, has become an important epidemic that continues to threaten the whole world. Which affected nearly 6 million people and almost 300,000 deaths, Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) or only Covidien-19 (COVID-19) with a terrible time, it has emerged as a global pandemic and rising quickly [1].
This disease is transmitted by sneezing as well as by coughing or even inhalation of droplet nuclei (Φ <5μm) produced during speech or by contact. Exhaled droplets from the COVID-19 patient or the active carrier of the virus may accumulate in the mucous membrane of the conjunctiva of the mouth, nose or eyes of other people in close contact. The virus is transmitted through personal contact with a COVID-19 patient or from utensils, furniture, surfaces, etc. It can be transmitted through indirect contact
*Corresponding author. *E-mail address: erhana1985@gmail.com, erhan.arslan@tubitak.gov.tr This paper was recommended for publication in revised form by Regional Editor Erman ASLAN Published by Yıldız Technical University Press, İstanbul, Turkey Copyright 2021, Yıldız Technical University. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
with fomites. Usually, symptoms such as fever, shortness of breath, sore throat, and cough appear in infected people [2]. The disease causes respiratory diseases such as pneumonia and acute respiratory distress syndrome, resulting in rapid death depending on the age, lung condition, immunity and sociodemographic profile of those affected. Many studies [3–5] have shown that this virus is highly harmful. It has become a pandemic reported by the World Health Organization (WHO) (characterizes COVID). Droplets generate aerosol by coughing, sneezing or speaking. Viruscontaining droplets form the substrate for viruses in the isolated area for airborne transmission of this disease. In addition, environmental conditions (relative humidity, temperature, precipitation, etc.) are specified as effective parameters for the virus [6–8]. Removal of airborne particles in airborne infection isolation rooms is important for the infection control of airborne diseases. Keeping COVID 19 patients and those with symptoms in isolated rooms is essential to prevent the disease. These isolated rooms are called Airborne Infection Isolation Rooms (AII). The virus can be carried in the air released from these rooms. An effective strategy must be implemented to prevent this [9,10]. In a study in which air and surface samples were tested to determine the distribution of severe acute respiratory syndrome coronavirus in hospital wards in Wuhan, China, it was stated that contamination in intensive care units was higher than in general services. A study was conducted at a shopping mall in Wenzhou, China, to determine possible modes of virus transmission. The study, which investigated a number of several COVID-19 cases, showed that the indirect transmission of the virus was likely caused by the virus transmission of common objects, aerosolization of the virus in a confined space, or the spread from asymptomatically infected people [11]. Various methods have been used in the disinfection of the exhaust gas. HEPA filtration, sanitization, heating UV are among the common examples [12–16]. Since the heating, ventilation and air conditioning (HVAC) system plays a very important role in the transmission of SARS-CoV-2, it has been a problem for this industry that the HVAC systems will be operated at maximum efficiency. The task of HVAC systems is to provide a healthy and comfortable environment. However, it has been found that insufficient ventilation in confined spaces is associated with an increase in respiratory tract infections [9,17,18] Few studies have been conducted to examine the effects on the design and of isolation rooms [18,19]. Thermal cabinet design using different approaches has been studied by many researchers [20–23]. Computational fluid dynamics (CFD) is of great importance as it provides information in the thermal and airflow fields. Many studies [23–26] have been done to track the motion of suspended particles. In addition, Yadav et al. [27] suggested in his study that the results obtained from the 2-dimensional model are close to the real results as analysed with the 3-D
flow. The CFD analysis can be used to determine the trajectory of these particles, determine their velocity and motion, and variables such as dynamic processes. It is important to investigate the sterilization of isolation rooms in hospitals, as there is a high risk of airborne transmission of the COVID-19 virus. Sterilization of the limited volume of air in the room is very important for healthcare professionals. The aerosolizing sanitization process of air can be designed by the CFD. However, there are a limited number of reports on the subject in the literature. This study focuses on the distribution of air released from sanitized air and ventilation within the room. It is intended to demonstrate how the flow behaves to achieve complete sanitation to ensure that the air released into every corner of the isolation room kills the COVID-19 virus. The data of the study were compared with a previous experimental study [28] to provide validation. The main purpose of the study is to examine both the ventilation and the flow physics of the air coming out of the sanitization machine to kill the COVID virus in the isolation room. In this context, the flow physics will be utilized to eliminate or minimize the virus in the isolation room, which is examined numerically.
Computational Design And Methodology
Geometrical Design Fig. 1 shows the model of the isolation room whose dimensions are taken from a hospital. All of the structures required in the isolation room (patient bed, door, ventilation, sanitized air machine) are shown in the model with their original dimensions. The length, width, and height
Figure 1. Schematic diagram of the computational domain (isolation room of hospital).
Figure 2. The geometry of mesh structure. (a) Isometric, (b) X-Z cross section, (c) X-Y cross section, (d) Y-Z cross section. of the isolation room are 9.1, 6.1, and 3.7 m respectively. The length and width of the ventilation grid are 1 and 0.3m respectively. The grill of the sanitized air machine where the air is blown is 1.8 and 0.3m. The exhaust air grids are also
1.8. and 0.3m.
