Effect of flow disturbance induced by walking on the performance of personalized ventilation coupled with mixing ventilation

Building and Environment 160 (2019) 106217

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Effect of flow disturbance induced by walking on the performance of personalized ventilation coupled with mixing ventilation

T

Douaa Al Assaad, Kamel Ghali, Nesreen Ghaddar* Mechanical Engineering Department, American University of Beirut, P.O. Box 11-0236, Beirut, 1107-2020, Lebanon

A R T I C LE I N FO

A B S T R A C T

Keywords: Flow disturbance due to walking Personalized ventilation Indoor air quality Dynamic mesh Computational fluid dynamics

The aim of this work is to investigate the effect of a walking occupant disturbance, on the performance of personalized ventilation (PV). A computational fluid dynamics (CFD) model of an office space with two people was developed. The first occupant was seated while using the PV (4 L/s). The second occupant was walking along a linear path parallel to the PV jet direction. A dynamic mesh was adopted to model the walking motion. The flow field was validated experimentally in a chamber equipped with stationary and walking manikins. The validated CFD model was used to assess the PV jet during the disturbance. Moreover, tracer gas was used to evaluate the breathable air quality of the PV user. CO2 was used to model passive contaminants in the space, while SF6 represented a local contamination source (walking occupant). Results showed that the walking disturbance deteriorated the PV efficiency. When the occupant was at its closest to the seated occupant and PV, turbulence intensities increased to 21%. This enhanced entrainment of contaminants into the jet, causing a decrease of εv by 72% and 56% for CO2 and SF6. When the occupant is at the end of the trajectory, the negative pressure at the back deflected the PV jet away from the breathing zone. This decreased εv by 60% and 45% for CO2 and SF6. A safe distance of 85 cm between the occupants was recommended to preserve air quality. Moreover, using a wider PV of 18.5 cm diameter (15 L/s) increased εv significantly.

1. Introduction Regulating indoor air quality (IAQ) in workspaces is crucial to protect workers from possible contamination due to indoor pollutants, as it directly affects their health and wellbeing [1]. This is done by installing heating ventilation and air conditioning (HVAC) systems in buildings to provide occupants with good breathable air quality and thermal comfort. These requirements are achieved through supplying conditioned fresh air to regulate the thermal environment and dilute contaminants’ concentration. There are many types of indoor pollutants such as airborne passive contaminants, which compromise IAQ and are the major cause of sick building syndromes (headaches, dizziness, nausea…) [2]. They originate from office equipment, human respiratory activities and volatile organic compounds from furniture and flooring materials [3]. The contaminant spread in the space depends on the airflow established by the HVAC system and the indoor disturbances. The occurrence of disturbances in the indoor environment is inevitable and can hinder the performance of HVAC systems. Indoor disturbances can originate from human activity (walking, moving, fidgeting…) [4] or mechanical activities such as the sudden

*

opening and closing of a door [5]. They can induce local unsteadiness and airflow perturbations in the space increasing local mixing effects. In the presence of a contaminating source, the mixing effects induced by the disturbances can transport pollutants and spread them away from their emitted locations to other cleaner zones. The effect of disturbances on IAQ has been studied in the case of mixing ventilation (MV) systems [6,7]. Hang et al. [8] studied through numerical modeling, the effect of human walking on the flow field and the transmission of contaminants from a patient in a six-bed isolation room equipped with a MV system. They reported that the disturbance induced by walking enhanced the mixing and spread contaminants in the space deteriorating IAQ next to other patients and that it took up to 60 s to recover the initial state once the disturbance stopped. In an experimental study of an office space mockup equipped with MV, Brohus et al. [9] found that the horizontal walking motion transported contaminants from the passive source to other cleaner locations. Additionally, they reported that random human motions generated local mixing phenomena and deteriorated air quality in case the moving occupant is at the same time a contamination source. Recently, new advanced air distribution applications have emerged

Corresponding author. E-mail address: [email protected] (N. Ghaddar).

https://doi.org/10.1016/j.buildenv.2019.106217 Received 3 May 2019; Received in revised form 31 May 2019; Accepted 14 June 2019 Available online 17 June 2019 0360-1323/ © 2019 Elsevier Ltd. All rights reserved.

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Nomenclature BZ C CFD d DNS Δt εv ex fr

HEPA HVAC IAQ LES MV PIV PV RANS t UDF Vw

Breathing zone Concentration (ppm) Computational fluid dynamics Distance crossed by the manikin (m) Direct numerical simulation Time step size in seconds (s) Ventilation effectiveness index Exhaust Fresh air

High efficiency particulate air Heating ventilation and air conditioning Indoor air quality Large eddy simulation Mixing ventilation Particle image velocimetry Personalized ventilation Reynolds-averaged Navier-Stokes time in seconds (s) User defined functions Velocity of walking (m/s)

Fig. 1. Illustration of (a) the office space conditioned by the MV and PV systems and occupied by stationary and walking occupants, and (b) the different stages of the walking period. 2

