Air quality in rooms conditioned by chilled ceiling and mixed displacement ventilation for energy saving

Energy and Buildings 43 (2011) 2684–2695

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Air quality in rooms conditioned by chilled ceiling and mixed displacement ventilation for energy saving W. Chakroun a,∗ , K. Ghali b , N. Ghaddar b a b

Department of Mechanical Engineering, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait Department of Mechanical Engineering, American University of Beirut, P.O. Box 11-0236, Beirut 1107-2020, Lebanon

a r t i c l e

i n f o

Article history: Received 3 June 2011 Accepted 19 June 2011 Keywords: Contaminant transport in plumes Indoor air quality with mixed return air Chilled ceiling displacement ventilation

a b s t r a c t A transient-contaminant-transport model is developed for assessing IAQ in the breathing zone when introducing return air into rooms conditioned by CC/DV system to save energy. The steady state transport model of [1] is extended to transient conditions while accounting for significant wall plumes associated with external loads. Experiments are performed to validate the extended model predictions of IAQ expressed in the level of CO2 concentration. Experiments are conducted in a chamber with two external walls in Kuwait Climate. Measurements are recorded in time of the air temperature and CO2 concentration at selected locations in the room and compared with values predicted by the model. Experimental results agreed well with model predictions. The maximum errors in predicted CO2 concentrations are less than ±25 ppm in presence of external load. 60% fresh air fraction resulted in 37% less energy consumption compared with 100% fresh air CC/DV system. The validated model is applied to a case study in Kuwait to evaluate energy saving over the cooling season for a typical office space while using mixed DV air. Energy savings of up to 20.6% can be realized using mixed supply air while maintaining IAQ compared with energy used for the 100% fresh air. © 2011 Elsevier B.V. All rights reserved.

1. Introduction The continuing rise in energy demand, costs and the associated environmental problems, notably climate change, is causing increased emphasis on the design of energy efficient airconditioning systems for both industrial and comfort applications. Buildings in Kuwait as an example account for more than 50% of all energy use in the country [2]. In the summer, the HVAC systems represent 70% of peak load. Targeting energy efficiency in HVAC systems to provide thermal comfort and good indoor air quality is a strategic intervention to reduce energy consumption in the building sector [3]. The chilled ceiling displacement ventilation (CC/DV) system is one HVAC system that is known to provide high indoor air quality by introducing 100% fresh supply close to the floor level displacing warm air into exhaust and creating a clean occupied lower zone in the space. The 100% fresh air exceeds the ASHRAE Standard 62.1 [4] minimum acceptable ventilation requirements of the space. The energy consumption cost of conditioning ambient air to appropriate supply condition of the displacement ventilation system is substantial. The mixing of part of the return air with the

∗ Corresponding author. Tel.: +965 24985804; fax: +965 24847131. E-mail address: [email protected] (W. Chakroun). 0378-7788/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.enbuild.2011.06.019

supply air may present an opportunity to reduce overall system energy consumption without violating indoor air quality standard. Several researchers [1,5,6] reported that for a 100% fresh air supply condition of equal indoor air quality and thermal comfort level, the CC/DV system consumed 53% less cooling energy than the conventional system at 100% fresh air system over the cooling season. The initial cost of the CC/DV system was found by [3] to be higher, but the payback period based on transient operation is less than 3 years when used to replace a 100% fresh air conventional system. However, when the 30% fresh air case of the conventional system was compared by the 100% fresh air CC/DV system, the CC/DV system did not offer any advantage in terms of cost over the conventional system. It is clear that competiveness is directly tied with the amount of fresh air used in the CC/DV system which at 100% value exceed the minimum acceptable value for ventilation given that Kuwait climate is characterized by high outdoor temperature that exceeds indoor comfort conditions by 20–30 ◦ C. Thermal comfort is ensured in the CC/DV conditioned space when the vertical temperature gradient in the occupied zone is less than 2.5 ◦ C/m [7]. Many researchers have adopted the “stratification height” in the CC/DV-conditioned spaces to be the measure of the indoor air quality [8–10]. The stratification height is defined as the elevation at which the density gradients disappear in the rising air and its plume spreads horizontally. It is determined from mass

