key: cord-0740090-4m52mmrh
authors: Liu, Mingxin; Liu, Junjie; Cao, Qing; Li, Xingyang; Liu, Sumei; Ji, Shengcheng; Lin, Chao-Hsin; Wei, Daniel; Shen, Xiong; Long, Zhengwei; Chen, Qingyan
title: Evaluation of different air distribution systems in a commercial airliner cabin in terms of comfort and COVID-19 infection risk
date: 2021-11-18
journal: Build Environ
DOI: 10.1016/j.buildenv.2021.108590
sha: 81fd2e2a7d20ce48be905ef2f1692f17fdea89aa
doc_id: 740090
cord_uid: 4m52mmrh

The air distribution system in an airliner plays a key role in maintaining a comfortable and healthy environment in the aircraft cabin. To evaluate the performance of a novel displacement ventilation (DV) system and a traditional mixing ventilation (MV) system in an airliner cabin, this study conducted experiments and simulations in a seven-row cabin mockup. This investigation used ultrasonic anemometers and T-thermocouples to measure the air velocity, temperature and distribution of 1 μm and 5 μm particles. Simulation verifications were performed for these operating conditions, and additional scenarios with different occurrence source locations were also simulated. This study combined the Wells-Riley equation with a real case based on a COVID-19 outbreak among passengers on a long-distance bus to obtain the COVID-19 quanta value. Through an evaluation of the airflow organization, thermal comfort, and risk of COVID-19 infection, the two ventilation systems were compared. This investigation found that polydisperse particles should be used to calculate the risk of infection in airliner cabins. In addition, at the beginning of the pandemic, the infection risk with DV was lower than that with MV. In the middle and late stages of the epidemic, the infection risk with MV can be reduced when passengers wear masks, leading to an infection risk approximately equal to that of DV.

The number of people choosing to travel by airliner is increasing [1] . In contrast with the typical environments inside public buildings, the environments of airliner cabins are narrow spaces with high occupant densities. Long periods of travel make passengers and crew members more sensitive to the cabin environment [2, 3] . The airborne transmission of infectious diseases has been reported in commercial aircraft, such as the transmission of influenza [4] , tuberculosis [5] , severe acute respiratory syndrome (SARS) [6] , and coronavirus disease 2019 (COVID-19) [7] [8] [9] [10] [11] [12] . Therefore, research on the indoor environments of airliner cabins is particularly important. A well-operated environmental control system (ECS) on an airliner can effectively remove pollutants and heat in the air and maintain a comfortable and healthy environment in the cabin [13] [14] [15] . Hence, the evaluation of different air distribution systems in airliner cabins is necessary.

Currently, mixing ventilation (MV) is commonly installed in a variety of airliners.

This type of ventilation system cleans air with high-efficiency particulate air (HEPA) filters and supplies the air to the cabin through shoulder diffusers and/or ceiling diffusers, exhausting air through side diffusers near the floor of the cabin. Unfortunately, the heat rejection efficiency [16] and pollutant transmission control [17] [18] [19] [20] of MV systems are not satisfactory. To provide a healthy and comfortable environment to passengers and crew members, a new type of ventilation system must be developed for airliner cabins. A superior system to MV is displacement ventilation (DV), which has been found to provide thermal comfort and good indoor air quality (IAQ) in buildings [21] [22] [23] [24] and is widely used in other spaces such as train compartments [25] . Previous studies have demonstrated J o u r n a l P r e -p r o o f the ability of DV to provide a comfortable and healthy environment in airliner cabins [26] [27] [28] . Although there was a clear vertical temperature gradient for the DV systems, the air velocity was low in DV [27] . A more homogeneous cabin air flow was found in DV systems that led to improved thermal comfort for aisle seats [28] . In this type of ventilation, clean air is supplied through side diffusers near the floor of the cabin and exhausted through ceiling diffusers. Meanwhile, according to one previous study, among all MV systems (including those with only shoulder diffusers to supply air and only ceiling diffusers to supply air), an MV system in which shoulder diffusers and ceiling diffusers simultaneously supply air has the lowest air age and the highest heat rejection efficiency [16] . In comparison, DV has a higher heat rejection efficiency than all MV systems [16] and significantly reduces drafts [27] [28] [29] . However, the previous researches [27, 28] only conducted simple analysis on the flow field in airliner cabins, and could not provide practical guidance for the selection of the air distribution systems. The only research by You et al. [26] can only provide guidance on the selection of air distribution systems in the early stages of the epidemic, and cannot compare the performance of air distribution systems in the middle and late stages of the epidemic.