Mesh Structure The mesh structure is defined as the ratio of the number of elements of the geometric structure to the number of nodes. More results that are accurate are obtained with an increasing number of elements. Decreasing the number of elements or nodes can lead to wrong results. The mesh structure is very important such studies. The correct mesh ratio is vital in optimizing results [29,30]. The mesh structure of the study is shown in Fig. 2. Discretization is required to solve partial differential equations which include the governing (Continuity Eq. 1, Momentum Eq. 2-3 and Energy Eq. 4) equations [31,32]. Second-order upwind schema with Boussinesq approximation is used for discretization. Simulation of the unstable air provides information about the flow physics of the system (i.e. the isolation room). The process of purifying the air in the isolation chamber by diffusion reveals the necessity of the simulation event to better understand the flow physics. It also includes phenomena such as pressure forces, buoyancy, forced convection, and heat transfer. The simulation of the natural ventilation process of the isolation chamber provides information about the behavior of flow physics [19]. Continuity equation [33,34] ∂ρ ∂t
Energy equation [33,34] dT ρ c p + ∇TV = ∇ (∇kT ) dt
Standard k-epsilon Model To numerically model laminar transitional flows, the transition Standard k - ε model is used in the present study. The k-ε model contains many unknown terms. In order to provide a more practical approach, this model should be applied to many models with turbulence. This situation reduces the unknowns. The standard k- ε model used as a publication is defined as two equations (Eq. 5 - 7). k denotes turbulent kinetic energy (Eq. 5, 6) and ε turbulent propagation (Eq. 7) [33].
( ) + ∂ ρU k = ∂ μ + μτ ∂k + P − ρε + P (6) ( ) ∂x σ κ ∂x ∂t ∂x
It is assumed that the turbulent viscosity (☐t) is proportional to the turbulence velocity and the length scale. These scales are derived from turbulence kinetic energy (k) and propagation rate (ε). Turbulent viscosity expression is as follows (Eq. 8) [35]. μt = ρCμ
Cμ is an experimental constant. In the k-ε turbulence model, k and ε values are needed to calculate the turbulent viscosity (☐t). These values are obtained from the Eq. 5–7 [36,37]. The turbulence kinetic energy generation due to mean velocity gradient and viscous forces, experimental constants Cε1 and Cε2, and σε, σκ Prandtl numbers. The values of the constants required for the solution are given in Table 1 [33]: As in many approaches, there are negligence assumptions in this model as well. Consequently, the modelled ε transport model can be used in a very similar form to the k transport equation. Validation of the Current Study and Mesh Independency In this study, the flow physics in the isolation room was examined numerically. A comparison was made with a previous experimental study to prove the accuracy of the study. The velocities in the cases in the experimental study [28] (v = 1.36 and 0.68 m/s) were examined and compared with the speeds in this study. The measurement points in the experimental study were used as a basis for comparison. Figure 3 (a) shows the measurement points, calculation area and velocity from various parts of the designed isolation chamber. It can be easily understood from Figures 3 (b) and (c) that the experimental [28] and current numerical values have a very good agreement against published data. Mesh Independency Mesh structure and independency analysis is an important issue for numerical studies to determine the optimum values of the study. In this context, combinations with different mesh numbers have been tried to determine the Table 1. Constants used in the k-ε model [25] Cε1
mesh structure of the isolation chamber. More results that are accurate were obtained compared to the increase in the number of meshes. However, as the number of meshes increased, temperature gives different results. Th e values obtained from the measurement point (P2) according to the number of meshes made for this study are shown in Fig
4. The error rate at low mesh counts (2042 and 5546) was
measured as 19.2% and 13.2%. In other, mesh numbers of 24906, 33048, 44988, 65958, and 148949 these error rates were calculated as 8%, 7.2%, 0.4%, 4.8%, 4.4%, respectively. As can be understood from the error rates, the optimum mesh number for the study was obtained as 44988. The maximum and average skewness values in this mesh number were 0.84 and 0.15, respectively.