W. Chakroun et al. / Energy and Buildings 43 (2011) 2684–2695

Nomenclature

Dt CC/DV D Hc M N R U T X z

area (m2 ) concentration (ppm) molecular diffusion (m2 /s) effective diffusion coefficient equal to the sum of contaminant coefficient of diffusivity in air and turbulent mixing diffusion coefficients (m2 /s) turbulent diffusivity (m2 /s) chilled ceiling displacement ventilation cylindrical heat source diameter (m) cylindrical heat source height (m) mass flow rate (kg/s) number of layers radial coordinate (m) upward velocity (m/s) temperature (◦ C) return fraction layer elevation from floor level (m)

Greek ˚   

heat source strength (W) temperature (◦ C) viscosity (N s/m2 ) density (kg/m3 )

A C D Deff

Subscripts c contaminant cir circulated air e exhaust ent entrained f floor fr fresh air g generation int interface with the room air k layer number l layer boundary m middle mix mixed return and fresh air p plume w wall plume s supply

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supply air temperature. They concluded that the highest supply air temperature causes the largest concentration in the lower zone. Yamanaka et al. [15,16] developed an interface layer steady state model to predict contaminant distribution in rooms with displacement ventilation accounting for convection flows along walls and in plumes based on semi-empirical correlations of Kofoed [17]. Their model required the prior knowledge of wall and room air temperatures and has empirically determined the interface layer thickness. Suzuki et al. [18] used the interface layer model for prediction of air quality in patient room ventilated by displacement ventilation. Kanaan et al. [1] coupled the wall-plume-multilayer model of Ayoub et al. [19] derived from first principles with a contaminant transport model. Their transport model accounted for contaminant buoyant convection within the plumes and room air layers and contaminant lateral and vertical diffusion inside and outside the plumes of high concentration of contaminant. They assessed indoor air quality at steady state conditions of room load between 40 and 70 W/m2 based on levels of carbon dioxide concentration in the radiant cooled space. Many factors were found the concentration distribution of CO2 within the space including the strength of the heat source, the air supply temperature and supply concentration of the contaminant. In this research, it is aimed to develop a model to predict the indoor air quality in spaces conditioned by CC/DV system when returned air is mixed with fresh air into the supply duct of displacement ventilation system and assess potential advantage of this practice in Kuwait climatic conditions on energy conservation. It is also aimed to investigate the concentration distribution of CO2 during this transient period and to find out the time period over which this transient interval extends. For this purpose, Kanaan et al.’s model [1] has been improved by incorporating the transient transport governing equations for CO2 transport. The main focus on the transient behavior is due to the variation in number of occupants in the space. The extended transient contaminant transport model will be validated by experimentation. The validated model will be applied to a case study of a typical office space in Kuwait climate to assess energy savings over the cooling season when mixed CC/DV system is used in comparison with 100% fresh air system and with mixed conventional system.

2. Methodology 2.1. Mathematical model

balances at the height when air supply flow rate is equal to the thermal plumes total flow rate and it is recommended not be less than a desired minimum, normally the height of a seated person, for acceptable indoor air quality [11,12]. Kanaan et al. [1] illustrated that the thermal stratification height (fresh zone height) at steady conditions does not coincides but may be significantly lower than the clean zone height identified by CO2 concentration level below 700 ppm. It is within that margin that energy saving can be realized by mixing a fraction of the return air with fresh air to bring down the clean zone height to the desired occupancy height. When DV supply air is mixed with some of the return air, it is not clear if sudden increase in fresh air requirements would result in a substantial transient period of high level of CO2 concentration in the space before adjusting to the need. The modeling of the steady transport of carbon dioxide in displacement-ventilated rooms was studied by Mundt [13] who monitored experimentally the interaction of the convection flows governing the air flow and its influence on the contaminant distribution in DV rooms. Xu and Yamanaka [14] examined the effect of heat loss through walls upon the contaminant concentration in room with displacement ventilation, and studied the effect of the