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compared to single PV jets. In spaces equipped with PV, disturbances occurring in PV vicinity are more compromising in terms of IAQ than the case of standalone MV system, in the absence or presence of contamination sources. The primary jet supplied from the PV interacts with the surrounding air through entrainment and turbulent diffusion phenomena. Consequently, the highest IAQ and comfort can be achieved by the PV, if the mixing of the primary jet with the surrounding air is minimal. This highly depends on the turbulence levels present in the surrounding of the PV jet. In indoor spaces, where disturbances occur in the neighboring vicinity of the PV, turbulence levels surrounding the primary jet increase due to enhanced mixing effect. This can negatively affect the performance of the PV in terms of IAQ whether a local source of passive contaminants is present or not. In the absence of a pollution source, any disturbance occurring next to the PV enhances the entrainment of contaminants from the relaxed indoor space into the jet. Moreover, contaminants can be entrained into the PV jet if a pollution

with the aim of reducing energy consumption. This included localization of the delivery of clean cool fresh air towards the occupants and relaxing the requirements of the space in terms of IAQ and background temperatures. A typical example of such HVAC systems is the personalized ventilation (PV) system. PV systems deliver high breathable air quality directly towards the occupant's breathing zone (BZ) such that entrainment and mixing with the surrounding environment is minimal [10,11]. PV systems existed in many configurations, which have been studied in literature (desk mounted, ceiling mounted…) [12,13]. Cermak et al. [14] tested experimentally two types of PV (vertical desk grills and round movable panels) in conjunction with a MV system in an office space occupied by two breathing thermal manikins. Their results showed that PV was able to enhance IAQ in the BZ as well thermal comfort while the mixed indoor environment was contaminated. In a numerical study, Makhoul et al. [12] applied the concept of coaxial jets to ceiling PV in offices. They reported that the system was able to improve thermal comfort and IAQ in the occupant microenvironment

Fig. 2. Illustration of: (a) the computational domain as seen in Fluent, (b) the computational thermal manikin with its different dimensions. 3

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speeds ranging from 0.8 m/s to 1.2 m/s [17]. In this study, the occupant is considered to walk at a typical velocity Vw of 1 m/s. For simplification, the walking phenomena was treated as a simple translational motion (see Fig. 1(b)) where the swinging motion of the hands and feet were not considered.

source is present and the contaminants are transported closer to the PV due to any disturbance. Consequently, the efficiency of the PV in providing high breathable air quality to the occupant's BZ might be compromised. To the authors' knowledge, no studies in literature have investigated the effect of an indoor disturbance on the performance of PV assisting MV, in the absence and presence of a local source of passive contaminants. In this work, the effect of common indoor disturbance is studied for an office space conditioned with the conventional MV system aided by a desk-mounted PV system delivering cool fresh air towards a seated occupant. The aim is to investigate the effect of the disturbance on the behavior of the PV jet and hence the breathable air quality delivered to the occupant. This is investigated in the absence and presence of local passive contaminants represented by SF6 and its source is the walking occupant. Due to the dynamic nature and complexity of the flow field, a transient 3D computational fluid dynamics (CFD) model was developed to solve for the different flow and thermal field variables in the space (velocity, turbulence, temperature) as well as contaminants’ concentration and transport. The CFD model was coupled with a dynamic mesh to simulate the occupant walking motion and accurately predict the ensued wake flow field and subsequent contaminant transport. The CFD model was validated experimentally. An analytical study was then conducted to assess the ability of the system in protecting occupants from indoor contaminants in the presence of a disturbance induced by walking.

3. Methodology 3.1. CFD modeling The dynamic flow field in the room involved complex and transient physical behaviors due to the presence of the MV system, which established a flow field characterized by significant turbulence intensities as well as stagnant recirculation zones. Moreover, the PV horizontal high velocity jet interacted with the macroclimate air through entrainment and turbulent diffusion. The seated and walking occupants were heat sources emitting constant heat fluxes. Therefore, density differences generated thermal plumes in the space. Furthermore, the standing occupant performed walking activity. This motion generated a turbulent wake flow field that trailed behind the occupant's body in addition to the thermal plume. Moreover, passive contaminants were assumed present in the space and were subject to entrainment and turbulent diffusion. Therefore, in order to solve for the complex flow field variables (velocity, temperature, contaminants' concentration…), 3-D CFD modeling was adopted. The CFD model was used to understand the flow field behavior and its effect on contaminants' transport. Hence, the breathable air quality at the occupant's breathing zone (BZ) can be assessed. Note that the BZ was defined as a sphere of diameter 2 cm and situated at 2.5 cm away from the occupant's nose [19]. Since 3-D CFD modeling was adopted, the commercial software ANSYS Fluent version 17.2 [20] was used to solve for the different variables in the space. Additionally, in order to represent the walking and seated occupants in the CFD model, a computational thermal manikin model was used. The computational domain of the space with its different components as well as the adopted thermal manikin with its different dimensions are shown in Fig. 2.

2. System description In this study, a typical office space was assumed conditioned by a conventional MV system assisted by a horizontal desk-mounted PV system. The office was equipped with a workstation where an occupant was seated and performing sedentary office activity. Fig. 1(a) illustrates the space with the considered HVAC system combination as well as the occupant. The MV system supplied a mixture of cool fresh air and warm recirculated air from the ceiling level in the space. The fresh air has typical CO2 concentrations of 450 ppm [3]. During MV operation, mixing occurred between the supplied air and the warm indoor air in the space before the air was exhausted from the return ceiling diffuser. Hence, uniform temperatures and contaminants' concentrations can be found in the macroclimate space. The macroclimate refers to the mixed indoor environment outside the microclimate, which is the environment in the vicinity of the seated occupant's breathing zone. Note that, high efficiency particulate air (HEPA) filters were installed at the MV inlet diffuser to clean the recirculated air before supplying it into the room. The PV system was operated using an independent air-handling unit, which withdrew fresh air from an adjacent fresh air source at typical CO2 concentration of 450 ppm. The PV supplied fresh air horizontally from a rounded outlet towards the stationary occupant's face (See Fig. 1(a)). Due to the swirl component of the PV fan, honeycomb flow straighteners sandwiched between two screens were placed downstream from the fan to straighten the flow. This technique was previously used to reduce swirl effect and yielded good results [15,16]. The PV was also equipped with HEPA filters to clean the air from all active particulate matter. In the current study, the flow field in the space was subjected to a typical disturbance by a walking occupant. Since the aim of this work is study the PV ability in providing occupant protection in the presence of a contamination source, the walking occupant was considered as infected. It constitutes a moving local contamination source generating passive pollutants represented by SF6 at the mouth level (z = 1. 5 m) as seen in Fig. 1(a). The occupant was assumed to enter the office and walk past the seated occupant's workstation disturbing the air near the PV. The trajectory followed by the occupant is along a linear path of length 2.5 m parallel to the PV jet direction. The walking occupant was situated at a distance of 65 cm away from the PV centerline (See Fig. 1(a)). In indoor spaces, the average occupant walks at typical