Generally, the evaluation of ventilation systems includes two aspects: comfort and health. Comfort is assessed in terms of airflow organization and thermal comfort. The evaluation of airflow organization usually requires experimental study or simulations of the air distribution. Thermal comfort evaluation typically involves the assessment of predicted mean vote (PMV), predicted percent dissatisfaction (PPD) and local thermal comfort. In the past, however, research on airliner cabins mainly addressed PMV and PPD J o u r n a l P r e -p r o o f [13] , with little attention paid to local thermal comfort. To evaluate the ability of a ventilation system to provide a healthy environment, particle or tracer gas measurements [13, 30] have generally been performed to obtain the distribution characteristics and removal efficiency of pollutants.

The characteristics of airflows and particle distributions in airliner cabins are usually determined by experimental measurements [31] [32] [33] [34] and numerical simulations [35] [36] [37] [38] .

The air distribution affects the distribution of particles and spread of pathogenic microbial aerosol particles [39, 40] . Gupta et al. [41] used the deterministic and Wells--Riley equations to evaluate the infection risk in a twin-aisle, fully occupied aircraft cabin. You et al. [26] combined the Wells-Riley equation with computational fluid dynamics (CFD) in simulating the infection risks for passengers in airliner cabins with different air distribution systems and found that there had highest infection risks in MV systems. A previous study calculated the distributions of contaminants in airliner cabins and concluded that the distributions of contaminants under an MV system were relatively uniform, which is not conducive to the control of infectious diseases [29] . However, the simulation results were probably recognized to be right only after they were compared and verified with experimental measurements [42] . Currently, there is little experimental data on high-precision particle distributions in airliner cabins. Existing studies involve few measured points [2, 43] and have not fully demonstrated the concentration distributions of contaminants in a section of the cabin; therefore, it is difficult to compare the experimental results with simulated values. Moreover, research on only a single particle size [44] cannot fully reflect the characteristics of contaminants in airliner cabins.

A previous study showed that 58% of influenza viruses were distributed on particles with an aerodynamic diameter ≥ 1 μm, and 42% on particles ≤ 1 μm [45] . Studies with multiple particle sizes can better demonstrate the particle distribution characteristics in airliner cabins. However, conducting solely experimental research is time consuming and labor intensive. Combining experimental investigation with simulation research allows more data to be obtained for a wide variety of cases.

During the COVID-19 epidemic, face masks were recommended by government health departments, and surgical-grade masks were widely used. The wearing of masks by passengers can effectively block the spread of the virus, thereby reducing the risk of COVID-19 infection. Masks can prevent the spraying of droplets by infected individuals, reduce the amounts and velocities of droplets, block virus-containing droplet nuclei, and prevent the wearer from inhaling these droplets. To analyze the impact of masks on the risk of passenger infection in airliner cabins, existing studies have used the filtering efficiency of masks on particles [41] . However, the number of viruses present on particles varies with particle size. It is not accurate to consider only the efficiency of masks in filtering particles. Rather, the efficiency of masks in filtering viruses should be used directly.

To evaluate the performance of the MV and DV systems, this study used the Wells-Riley equation to obtain the quanta value based on an outbreak on a long-distance bus during the 2020 COVID-19 epidemic. As reported in this paper, the particle concentration (1 μm, 5 μm) distribution in a single-aisle airliner cabin mockup was measured under two ventilation systems, and CFD simulations were performed on this scenario and other J o u r n a l P r e -p r o o f 7 cases. This study also compared MV and DV in terms of comfort and health. The results were used to evaluate contaminant transmission and thermal comfort performance under the two ventilation systems in airliner cabins.

In this study, a single-aisle full-scale cabin mockup was built in a laboratory. Figure   1 is an interior view of the mockup. There were 7 rows with 42 seats in which 42 dummies were seated. The cabin floor was covered with carpet and equipped with light strips. Air of a certain temperature was supplied to the cabin mockup through an air conditioning system. The airflow rate in the cabin was 9.5 L/s per passenger in both DV and MV systems. The surface temperature of the dummies was 30 °C ± 1 °C, dummies with a uniform temperature were set according to the study from Liu et al. [46] . [46] . The cabin mockup was ventilated by either of two ventilation systems, MV or DV, with identical airflow rates. The mixed ventilation system had 7 air supply diffusers on the ceiling, marked by ① in Figure 2 , and their airflow rate accounted for 40% of the total air supply; there were 7 air supply diffusers on the shoulders on both sides, marked by ②, and their airflow rate accounted for 60% of the total air supply; and its exhaust diffusers were located on the lower parts of both sidewalls near the floor, marked by ③ in Figure 2 . In the DV system, air was supplied through diffusers in the lower parts of both sidewalls near the floor, marked by ③, and exhausted through diffusers in the ceiling, marked by ①. 

For evaluation of the transport of contaminants and the thermal comfort of J o u r n a l P r e -p r o o f passengers under different ventilation systems, the three-dimensional air velocity, air temperature and particle concentration distributions were measured in the cabin mockup.