The contaminant transport model of [1] is developed for steady state conditions and is well-suited for applications with non-uniformly heated walls. In this section, transient effects are incorporated into the model due to internal load changes and storage effects in the walls induced by internal and external conditions. The transport model was developed for an inert gas and neglected lateral diffusion of CO2 between room air and wall plumes. In addition, the transport of CO2 by wall plumes entraining room air is included in the model. The space is divided into N adjacent horizontal air layers as shown in Fig. 1. The first m layers are from the floor to the contaminant source height (in Fig. 1, m = 7). The last layer starts at the critical plume height to the ceiling where the critical height is the elevation at which the density gradients disappear and the plume spreads horizontally. The middle layers from are equally divided and have the same height. It is assumed that each air layer volume is lumped at uniform temperature and contaminant concentration. The contaminant is generated at a rate Mg from a heat source of strength ˚. The air layers energy balances, temperature gradient and stratification height are determined by the coupled transient thermal model of Ghali et al. [11].

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The wall plume average upward mass flow rate Mw is calculated as a function of z induced by a wall warmer than air for a known room temperature gradient d/dz using the model of Ayoub et al. [19]. The model is based on a power law distribution of the temperature difference between the wall and the air (z) and is given by (z) = Tw − Ta = Nz n

(3a)

where N and n are constants that depend on the room vertical temperature gradient and supply temperature. The range of exponent n for wall plumes is taken as −0.6 < n < 0 and a typical value for CC/DV system feasible operating range is −0.23 [19]. The wall plume volume flow rate per unit width at any height z is calculated as Mw (z) = 4

Fig. 1. Schematic of the CO2 transport model layers in the space and notations.

The predicted temperature gradient is used in computing the critical height zt of the plume using Mundt [13] correlation given by

 1/4

zt = 0.74˚

d dz

−3/8 (1a)

where d/dz is the vertical temperature gradient of room air in ◦ C/m, ˚ is the point source heat flux in Watt. The time-averaged velocity downstream of the plume is approximated by and exponential profile [20] given by



u = 0.128˚1/3 z −1/3 exp

−96r 2 z2



(1b)

where r is the radial coordinate and z is the vertical coordinate. The source plume average upward mass flow rate Mp as a function of z induced by the heat source ˚ for given a known temperature gradient d/dz can be calculated using Mundt [13] correlation as follows:



Mp = 0.00238  ˚3/4

d dz

−5/8

B1

(2)

where B1 = 0.004 + 0.039A1 + 0.38A21 − 0.062A31



A1 = 2.86z

d dz

3/8

˚−1/4

and  is the air density. In case of extended sources such as the cylinder used in our study, real source is replaced by a virtual point source such that the border of the plume above the point source passes through the upper edge of the real cylindrical source. The parabolic contaminant concentration plume profile Cp (r, z) of Kanaan et al. [1] is adopted. The plume average mass flow rate obtained from Eq. (2) is then equated to the average mass flow rate obtained from the integrated concentration profile over the area to obtain the plume area and radius. The maximum concentration at any time is at the plume center (generation line) and the minimum concentration is at the plume boundaries where it is assumed equal to the concentration of adjacent room air layer. Utilizing the profile symmetry at the center line will enable closing the solution for both air and plume layers’ concentrations using mass balances of air and plume layers.

 Gr 1/4 Z

4

f∞

(3b)

where GrZ is the Grashof number (Grz = (gz3 /2 )ˇ(z)) where g is the gravitational constant, ˇ is the thermal expansion coefficient, and  is the kinematic viscosity. The volume flow rate per unit width of the wall plume Mw is directly related to the “shape” of the temperature distribution along the vertical wall rather than a computed average wall temperature. The shape factor f∞ is calculated as a function of the exponent n [19]. The wall plume contaminant concentration Cw at any height z is assumed to have a second order profile and a similar methodology used by Kanaan et al. [1] is followed making use of the impermeable wall condition ∂Cw /∂y = 0 (y is the normal to wall coordinate), continuity in the value of the concentration at the edge of the air boundary layer next to the wall, the definition of average concentration inside the wall plume, and overall mass balance on the wall plume layer. The plume average mass flow rate obtained from Eq. (3b) is then equated to the average mass flow rated obtained from the integrated concentration profile over the area (wall plume width × boundary layer thickness ıw ). The boundary layer thickness is calculated from the model of Gebhart et al. [21]. The mass balances are performed on each layer. The flow rates resulting from walls and heat sources are denoted by Mw(j,k) and Mp(i,k) respectively. The net circulated mass Mcir,k at each boundary (common surface of two adjacent layers) is calculated as Mcir,k = Ms −

n  i=1

Mp,(i,k) −

4 

Mw,(j,k)