Results And Discussion
The C FD a pproach c an b e u sed t o s tudy t he w orking fluid ( air) i n t he c omfort z one a nd t he p ollutants i n the fluid [38]. Figs. 5 and 6 show the operating and nonworking states of the sanitized air machine, respectively. The s treamlines o btained only e merging f rom t he operation of the sanitized air machine are given in Fig 5. Figs. 5a and 5b express top view (XZ plane) and 3D view (XYZ plane), respectively. According to the velocity distribution of the flow lines in the room, it was observed that the fresh air coming out of the sanitized air machine was distributed throughout the isolation room. The s ituation w hen the sanitized air machine is not working (Fig. 6) shows the setup of different i sometric v iews o f t he i solation r oom. The top view of the continuously variable streamlines (XZ plane) is given in Fig. 6a. It is clear that the behavior of the fluid i n t he r oom i s d ue t o t he v entilation g ap p ositioned at the top. The 3-dimensional (XYZ plane) view of the room is given in Fig. 6b. As can be understood from here, it seems that the streamlines fall down the ventilation hole and hit the floor. It is clear t hat t he fluid hitting the floor recoils and spreads to other parts of the room (walls, patient beds, etc.). Fig. 6 shows the streamlines in the room when only operating ventilation. It can be said that the current lines are distributed in the same density throughout the room. The figures below provide a quantitative review for the analysis of the flow, fluid velocity, and temperature and turbulence kinetic energy in the isolation room when the sanitized air machine and ventilation are operating simultaneously. The velocity vector projection is shown in Fig. 7. This means that the longer vectors are faster. It can be said that the speeds slow down because of the encounter of the air coming from the horizontal (air coming from the sanitized air machine) and from the vertical ventilation direction. It is also seen that the air hitting the walls relatively slows down. Fig. 7 shows the velocity distributions of the air in the XY (Fig. 7a) and YZ (Fig. 7b) planes, respectively. In the case where the sanitized air machine and the ventilation
Figure 3. Validation of case. (a) Validation of measurement points by experimental study [28], (b) velocity =1.36 m/s and case C2 considered, (c) velocity = 0.68 m/s and case D1 considered.
are working together, it is seen that the inlet velocities are high in the XY plane, but the velocity approaches zero in the eddies. In the YZ plane, it can be seen from the way that eddies are formed by the collision of the two air. Because of this mixing, the room is expected to be sterilized. This situation is very important for those staying in the isolation room.
Figs. 8a and 8b shows the temperature distributions when the sanitized air machine and the ventilation work together in the XY and YZ planes, respectively. Fig. 8a, showsthat inlet air and near the inlet contour was relatively lower than the ambient air temperature, while the temperature of the air leaving the sanitized air machine was higher. Accordingly, it is seen that the relatively cold
air (24°C) coming from the ventilation is mixed with the warm air (30°C) from the sanitized air machine. In both cases (Figure 8a and b), the temperature gradient, which is the mixture of cold air coming from the ventilation and hot air coming from the sanitized air machine, creates asymmetrical contours. It is clear that in the section where the sanitized air machine is located, a better mix is observed in Figure 8b (right side). The change of turbulent kinetic energy created by two airs mixed with each other in the room is given in Fig.
9. The representation of the region where the eddies are
formed is shown in Figure 10b. Two large eddies are seen in Fig. 10b shown to identify regions of high turbulence. The density of turbulence is proportional to the density of the mixture [19,38]. This means that a better mixture is created due to high turbulence. Thanks to the mixture, the COVID19 virus in the room will be completely destroyed. Thus, the lives of those staying in the room can be better protected.
Figure 5. Distribution of streamlines start from sanitized air ventilation. (a) top view (XZ plane), (b) 3D view (XYZ plane).
Figure 6. Distribution of streamlines initiated from air ventilation. (a) top view (XZ plane), (b) 3D view (XYZ plane).
Figure 7. Air velocity distribution in an isolation room (Sanitized air machine and ventilation work at the same time). (a) XY plane, (b) YZ plane.
Figure 8. Temprature contour distribution in an isolation room (Sanitized air machine and ventilation work at the same time). (a) XY plane, (b) YZ plane.
Conclusions
This study was carried out to understand the flow physics of the air in the isolation room. In the study, it was carried out to investigate the conditioned air from the ventilation and sanitized air machine reaches every point of the room and the evacuation of the COVID-19 virus. For this, CFD analysis was performed and the standard k-epsilon model was chosen to perform numerical
modelling. Considering that there is no known cure for the COVID-19 virus, it is necessary to minimize the risk of contracting the disease. This study has a crucial role at this point, because it is important to understand the physics of the fluid in the room to minimize the risk of getting the virus for patients and healthcare workers in the isolation room. As result of the flow modelling that was analyzed as unsteady in the study, it was concluded that it would be
Figure 9. Turbulent kinetic energy contour distribution in isolation room (Sanitized air machine ventilation work at the same time). (a) XY plane, (b) YZ plane.
more possible to evacuate the COVID-19 virus by increasing the turbulence according to the volume of the isolation room. • In this study, the situations causing the death of the virus because of the mixing of the air coming from the sterilized air machine and the ventilation in the isolation room were investigated. • The presence of eddies in the room was revealed. • The results of the study were compared with the previous experimental study and were found to be consistent. • The k-epsilon model, which includes the equations of motion, was used in CFD analysis. • From simulation analysis, it was found that high turbulent areas in the isolation chamber can be an effective way to disperse the disinfectant into a volume of the closed isolation chamber to kill or minimize the COVID-19 virus.
Data Availability Statement
The authors confirm that the data that supports the findings of this study are available within the article. Raw data that support the finding of this study are available from the corresponding author, upon reasonable request.
Ethics
There are no ethical issues with the publication of this manuscript.
References
- The article references can be accessed from the .pdf file.
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ARSLAN, E. Numerical analysis of an isolation room to prevent the spread of COVID-19. Journal of Thermal Engineering 2022, Vol. 8, pp. 632-641. https://doi.org/10.18186/thermal.1190529