3.1.1. Dynamic mesh modeling In order to solve the different flow field variables accurately, an appropriate mesh needs to be implemented for the considered computational domain. Moreover, the mesh needs to adjust itself to the walking motion without compromising the quality of the grid. Therefore, a dynamic mesh was adopted when motion was present. In previous studies, dynamic meshes have been coupled with CFD models involving time-depending object motion and yielded good results [17,18,21–23]. When the manikin walked forwards, a specific control volume in the space, encompassing the linear path followed by the manikin, was directly affected by the motion while the rest of the space remains static. Therefore, the mesh was divided into static and dynamic meshes zones as can be seen in Fig. 3(a). These two zones were separated by interfaces through which information was exchanged (sliding mesh theory) [24]. Thus, instead of updating the entire mesh during the motion, only the dynamic zone was re-meshed while the static zone remained unaffected. This technique preserved the grid quality and reduced computational time. In this study, the dynamic mesh zone was updated by using the spring based smoothing method as well as the local re-meshing method [17,18,23]. Hence, the adopted CFD model was transient and would advance in time at a time step Δt. The time step was chosen such that the distance crossed by the manikin during Δt was smaller than the element size. Otherwise, this could cause the mesh control volumes to overlap, thus creating negative cell volumes. For this study, a time step of 0.001 s was found as adequate to update the dynamic mesh and advance in time without deteriorating grid quality. Both the static and dynamic mesh zones were meshed with an unstructured grid having tetrahedral control volumes. Face sizing of 4

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3.1.2. Airflow field modeling The flow field mechanisms generated by the walking thermal manikin, PV & MV systems lead to high turbulence intensities in the space. Therefore, a proper turbulence model was adopted. The application of direct numerical simulation (DNS) or large eddy simulations (LES) required considerable computational cost and time [21]. Consequently, the Reynolds-averaged Navier-Stokes (RANS) method was chosen to model the unsteady flow and disturbances due to the moving manikin. The RNG k-ε model with enhanced wall treatment was used due to its accuracy in predicting airflow fields over solid bodies [25–27]. This model was used in the study of Tao et al. [17] on the numerical modeling of human walking, and compared it with other turbulence models. It was found that the RNG k-ε model gave the most compatible results with experimental particle image velocimetry (PIV) measurements. Since there were no density variations in the space, the Boussinesq approximation was chosen to account for buoyancy driven flows [28,29]. As for the species' concentration field, it was solved using the species transport equations. Note that CO2 was used to model the presence of passive contaminants in the space, while SF6 was used to model the local source of contaminants generated by the moving manikin. The momentum, energy, k, ε and species’ transport equations were discretized using the second order upwind scheme while the transient term was discretized using the second order implicit scheme. As for pressure, the “PRESTO!” scheme was used since it accounts for pressure gradients at the boundaries [28,29]. All variables were time dependent since human motion was present. As for the pressure-velocity coupling, the “PISO” algorithm was chosen due to its suitability in transient applications [20]. For a converged solution, several criteria were applied. The scaled residuals were lower than 10−8 for the energy equation and lower than 10−6 for the rest of the variables [28]. Moreover, the net heat flux was 1% less than the total heat gained and mass balance was ensured in the space. 3.1.3. Boundary conditions A physical solution to the problem was determined by the proper assignment of the boundary conditions in the space. The MV and PV supply diffusers were set as velocity inlets having constant velocities of 0.4 m/s and 0.51 m/s respectively, specified airflow temperatures of 19 °C and 22 °C respectively, turbulence intensities of 5%, length scales of 0.017 m and 0.007 m respectively. The MV exhaust diffuser was assigned as a pressure outlet with zero gauge pressure, turbulence intensity (5%) and hydraulic diameter (0.451 m). Surfaces such as walls, ceiling, computer, lights, office equipment and thermal manikins are assigned as walls generating heat fluxes (10 W/m2, 10 W/m2, 100 W, 100 W, 30 W, 75 W × 2, respectively) adding to the room load to be removed by the MV system. To model the manikin walking motion (Vw = 1 m/s), a user defined function (UDF) was assigned as a condition in the dynamic mesh settings. The effect of the disturbance on the breathable air quality was studied in the absence and presence of a local source of passive contaminants. In the absence of a local source, the breathable air quality was assessed using carbon dioxide (CO2). An elevated CO2 concentration of 850 ppm was specified at the MV inlet while a typical CO2 concentration of 450 ppm [3] was specified at the PV nozzle outlet. In the presence of a local source of passive contamination, the walking manikin was considered as the source since it walked at close proximity to the PV. The latter generated contaminating species (SF6), emitted from the mouth segment (See Fig. 2(b)). The latter was set as a velocity inlet with specified temperature (37 °C), hydraulic diameter (0.03 m), turbulence intensity (5%), and a concentration of 106 ppm of SF6. Note that the PV fresh air was considered free from any contaminating species (0 ppm of SF6) while the recirculated air supplied by the MV has a small concentration of 300 ppm of SF6.

Fig. 3. Illustration of: (a) the computational domain with static and dynamic mesh zones, (b) the corresponding mesh. Table 1 Grid independence test using 5 different mesh cases.