(4)

j=1

where Ms is the supply mass flow rate. In the above equation, k (=1 to N − 1) refers to the number of the air layer and its associated the boundary surfaces, and n is the number of heat sources. The stratification height Hs is defined as the level at which the circulated mass is zero. The wall plumes’ mass flow rates are determined from the wall-plume-multilayer thermal model of Ayoub et al. [19]. The problem formulation is valid for a single or multiple CO2 generating plume source/s or other types of emitted gases associated with heat sources provided that the assumption of laterally lumped air layer outside the plume is valid. This assumption is applicable for displacement ventilated rooms where gradients in concentration, temperature and velocity are small compared to vertical gradients of these variables. Since CO2 concentration levels are easily measured, the gas is recognized by ASHRAE (American Society for Heating, Refrigeration and Air conditioning Engineers) as the surrogate ventilation index. CO2 concentration level in an air conditioned room is a good indicator of occupancy and air quality within a space [4]. The contaminant transport within the plume and at the wall is driven by convective buoyant flows with and lateral diffusion due to concentration gradient at the boundary of the plume with room air. Wall plumes are not emitting sources and the difference

W. Chakroun et al. / Energy and Buildings 43 (2011) 2684–2695

in contaminant concentration in room air and the wall plume is due to the rising warmer plume entraining cooler room air and for the hot climate of Kuwait, the downward wall plumes are neglected. The transient contaminant mass balance of the air layers from 1 to m − 1 is given by

 M  s

∂C1 = ∂t

Layer 1 :

m−1



Cs + Af D

⎛

− ⎝Mcir,1 +

h 



∂C ∂z

1

⎞

(5a)

layers 2 to m − 1 :

∂Ck = ∂t

Ms m−1



 Cs + Af D

Mcir,k−1 +

−

Mcir,k +



∂C ∂z

 − k



h 

Mw,(j,k−1) j=1

∂C ∂z

+ Aw Dc

Ck−1



h 

(5b)

Ck

Mw,(j,k)

where Cs is the contaminant concentration of the supply air, Af is the floor area, D is the molecular diffusion coefficient,  is the air density, and Ck is the average concentration in the air layer k (k = 1 to m − 1). The supply flow rate is assumed to be distributed equally into the first m − 1 layers. The transient contaminant mass balances for middle layers k = m to N − 1 are performed for three regions, the first is for the room air, the second is in the source plume region, and the third in the wall plume region. The mass balance for the room air is given by

Mcir,k−1 +



h 

 Ck−1 + Ak D

Mw,(j,k−1) j=1

− Ment,k Ck −

Mcir,k +

∂C ∂z

 − Aint,k Deff c k



h 

Mw,(j,k−1)

 Ck − Ak−1 D

∂C ∂z

 

∂Cp  ∂r

zmk

 k−1

j=1

−

4 

Ment,wj,k Ck −

4 

j=1

Aint,w,k Deff c

 

∂Cwj  ∂y

(6) y=ıw

j=1

In the above equation, the first term on the left side is the convective term associated with air transport, the second term is diffusion between layers k − 1 and k, and the third term is diffusion between the plume and adjacent air layer. The subscript k refers to the air layer number, Aint,k is the interface area between the plume and air layer k, c is the air-contaminant mixture density, Cp is the contaminant concentration inside the plume, Ak is the floor area minus the area of the plume at the boundary, and Ment,k is the mass entrained by the plumes from the adjacent air layer k and r is the radial coordinate from the plume centerline. Since thermal plumes are free-boundary flows, they are turbulent at their boundaries and in the upper layers of the plume. The diffusive term at plume boundaries is augmented by adding turbulent diffusion coefficient. The average effective diffusion coefficient is taken as 0.4496 × 10−3 m2 /s for a heat load of 100 W [1]. The transient contaminant mass balance inside the plume for air layer k (=m to N − 1) is given by ∂Cp,k ∂t

= Mp,k−1 Cpk−1 + Ment,k Ck + Mg × 106 (for k = m)