Ultrasonic anemometers were used to measure the air velocity in three directions, and Tthermocouples were used to measure the temperature. Particles were released by a polydisperse particle generator and measured by an aerodynamic particle size spectrometer. Table 1 shows the parameters of the instrument. Bioaerosols with a size between 1.0 and 5.0 μm generally remain in the air, while larger particles are quickly deposited on the surface [47, 48] . In this study, 1μm particles were selected to represent small-sized particles, and 5μm particles were selected to represent large-sized particles.

If the particle size is larger, it may be trapped on the surface. This experiment focused on 1-μm and 5-μm particles, and the contaminant source was located at seat 4B. The particles were released from a small ball with a diameter of 8 cm, at a flow rate of 39 L/min. Pointby-point measurement of the particle concentration distribution began after the concentration in the exhaust pipe had stabilized. Before the measurements were taken, the system was allowed to run for 24 hours to ensure stable velocity and temperature distributions. The resolution of each measurement point was 150 mm × 150 mm, and the measurement section was the cross section in front of the fourth row of dummies. A previous study determined that passengers in this section would be at high risk of infection [26] . The specific experimental methods and instrument selection in the present study were identical to those of Liu et al. [46] . 

For accurate simulation of the velocity, temperature and particle concentration distributions in the cabin mockup, a suitable turbulence model was required. The realizable k-ε model has been proved to be the most effective and economical model for simulating airflows in the enclosed spaces of airliner cabins [49, 50] .

To simulate the transport of particles in the cabin mockup, this study used the Lagrangian method, as shown in formula (1):

where ⃗⃗⃗⃗ is the particle velocity (m/s), is the time (s), is the air viscosity (Pas), is the particle density (kg/m 3 ), is the particle diameter (m), is the Cunningham correction factor, ⃗ is the air velocity (m/s), is the acceleration of gravity (m/s 2 ), is the air density (kg/m 3 ), is the Brownian force (N), ℎ is the thermophoretic force (N), and is the Saffman force (N). Table 2 lists the boundary conditions in the CFD simulation, which were defined according to the measurement data. In the simulations, the residuals represented relative errors in the calculation of a particular variable. The solutions were considered to have converged when the sum of the residuals was less than 10 -3 , 10 -3 and 10 -6 , respectively, for the continuity, momentum and energy equations. A standard wall function was used in the simulation. The computational domain was the air domain.

In the simulation, the particle source was generated in front of the dummy's head (4A, 4B, 4C), and particle sizes of 1 μm and 5 μm were selected. Reflective wall surfaces were employed as the wall boundary type, which means that particles would bounce off the boundary as the momentum changes [51] . 

According to a mesh-independence study, the di-erence in average outlet velocity for cases with 9 and 14 million elements was 1.32%, and the corresponding value for the difference in average outlet temperature between these two grid resolutions was 0.89%.

Because the di-erence between the output results of the numerical simulations with grid sizes of 9 and 14 million elements was small, a grid size of 9 million was selected for the remainder of the study. The minimum element size was 10 mm and was located at the air supply diffusers. Figure 3 shows the distribution of the grid for the MV and DV systems. The results of an error analysis between simulated and experimental values at the measurement points are shown in Table 3 . In comparison with the findings of a previous study [31] , the simulated values in the present study were closer to the experimental values, probably because of the fine calibration of the air supply boundary [46] . Although Simulations were performed for 1-μm and 5-μm particles. A previous study showed that 58% of influenza viruses were distributed on particles with an aerodynamic diameter ≥ 1 μm, and 42% on particles ≤ 1 μm [45] . Therefore, this investigation calculated the infection risk by 58% of particle concentration with 5 μm, and 42% of particle J o u r n a l P r e -p r o o f concentration with 1 μm. Considering the concentration distributions of the two particle sizes, the calculated infection rate was compared with the infection rate determined from the experimental results (as shown in Figure 6 ), and the overall trend was essentially the same in both cases. 

The velocity nonuniformity index (VUI) [16] is used to quantitatively analyze the distribution characteristics of the air velocity in airliner cabins and are calculated as follows:

where is the number of measurement points (-), is the time-averaged velocity at each point (m/s), and is the area-averaged velocity for all measurement points (m/s).

The temperature nonuniformity index (TUI) [16] is used to quantitatively analyze the distribution characteristics of the temperatures in airliner cabins and are calculated as follows:

where is the time-averaged temperature at each point (°C), and is the areaaveraged temperature for all measurement points (°C).

The heat removal efficiency (HRE) [16] reflects the energy utilization of a ventilation system, and indicates the temperature distribution uniformity and the performance in discharging contaminants. The calculation formula is:

where is the exhaust temperature (°C), is the supply air temperature (°C), and is the area-averaged temperature (°C).

The mean age of air [16] reflects the residence time of air in an airliner cabin and is calculated as follows:

where is the mean age of air (s), ( ) is the SF6 concentration at the measurement point (ppm), and (0) is the steady-state concentration at the beginning of the measurement (ppm).