Mesh Mesh Mesh Mesh Mesh

1 2 3 4 5

Face sizing Manikin/walls (cm)

Number of control volumes in static (dynamic) zones

Relative error between two consecutive meshes (%)

2/10 2/8 1.5/7 1.5/6.5 1.5/6

217,510 271,888 328,984 401,360 641,850

– 30.8% 17.6% 7.3% 4.5%

(160,428) (160,428) (239,038) (239,038) (239,038)

1.5 cm, 2 cm and 6 cm were set for the manikin; for the inlets of the supply, exhaust, and PV, and for wall boundaries respectively. In order to capture the entrainment process between the PV and the surrounding air, a sphere of influence of radius 0.2 m was set between the PV and the seated manikin (See Fig. 3(c)). This grid had a maximum skewness of 0.86 and a minimum orthogonality of 0.33. Moreover, this grid configuration ensured a mesh independent solution with a maximum relative error of less than 5%. The relative error was defined based on the difference of average temperature between two consecutive mesh configurations, in the cross sectional plane of the walking manikin (y = 0.7 m). The static mesh zone was characterized by 641,850 control volumes and 121,739 nodes while the dynamic mesh zone was characterized by 239,038 control volumes and 45197 nodes. The final grid can be seen in Fig. 3(b) while the different mesh cases can be seen in Table 1. Note that the maximum skewness and minimum orthogonality during dynamic meshing (re-meshing & smoothing processes) remained below 0.91 and above 0.26 respectively.

3.1.4. Occupant contamination assessment To assess occupant contamination in the space, the tracer gas 5

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Fig. 4. Illustration of (a) the experimental chamber schema with the MV and PV systems, (b) measurement poles' locations and (c) climatic chamber with manikins, PV and hot wires. Table 2 The boundary conditions measured experimentally and used as input into the CFD model. Boundary conditions measured experimentally Boundary

Condition

MV supply

Measured velocity: 0.4 ± 0.1 m/s Measured inlet temperature: 19 ± 0.2 °C Turbulence intensity: 5 ± 1% Instrument used: OMEGA HHF2005HW model Exhaust flow rate: 64 L/s Measured outlet temperature: 26 ± 0.2 °C Turbulence intensity: 5 ± 1% Instrument used: OMEGA HHF2005HW hot wire anemometer Surface heat flux due to 75 W heat source: 39 W/m2. Walking speed of standing manikin: 1 m/s Instruments used: SWEMA03 anemometers Ceiling lights (100 W) Walls with U-value of 1.5 W/m2·K (10 W/m2) Computer (100 W); Office equipment (25 W); Seated manikin (39 W/m2) ΔTin-out measured using OMEGA T-type thermocouples Velocity inlet (constant value): 0.9 ± 0.03 m/s Constant inlet temperature: 22 ± 0.2 °C Turbulence intensity: 5 ± 1% Instruments used: SWEMA03 anemometers

MV exhaust

Walking thermal manikin

Ceiling, walls, computer, office equipment, seated thermal manikin

PV nozzle outlet

contaminants in the exhaled air through the mouth [30]. The ventilation effectiveness index εv was used to evaluate IAQ. This index was suggested by Melikov et al. [30] to assess the ability of a PV in providing fresh air. To evaluate the ability of the PV in providing good breathable air quality in the BZ, Al-Assaad et al. [28] adopted the

method was used. Carbon dioxide (CO2) was selected to represent passive contaminants and assess the breathable air quality delivered by the PV in the presence of flow disturbance induced by walking. When considering the walking occupant as a local source of passive contaminants, sulfur hexafluoride (SF6) was used to represent 6

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Fig. 5. Illustration of the contours of: (a) velocity, (b) temperature and (c) turbulence intensity in the y = 0.7 m and y = 1.35 m cross sectional planes at time t = 0 s, before the walking event.

average values of εv (%). The ventilation effectiveness index is given by

ε v (%) =

Cex − Ci × 100 Cex − Cfr

became polluted and breathable air quality was compromised.

3.2. Experimental methodology

(1)

where Cex is the species' concentration at the outlet, Ci is the CO2 or SF6 concentration at any location in the space, Cfr is the concentration of CO2 or SF6 in the PV primary fresh air supply. Since εv was evaluated at the BZ, Ci in equation (1) corresponds to CBZ. When εv index is high, the contaminants' concentration was close to that supplied at the PV nozzle outlet indicating high IAQ. However, when εv was low, the PV air

To ensure the validity of the CFD and dynamic mesh models in predicting the flow field variables (temperature, velocity, species’ concentration), as well as their temporal variations, experiments were conducted in a climatic chamber equipped with MV and PV systems. The experimental chamber had dimensions of 3.4 m × 3.4 m × 2.8 m. It was conditioned by MV and PV system as 7

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Fig. 6. Experimental and predicted values of velocity and temperature gradients at the different measurement poles (Poles 1 to 4) and in the PV jet.