 − Ap,k−1 Dc

∂Cp ∂z



 k−1

 

k−1

∂Cp  + Aint,k Deff c ∂r 

zmk

 − Mp,k Cpk

(7a)

k

= Mw,k−1 Cwk−1 − Mw,k Cw,k + Ment,wj,k Ck



j=1

∂Ck = ∂t

∂t

− Aw,k−1 Dc

+

∂Cp ∂z

where Mpk and Apk are the mass flow rate and area of the plume at the boundary respectively and Mg is the contaminant mass generation rate. Similarly, the transient contaminant mass balance inside the wall plume for air layer k is given by for each wall j (=1–4) as follows: ∂Cwj,k

Mw,(j,1) ⎠ C1

j=1



 + Apk Dc

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∂Cwj

∂Cwj ∂z

∂z





  + Aint,wk Deff c ∂y  k−1 z,ı ∂Cwj 

w

(7b) k

A zero vertical concentration gradient at the interface of the N − 1 layer with exhaust flow concentration at the Nth layer is imposed. The concentration is assumed to reach will mixed saturation level at the exhaust concentration of layer N. When mixing with return air, the supply air contaminant concentration is defined by Cs = xCe + (1 − x)Cfr

(8)

where x is the return air fraction in the supply air and Cfr is the fresh air concentration. The mass balance equations for the air layer, plumes and wall plumes represent a set of first-order coupled partial differential equations. The equations are solved using first order Euler forward method for temporal integration and finite difference central differencing for spatial discretization to predict the transient room air and plume average concentrations at different heights and the coefficients of the concentration profiles inside the plume and at the walls. The details of the numerical discretization are not provided here since they are detailed in Ref. [1]. However, at each time step the mass balance equations for given outdoor, supply and ceiling conditions are solved iteratively by back substitution for to find the unknowns: C1 to CN and Cpm to CpN and the solution is march forward in time over the period of interest. The developed model accounted for the convection through layers, the diffusion between layers, plumes and room air and it will be validated by experimentation. 2.2. Experiments The test facility conditioned space by a CC/DV system has a length of 4.59 m, a width of 4.95 m and height of 3.80 m with the chilled ceiling suspended at a height of 2.8 m. The schematic diagram of CC/DV room is shown in Fig. 2(a). On the North wall, the air supply diffuser is located 0.1 m above the floor. It has a length of 2.45 m and a height of 0.5 m. The air exhaust slot (1.5 m × 0.45 m) is just below the chilled ceiling and extends vertically 0.45 m. The chilled ceiling panel that covers 80% of the ceiling is suspended from the roof and is well insulated from the back side to prevent conduction through the roof. The temperature is measured using T-type shielded thermocouples (accuracy of ±0.5 ◦ C). The data acquisition system of type OMB-Multiscan-1200 is capable of recording data every second but data is saved every minute. Thermocouples are mounted on the North, East, South and West walls. Thermocouples are also mounted on the chilled ceiling and three external thermocouples are mounted outside the window to measure the ambient temperature. The external thermocouples are ensured to measure the ambient temperature by using shades that enclose the thermocouples from the top and the sides. These shades have double sheet metal plates separated by insulation. Rods with either

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thermocouples or CO2 sensors hanged upon them are used to measure temperature and CO2 concentration in ppm respectively. Four rods have thermocouples mounted upon them and the separation between each consecutive thermocouple is 0.1 m. Two small rods (of height 0.6 m) are used to measure the temperature of the grill. Two long rods of height 2.6 m are placed 0.82 m from the south wall. A long CO2 rod is put between these two rods at a perpendicular

distance of 1.2 m from the nearest chair. Two small rods are put in front of the inlet grill where the CO2 probes are mounted to measure CO2 before an experiment starts and after it ends. Air velocities in front of the supply grill are measured using a 1-D anemometer of type Extech 407412. The chilled water that supplies the CC/DV system is supplied from Carrier chiller unit of the building. The main pipe is supplying

Fig. 2. Schematic diagrams for (a) CC/DV room and (b) CC/DV facility.