Local thermal comfort in particular can reflect the influence of different parameters on passenger thermal comfort in airliner cabins [46] . Local thermal discomfort in airliner cabins is caused mainly by drafts and large vertical temperature differences between the head and ankles. These effects can be characterized by the draft rate (DR) and percentage dissatisfied (PD) [52] .

The draft rate (DR) [53] can be calculated by formula (12):

where is the local air temperature (°C), is the local mean air velocity (m/s), and is the local turbulence intensity (%).

The percentage dissatisfied (PD), as a function of the vertical air temperature difference between the head and ankles, can be calculated by formula (13) 

J o u r n a l P r e -p r o o f 20 where △ , is the vertical air temperature difference between head and ankles (°C).

In this study, the health evaluation was based on the COVID-19 infection risk.

According to the Wells-Riley equation [54] and the particle concentration obtained from the experiments and simulations, the infection risk for passengers in airliner cabins can be estimated with the use of formula (14):

where is the infection risk for a passenger in an airliner cabin (-), This study analyzed a case of COVID-19 transmission during a 2-hour bus ride [55] to determine the COVID-19 quanta value. Figure 7 is a schematic diagram of COVID-19

transmission inside the long-distance bus. The index person was located in seat 13B, the total number of infected passengers was 5, and the total infection risk was 5/22. The breathing flow rate for each passenger was set to 0.00053 m 3 /s. The COVID-19 quanta value was inversely calculated as 29.8/h. A previous study showed that the COVID-19 quanta was between 14.0/h and 48.0/h [56] . This quanta value was applicable only for correcting the particle concentration in the measurement and may not represent the actual situation. In addition, we assumed that the duration of airliner flights was 3 hours (10800 s). In our simulation setup, the breathing area was a small box with a size of 20×20×20 cm 3 in front of the head of each dummy [57] . However, compared to cabin mockups in previous studies , the velocity and temperature uniformity of the DV system have been greatly improved [46] . A previous study determined that the mean age of air in DV systems was significantly lower than in MV systems [16] . However, in the present study the mean age of air under the MV system (with simultaneous supplies from the ceiling and sides of the cabin) did not differ greatly from that under the DV system. TUIs, (c) HREs, and (d) mean age of air.

This study calculated the effects of drafts and vertical temperature differences on the thermal comfort of passengers under DV and MV systems and analyzed the DR and PD at 6 positions from A to F, as shown in Figure 9 . The drafts that occurred with MV were strong. This was true especially for the seats near the aisle, where the draft rate reached 40%, possibly because the MV system in this study used supply air from the shoulder diffusers and ceiling diffusers simultaneously. The drafts that occurred with DV were basically small at each position, and the maximum draft rate (approximately 5%) was observed near the wall, possibly because this location was near the air supply diffusers.

From the results of this study, we believe that the main reason for the discomfort of passengers under the MV system is drafts, whereas drafts have little effect on the thermal comfort of passengers under the DV system.

The two ventilation systems also produced significantly different temperature changes vertically from the head to the ankles. The reason for this result was the temperature stratification under the DV system and the relatively large temperature difference between head and ankles, whereas the temperature was more uniform under the MV system. From the results of this study, we believe that the main reason for the discomfort of passengers under the DV system is the excessive vertical temperature difference from the head to the ankles; in contrast, the vertical temperature difference had little effect on the thermal comfort of passengers under the MV system. If the head-ankle temperature difference can be kept sufficiently small, DV can maintain an acceptable thermal environment. This point of view is identical to that of Müller et al. [58] . The air distribution in an airliner cabin is related to the distribution of COVID-19 infection risk. The latter distribution was divided into three scenarios: no passengers wear masks, only the index passenger wears a mask, and all passengers wear masks. 

Surgical-grade masks reduce the number of viruses in exhaled breath by 3.4 times [59] . Figure The average infection risks for passengers in all scenarios are shown in Table 4 . 

In a previous study, single-sized particles were used to calculate the infection risk in airliner cabins [26] . However, it has been shown that viruses are distributed differently on particles of different aerodynamic diameters. For example, another previous study found that 58% of influenza viruses were distributed on particles with an aerodynamic diameter ≥ 1 μm, and 42% on particles ≤ 1 μm [45] . Therefore, calculations with singlesized particles may be inaccurate.

In the present study, the calculations of infection risk with mixing ventilation were taken as an example. These calculations were divided into three operating scenarios: 1μm particles, 5-μm particles, and mixed particles (1 μm, 5 μm). The results are displayed in Table 5 , which was calculated by simulation results. 

Healthy and comfortable environments in the cabins of commercial airliners are essential for passengers and crew members. In this study, two air distribution systems 

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This study was supported by the National Natural Science Foundation of China