flow and thermal fields generated by the disturbance were validated. To measure the velocity of the MV and PV supplies and MV exhaust, OMEGA HHF2005HW hotwire anemometers were used. They are characterized by a measurement accuracy of ± 0.5 °C with full-scale velocity measurement ranging between 0.2 m/s and 20 m/s. The SWEMA03 hotwire anemometers were employed to measure the steady-state velocity and temperature fields’ in the PV jet and MV macroclimate respectively, as well as transient velocity variation due to the walking manikin. These anemometers have a temperature measurement accuracy of ± 0.1 °C and velocity measurements ranging between 0.05 m/s and 3 m/s with an accuracy of ± 4% and a response time of 0.2 s. The anemometers were equipped with SWEMA Multipoint software allowing for real time data logging of thermal and velocity fields. To validate the PV jet flow fields, three SWEMA03 hot wires were positioned downstream from the PV jet: at the inlet, at a distance of 0.2 m downstream from the inlet and at the BZ. Hence, they can measure the horizontal variation in the thermal and velocity fields. As for the MV flow field, the vertical gradients of temperature and velocity in the MV macroclimate can be measured at different locations and heights in the space before the occurrence of the disturbance. For this reason, measurement poles were placed at four different locations in the space. The velocities and temperatures were measured at each pole, at four different heights (0.4 m, 0.8 m, 1.2 m and 1.6 m). Two measurement poles (Pole 1 and 2) were placed at an x-distance of 0.25 m and 0.4 m from the seated and walking manikins respectively, and a ydistance of ± 0.2 m symmetrically with respect to the PV nozzle outlet (See Fig. 4(a)). Consequently, the hot wires can measure the temperatures and velocities near the PV at steady state conditions. The third pole (Pole 3) was placed at a y-distance of 0.1 m from the workspace to measure the air velocities and temperatures next to the heat sources generating thermal plumes. The final measurement pole (Pole 4), was positioned beneath the exhaust, at a distance of 0.15 m from the left wall, to measure the flow field next to the pressure outlet. Note that

seen in Figs. 2 and 4(a). The different boundary conditions measured in the experimental chamber and used as input into the CFD model are summarized in Table 2. The MV system supplied cool recirculated air from a ceiling diffuser at a velocity of 0.4 m/s and a temperature of 19 °C assuring a thermally comfortable temperature of 26 °C inside the space [31]. In the middle of the room, a thermal manikin representing a seated occupant was positioned and the PV was placed on the desk at a distance of 40 cm from the manikin's face. A PV fan having a typical diameter of 10 cm was sandwiched between two screens to reduce its swirl effects [32] and laminarize the flow before supplying it towards the manikin. Note that, the PV withdrew fresh air from an adjacent fresh air source at a flow rate of 4 L/s and a temperature of 22 °C. The PV flow rate was such that the velocity at the face was between [0.1–0.2] m/s, thus avoiding draft discomfort. Moreover, the PV supply temperature was 4 °C lower than the background temperature to avoid temperature dissymmetry and hence thermal discomfort [28,33]. Next to the seated manikin, a standing manikin (See Fig. 4(a)) was positioned. Both thermal manikins were manufactured with 3 mm-thick aluminum sheets and have the same dimensions as the computational thermal manikins (See Fig. 2(b)) which mimicked the anatomy of an average 1.7 m tall human. The inside of each manikin was equipped with a heat source of 75 W, identical to the heat flux generated by an occupant at sedentary activity. The heat sources were placed in the middle of each of the manikins' bodies to produce a uniform heat flux distribution of 39 W/m2 between the segments. Note that the manikins' edges were insulated with Styrofoam insulation having a U-value of 1.14 W/m2⋅K. The walking manikin was connected to a wheel cart with wheels of radius 3 cm, and controlled by a DC motor (318 rpm) powered by a battery. This assured a walking speed of 1 m/s. The motor was controlled by a manual on/off switch located outside the climatic chamber (See Fig. 1(b)). The developed CFD model predicted the velocity and thermal fields in the space. First, the steady MV and PV flow and thermal fields were validated before the occurrence of the disturbance. Then, the transient 8

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Fig. 7. Illustration of the velocity contours, pressure contours and turbulence intensity contours in the z = 1.1 m between t = [0.45 s–1.5 s].

Poles 1 and 2 also measured the transient variations of velocity during the walking event. In fact, when the manikin walked forwards, it disturbs the air in the vicinity of the PV jet. Hence, the hot wires at Poles 1 and 2 placed between the PV jet and the manikin's path, can detect the motion. Consequently, the hot wires measured the corresponding transient airflow velocities during and after the manikin motion. Note that this technique was also adopted in the previous study of Han et al. [22] to validate the ability of their CFD model in predicting the wake flow field of a walking manikin.

manikins (150 W), lighting (100 W), computer (100 W) assuring a room temperature of 26 °C. Note that the heat flux from the walls was measured using an OMEGA heat flux meter OS-652 model characterized by an accuracy of ± 1% for heat flux measurement at ambient temperature ranges of −18 °C–43 °C and a response time of 1 s. After 5 h of running the experiment, the flow field in the room reached steady state conditions. Consequently, the corresponding MV and PV flow fields can be measured before walking starts. Initially, the walking manikin was standing still behind the seated manikin (See Fig. 2). The walking period was initiated by switching on the DC motor. Subsequently, real time velocities were instantly measured by the hot wire anemometers at different heights and logged by the Multipoint software. The DC motor was switched off after 2.5 s when the manikin crosses 2.5 m. The velocities and temperatures measured experimentally were then compared with the CFD model results. All experiments were repeated three times for accuracy and repeatability.

3.2.1. Experimental protocol The experiment was initiated by turning on the MV and PV systems in the room as well as the lights and the different heat sources. In addition, the thermal manikins’ heat sources were turned on and emitted 75 W each. The MV supply velocity was set at 0.4 m/s and 19 °C while the PV supply velocity was set at 0.5 m/s (4 L/s) and 22 °C. Thus, a load of 40 W/m2 was removed from the space due to the walls (100 W), 9

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Fig. 8. Illustration of the velocity contours, pressure contours and turbulence intensity contours in the z = 1.1 m between t = [1.5 s–7 s].

4. Results and discussion

measured values of velocity/temperature with a maximum relative error of 13%/3.64% found at Pole 1 at 0.8 m/1.2 m. As for the PV, velocities decreased from 0.52 ± 0.04 m/s to 0.12 ± 0.03 m/s and temperatures increased from 22 ± 0.2 °C to 23.1 ± 0.1 °C from the PV supply to the BZ (See Fig. 6). Good agreement was found between numerical and experimental values with a maximum relative error of 12.5% and 4.27% in the velocity and temperature respectively at a distance of 0.2 m from the PV nozzle outlet.