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Table 1 Relevant experimental parameters. Experimental parameters

Date Initial time interval with one person present Intermediate time interval with three persons present Final time interval with one person present Supply air flow rate, kg/s Supply air temperature, ◦ C Chilled ceiling temperature, ◦ C Outdoor average air temperature, ◦ C CO2 concentration in supply air, ppm Return fraction air, % Total sensible load per unit area, W/m2

Experiment number I

II

III

September 21 10:30–11:30 11:30–13:00 13:00–14:30 0.2 20 16 40.4 422 0 52.0

September 22 10:30–11:30 11:30–13:00 13:00–14:30 0.2 20 16 41.8 423 20% 52.0

September 26 10:30–11:30 11:30–13:00 13:00–14:30 0.2 20 16 38.9 430 40% 52.0

the ceiling panels by chilled water while a branched pipe is going to the fan coil unit (FCU) to cool the supply air that enters from the supply grill. Two three-way valves exist, one for the chilled ceiling (CC) and one for the FCU and they are connected to thermostats. The flow rate of the chilled water is measured using ultrasonic flow mater. Thermometers that are mounted on the pipes are used to monitor the temperature of the inlet and outlet chilled water into and out off the chilled ceiling as well as into and out off the fan coil unit (FCU). Fresh outside air is inserted to the system via a variable speed inlet fan. Air flow rate is controlled using a main variable control damper (VCD) and the variable speed motor of the FCU. By trial and error five VCD’s are used to ensure that the air speed is uniform throughout the inlet grill. An outlet fan takes the air out of the room. It is identical to the inlet fan. Fig. 2(b) shows the schematic diagram of the facility. As mentioned earlier, the T-type thermocouples have an error of ±0.5 ◦ C. The data acquisition system of type OMB-Multiscan-1200 is capable of recording data every second but averaged data is saved every minute. Thermometers used to measure the temperature of the chilled water are of accuracy of ±0.1 ◦ C. The Telaire 7001 sensors used to measure the CO2 concentration in ppm have resolution of ±1 ppm, response time of less than 1 min, and accuracy of ±50 ppm or 5% of the reading whichever is higher. Each Telaire 7001 sensor is connected to onset 2077 data logging kit with an accuracy of ±10 mV ± 1% of the reading. Each 1 mV is equivalent to 1 ppm of CO2 . The air velocity is measured using Extech’s Hygro-ThermoAnemometer model 407412 with a resolution of 0.01 m/s and an error of ±2%. The same instrument is used to measure the relative humidity with an error of ±3% for RH from 10% to 70% and an error of ±4% for RH from 70% to 95%. An air flow meter manufactured by Fluke model 922 with a resolution of 1 L/s is used to measure the volumetric flow rate of air in the duct. The accuracy in velocity is ±2.5% at 10 m/s. Experiments were performed over three consecutive transient periods. For every period of emission, the measurements are continuously recorded by the CO2 sensors mounted at different heights on the columns placed at different distances from the CO2 source. The concentration at a certain height and at any moment is calculated as the average of the readings at this height on three different positions at that moment. The relevant parameters of the conducted experiments at different intervals and return air fractions are provided in Table 1. As mentioned earlier, the T-type thermocouples have an error of ±0.5 ◦ C. The data acquisition system of type OMB-Multiscan-1200 is capable of recording data every second but averaged data is saved every minute. Thermometers used to measure the temperature of the chilled water are of accuracy of ±0.1 ◦ C. The Telaire 7001 sensors used to measure the CO2 concentration in ppm have resolution of ±1 ppm, response time of less than 1 min, and accuracy of ±50 ppm