When the standing occupant walks forwards towards the seated occupant, the air velocity and pressure fields in the vicinity were affected, which also effected the horizontal PV jet flow. The experimental chamber was modeled using CFD and the boundary conditions measured experimentally were used as input to the numerical model as was presented in Table 2. The results for the airflow and temperature field obtained from the CFD model are first presented to understand their behavior and to validate the velocity and temperature fields before and during the occurrence of the disturbance induced by walking. Then, the species’ concentration field in the space was presented to assess the breathable air quality in the BZ of the seated occupant.

4.2. Dynamic airflow field behavior and velocity validation When the standing occupant started moving closer to the PV, the airflow field in its vicinity is disturbed. Figs. 7 and 8 illustrate the contours of velocity, pressure and turbulence intensity from the start of the walking event at t = 0.45 s until t = 1.5 s, and from 1.5 s until the end at 7 s at the z = 1.1 m plane. Due to the translational motion, a pressure difference was established between the front and back of the standing occupant throughout the disturbance period. High pressure was observed at the front of the occupant due to the forward motion compressing the air, while low pressure was observed at the back. Consequently, the air was driven from the front to the back and was entrained into the wake flow. This can be seen in the pressure contours as well as the velocity contours in Figs. 7 and 8, from t = 0.45 s–2.5 s. According to Figs. 7 and 8, a constant pressure difference of 1.8 Pa existed between the front and back of the occupant throughout the walking event. Due to this pressure difference driving the flow, a wake flow trailed behind the body with maximum peak velocities of 1.5 m/s. Note that the turbulence surrounding the walking occupant increased to 16% due to the motion (See Fig. 7, t = 0.45 s). Before the walking occupant reached the seated occupant, the pressure difference and circulating airflow did not influence the PV jet.

4.1. Steady state conditions and validation Before the standing occupant walked forwards, a steady state flow field was established in the space. This can be seen in Fig. 5 which illustrates the contours of velocity, temperature and turbulence intensity (%) in the y = 0.7 m and y = 1.35 m mid-planes, before the occurrence of the disturbance. According to Fig. 5(a) and (b), the space was characterized by a quiescent environment of 26 °C, with a velocity range of [0.05 m/s - 0.13 m/s]. As for the PV, its velocity decreased from 0.51 m/s to 0.22 m/s while its temperature increased from 22 °C to 23 °C. Note that the turbulence intensity reached a maximum of 10% in the surrounding of the PV jet (See Fig. 5(c)). This can be seen from the measured values of velocity and temperature (Poles 1 to 4) in Fig. 6. According to Fig. 6 (Poles 1 to 4), the MV background velocities ranged between [0.04 ± 0.02 m/s – 0.1 ± 0.02 m/s], while the average space temperatures ranged between [25.4 ± 0.2 °C–26.4 ± 0.2 °C]. Good agreement was found between the predicted and experimentally 10

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Fig. 9. Experimental and predicted CFD values of the airflow field velocities measured at a height of: (a) z = 0.4 m, (b) 0.8 m, (c) 1.2 m, (d) 1.6 m for Poles 1 and 2.

period while the walking occupant was still behind the seated occupant. Additionally, the PV jet was no longer affected by mixing effects, but by the air joining the wake flow field at the back of the occupant (See Fig. 8, t = 2.5 s). The negative pressure (−0.3 Pa) found at the back created a suction effect and thus the air was entrained from the space into the wake. The suction effect also deflected the PV jet and the core region was pulled away from the BZ towards the wake. This exposed the BZ to the macroclimate air and increased the risk of exposure to contaminants that might be present. This can be seen in the contours of velocity and pressure in Fig. 8 at 2.5 s. At t = 2.5 s, the walking period was concluded and the standing occupant stood still until the wake flow was fully dissipated at t = 7 s. According to Fig. 8 at 7 s, it can be seen from the pressure contours that the pressure difference between the front and back of the occupant disappeared. Moreover, it can be seen from the velocity contours that the wake dissipated (residual velocities of 0.25 m/s) and that no airflow circulated around the body. Additionally, the PV jet returned to its

This can be seen in the pressure and velocity contours shown in Fig. 7 at t = 0.45 s and 1.35 s. When the walking occupant reached the seated occupant after crossing a distance of 1.35 m, the flow field generated by the walking motion started to influence the PV jet. As the occupant walked close to the seated occupant and the PV, turbulence intensities increased to reach maximum values of 21% in the manikin surrounding and the side of the PV jet facing the walking occupant (See Fig. 7, t = 1.35 s, 1.5 s). Hence, the airflow circulation from the front to the back of the occupant, generated mixing phenomena in the microclimate surrounding the PV jet while the potential core region remained intact (See Fig. 7, t = 1.35 s, 1.5 s). This enhanced the entrainment of contaminants from the space into the jet, which could compromise IAQ in the BZ. After the occupant walked past the PV and moved away from the seated occupant, the end of the movement period was reached after walking 2.5 m. During this time, turbulence intensities decreased to their original values (16%) obtained at the beginning of the walking 11

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Fig. 10. Illustration of the contours of CO2 concentrations at the z = 1.1 m, y = 1.35 m planes for: a) t = 0 s (base case), b) t = 1.5 s and c) t = 2.5 s, d) t = 7 s (manikin at rest).

disturbed the air in the microclimate surrounding the occupant without affecting the values of macroclimate temperature. This can be seen experimentally in Fig. 9, which illustrates the measured and predicted values of velocity at measurement Poles 1 and 2. According to Fig. 9,

original state prior to the disturbance. On the other hand, turbulence intensities dropped to reach their original maximum values of 10% in the PV surrounding prior to the disturbance. Note that during the walking event from 0 s till 2.5 s, the motion 12

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Fig. 11. Illustration of the contours of SF6 concentrations at the z = 1.1 m, y = 1.35 m planes for the: (a) base case (no disturbance), b t = 1.5 s and c) t = 2.5 s, d) t = 7 s (manikin at rest).

the velocities at Poles 1 and 2 started increasing at 0.5 s and 1 s respectively, since the manikin reaches Pole 1 first. The values of velocity increased gradually at the different heights as the occupant approached the measurement locations as seen in Fig. 4(a). For Pole 1, the predicted (measured) velocities reached maximum values of 0.11 m/s (0.12 ± 0.02 m/s), 0.18 m/s (0.19 ± 0.02 m/s), 0.29 m/s (0.27 ± 0.02 m/s) and 0.23 m/s (0.24 ± 0.02 m/s) for heights of z = 0.4 m, 0.8 m, 1.2 m and 1.6 m respectively. Good agreement was found between experimental and predicted values with a maximum relative error of 11.5% obtained at z = 1.2 s at t = 1.4 s. For Pole 2, the predicted (measured) velocities reach maximum of 0.11 m/s (0.12 ± 0.02 m/s), 0.17 m/s (0.18 ± 0.02 m/s), 0.25 m/s

(0.27 ± 0.02 m/s) and 0.22 m/s (0.24 ± 0.02 m/s) for heights of z = 0.4 m, 0.8 m, 1.2 m and 1.6 m respectively. Good agreement was found between experimental and predicted values with a maximum relative error of 11.1% obtained at z = 0.8 s at t = 1.3 s. Smaller peak velocities were found at z = 0.4 m for both poles, since this height was closer to the legs of the manikin which were thinner and closer to the floor. Higher velocities were found for both poles at heights of 1.2 m near the torso segment and in the surrounding of the PV jet where maximum turbulence was found. It can also be seen in Fig. 9 that after the manikin crossed the measuring locations at Poles 1 and 2, the velocities at all heights decreased and reached the background velocities once more. 13

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Fig. 12. Illustration of the temporal variation of the ventilation effectiveness: (a) εv, CO2 (%), (b) εv, SF6 (%) throughout the disturbance period and the base cases.

Fig. 13. Illustration of the contours of velocity, pressure and turbulence intensity (%) for a wider PV jet at t = 1.5 s and 2.5 s.

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Fig. 14. Illustration of the contours of: a) CO2 concentrations, b) SF6 concentrations at t = 1.5 s and 2.5 s.

4.3. Effect of disturbance induced by walking on IAQ

Hence, it considers a space, where CO2, a common type of species found in the air was used to assess IAQ. In the second case, the PV ability is protecting occupants from contaminating species is evaluated. Therefore, the walking occupant is considered as infected and

The PV performance in the presence of a disturbance was assessed in two cases. The first case assesses the PV ability in delivering fresh air.

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was unaffected. (See Fig. 7, t = 0.45 s and Fig. 12(a)). However, in the case of a local source of SF6, εv (%) decreased by 6.31% (See Fig. 12(b)). This was expected since the walking occupant and thus the SF6 contamination source is approaching the PV. Note that while the walking occupant was behind the seated one, εv (%) for SF6 was still higher than the base case value (85%) since the occupant was behind the PV and not in the critical position (half way between seated occupant and PV). When the moving occupant reached the seated occupant and walked close to the seated occupant and the PV, εv rapidly decreased below the base case values for both CO2 and SF6 by 72.8% and 53% respectively (See Fig. 12). This is due to the increased turbulence and mixing in the PV vicinity (See Fig. 7, t = 1.35 s, 1.5 s), which enhanced the entrainment of contaminants from the surrounding macroclimate into the jet (See Figs. 10 and 11, t = 1.5 s). After the occupant walked past the PV, εv (%) continued on decreasing rapidly for both CO2 and SF6 by 60% and 45% respectively (See Fig. 12). This was due to the suction effect created by the negative pressure found at the back of the occupant, which deflected the PV jet core region (See Figs. 8, 10 and 11 at t = 2.5 s). The BZ was no longer under the protection of the PV and was exposed to the contaminated macroclimate. Hence, the deflection of the jet was much more critical to the breathable air quality than the increased mixing in the PV vicinity. Note that the decrease in εv was more substantial in the case of CO2 than SF6. This was expected since in the case of SF6, the PV fresh air was free from SF6 while in the case of CO2, the PV jet already had a concentration of 450 ppm of CO2. At the end of the walking period (2.5 s), the motion stopped and the wake flow started to dissipate and steady state conditions were reached again in the space at 7 s. It can be seen from Fig. 12, that the εv (%) gradually increased to reach 92% (base case value) for CO2 and 96% in the case of SF6 at 7 s (See Figs. 10 and 11 at t = 7 s, Fig. 12). The latter was higher than its value during the base case and at t = 0 s, since the walking occupant is far in front of the PV and generating contaminants away from the jet.

Table 3 Average ventilation effectiveness εv (%) for CO2, SF6 during the base case and critical times for the original and improved PV units. Cases

Base case Critical mixing phase Critical deflection phase

Original PV (d = 10 cm, 4 L/s)

Improved PV (d = 18.5 cm, 15 L/s)

‾εv, CO2 (%)

‾εv, SF6 (%)

‾εv, CO2 (%)

‾εv, SF6 (%)

92 53.5

85 62

92 92

87 87

17.3

35.8

92

95

constituted a local contamination source. In that case, SF6 was used to represent contaminating species due to respiratory activities. For each case, IAQ in the BZ throughout the disturbance period was compared to a base case where disturbances were absent and where the PV can offer uncompromised protection against species. In the case of CO2, the considered base case is at t = 0 s, prior to the disturbance, where the standing occupant is at the initial position behind the PV (See Fig. 3(a)). In the SF6 case, the base case considered the critical position of the contaminating occupant standing midway between the PV and the seated occupant. During the base cases, εv (%) during uncompromised performance of PV reached values of 92% and 85% for CO2 and SF6 respectively. Figs. 10 and 11 illustrate the contours of CO2 and SF6 concentrations respectively for the base case and during the two critical phases (t = 1.5 s and 2.5 s) and towards the end of the disturbance period (t = 7 s). Fig. 12 illustrates the transient variation of the ventilation effectiveness εv (%) for both CO2 and SF6 throughout the disturbance period as well as the corresponding base cases. In the case of CO2, before the walking occupant reached the seated occupant, εv (%) remained at a constant value (92%) and IAQ in the BZ

Fig. 15. Illustration of the velocity contours, pressure contours and turbulence intensity contours in the z = 1.1 m plane for a distance of 85 cm between seated and standing occupant. 16

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4.4. Improved PV design in the presence of flow disturbance induced by walking

5. Conclusion A 3-D CFD model of an office space conditioned by a MV and PV system was developed. The PV of diameter 10 cm supplied fresh air at 4 L/s towards a seated occupant in the room. The model was coupled with a dynamic mesh to simulate the airflow field behavior generated by a walking occupant and its effect on the performance of the PV. It was found that the airflow circulation generated by the motion, increased turbulence and created local mixing effects, which enhanced the entrainment of contaminants from the macroclimate into the PV jet. This deteriorated IAQ in the BZ by 72% in the case of CO2 and 56% in the case of SF6. IAQ further deteriorated after the occupant moved away from the PV. The PV jet was deflected and pulled away into the wake due to negative pressure at the back of the occupant. This exposed the BZ to the polluted macroclimate and deteriorated the breathable air quality drastically by 60% and 45% for CO2 and SF6 respectively. A larger PV nozzle diameter was thus recommended as an improved PV design to avoid any deterioration of PV performance in the presence of indoor disturbances. Moreover, A safe distance of 85 cm between the occupants was recommended to preserve high breathable air quality.

To conserve the PV efficiency and provide good IAQ in the BZ, the core region of the PV jet needs to be conserved, such that the BZ is always targeted. One way is to provide more fresh air in order to decrease mixing and entrainment. Moreover, the core region needs to cover a larger surface area of the occupant's face. Hence, even if the deflection phenomena occurs, this does not risk exposing the BZ to the contaminants in the macroclimate. To achieve a wider core region, an enhanced PV design would be that with a larger PV nozzle diameter. Therefore, simulations were repeated on a bigger PV nozzle diameter. A diameter of 18.5 cm was selected (46% larger) [34]. The larger PV supplied a flow rate of 15 L/s in order to assure the same IAQ as the smaller PV during the base cases of both CO2 (εv = 92%) and SF6 (εv = 87%). Fig. 13 shows the contours of velocity, pressure and turbulence intensity (%) at 1.5 s and 2.5 s, while Fig. 14 shows the contours of CO2 and SF6 concentrations at 1.5 s and 2.5 s. During the critical mixing phase, it can be seen that the mixing effects remained due to airflow circulation, as is the case with the original narrower PV jet. However, the increase in turbulence intensities (maximum of 17%) was smaller than the case with the narrower jet (maximum of 21%) (See Fig. 13, t = 1.5 s). The entrainment effects were present for both CO2 and SF6 (See Fig. 14) but were smaller than the case of smaller PV (See Figs. 10 and 11 at t = 1.5 s). It can be seen that the BZ is still fully protected by the PV core region and εv (%) still had high values of 92% and 87% for CO2 and SF6 respectively during this phase as also presented in Table 3. During the critical deflection phase as seen from Fig. 13 at t = 2.5 s, the negative pressure at the back barely deflected the PV jet. The wider jet retained its form and still covered the entirety of the occupant BZ. This was reflected in the IAQ at the occupant BZ as seen from Fig. 14 at 2.5 s. The BZ was fully protected by the PV core region and εv (%) remained at high values of 92% and 95% for CO2 and SF6 respectively during this phase (see Table 3). Thus, a wider PV jet was able to deliver high breathable air quality while still protecting occupants from any risk of infection due to an outside source of contaminants. Furthermore, it is also possible to recommend a safe distance between the seated and moving occupants in the workstation design such that the PV core region is preserved. The distance should be such that the wake flow generated by the walking occupant, is far enough from the PV jet to ensure minimal interaction occurs between the two flows. For this reason, simulations were performed for larger distances (> original distance of 65 cm) separating the occupants. It was found that a minimum safe distance of 85 cm between occupants is needed so as not impede the PV performance and thus breathable air quality. The results are shown in Fig. 15 which illustrates the contours of velocity, turbulence intensity (%), and pressure at z = 1.1 m plane for a distance of 85 cm between occupants. According to Fig. 15, the turbulence intensities in the wake and PV jet surrounding remained equal to their original values of 16% and 10% respectively along the followed trajectory at t = 1.5 s, that. Therefore, there was no increase in mixing and entrainment. Thus, the PV potential core region remained intact and breathable air quality was maintained. Additionally, after the manikin moves away from the PV, there was no deflection of the PV jet. This can be seen in the velocity contours in Fig. 15 at t = 2.5 s. According to the pressure contours shown in Fig. 15, the negative pressure region at the manikin back (−0.3 Pa) was far from the PV jet. Hence, the potential core region still covered the BZ and the breathable air quality was maintained. Consequently, workstations equipped with PV systems can be designed such that a minimal distance of 85 cm is maintained between the seated occupant and any walking occupant who passes nearby.

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