or 5% of the reading whichever is higher. Each Telaire 7001 sensor is connected to onset 2077 data logging kit with an accuracy of ±10 mV ± 1% of the reading. Each 1 mV is equivalent to 1 ppm of CO2 . The air velocity is measured using Extech’s Hygro-ThermoAnemometer model 407412 with a resolution of 0.01 m/s and an error of ±2%. The same instrument is used to measure the relative humidity with an error of ±3% for RH from 10% to 70% and an error of ±4% for RH from 70% to 95%. An air flow meter manufactured by Fluke model 922 with a resolution of 1 L/s is used to measure the volumetric flow rate of air in the duct. The accuracy is a function of velocity and duct size. The accuracy in velocity is ±2.5% at 10 m/s. An ultra-sonic device of type Shenitech model STUF-200H with ±1% accuracy at velocity above 0.18 m/s, was used to measure the chilled water flow rate through the pipes. A power meter of manufactured by fluke model ANALYST 2050-2060 with an accuracy of 2.5% reading ± 5 digits was used to measure the power consumption of the FCU motor and the inlet and outlet fans. Experiments were performed over three consecutive transient periods. For every period of emission, the measurements are continuously recorded by the CO2 sensors mounted at different heights on the columns placed at different distances from the CO2 source. The concentration at a certain height and at any moment is calculated as the average of the readings at this height on three different positions at that moment. A variation up to 25 ppm was experienced from the average concentration based on the location from the source. Since CO2 transport model lumps room air layer outside the plume layers into one concentration, this variation is insignificant. The vertical concentration gradient of CO2 is substantial greater than lateral gradients in the air layers due mainly to the upward convective air flows. The relevant parameters of the conducted experiments at different intervals and return air fractions are provided in Table 1. 2.3. Case study for Kuwait climate and CC/DV system energy consumption The developed model is applied on a case study for Kuwait climate to ensure meeting the requirement of good air quality in a CC/DV-conditioned space and the effect of using mixed air in DV system on energy savings over the cooling season (May–October) is evaluated. The system economic feasibility will be also compared with conventional systems for the Kuwaiti climate. The hourly direct and diffuse solar radiation incident on the walls and the values of ambient temperature are derived directly from hourly weather data files of Kuwait for a typical day of each month of the season when cooling is needed [22]. The test case considered for the study is a 5 m × 5 m × 3 m space. The ceiling and floor are considered internal partitions. The south and west walls are external. It is assumed that all the walls have the same construction with

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Fig. 3. Case study people schedule.

an overall heat transfer coefficient U of 0.57 W/m2 K. The chilled ceiling covers 80% of the area. The maximum number of occupants is six. The space peak load is 80 W/m2 . The space latent peak load portion is 16 W/m2 due to people and ventilation requirements of 7 l/s/person [4]. The occupant schedule is shown in Fig. 3. The system sizing and design will follow the procedure outlined by Keblawi et al. [10] using the CC/DV design charts to set the supply flow rate, and inlet conditions based on the peak load and then deduce the ratio of cooling load removed by the chilled ceiling to the total cooling load removed from the space by both the chilled ceiling and the DV system. The system peak cooling load at 100% fresh air by the DV system is 4.85 kW of which 1.45 kW is latent load removed by the cooling coil, and 1.53 kW sensible load removed by the DV supply cooling coil and 1.87 kW removed by the chilled ceiling. Once the various components of the CC/DV system shown in Fig. 2(b) are sized, simulation models for each system component will be used to determine the total power consumption by the chiller, fans, and pump in addition to reheat power if needed. The chiller load is the sum of the chilled ceiling load and the cooling coil load. The chilled ceiling load is determined from the space model [23]. The simulation models of the system component will use published models reported in Keblawi et al. work [10,24] where the performance of the chiller is determined from the part load ratio of the chiller, the fan power is assumed to vary as a cubic function of the volumetric airflow rate based on the reference energy consumption, and the cooling coil operation model is adopted from the quasi-static lumped coil model of Braun et al. [25] to predict outlet air temperature and humidity conditions for known air inlet conditions. The model of Keblawi et al. [10,24] has been applied and tested in previous applications by the authors [3,9,23]. The simulation of the component models are integrated with the transient CC/DV space model of Ayoub et al. [19] to predict the associated stratification height and vertical temperature gradient (needed as input to the current transport model) in the space associated with set operational conditions of supply air and chilled ceiling temperature for given room instantaneous load. The simulation models of the CC/DV system operation will be implemented using an optimized control strategy for minimum energy consumption while maintaining thermal comfort conditions and acceptable indoor air quality. The control strategy adopted for the case study uses the optimized strategy of [23] for setting the operational parameters of both the CC and DV systems (supply flow rate and supply air temperature, and chilled ceiling temperatures) at 100% fresh air for DV supply air to the space. The methodology outlined by Ghaddar et al. [23] determines the optimal set points of the system during transient operation for the case

of 100% fresh air while maintaining thermal comfort conditions and ensuring a stratification height of the fresh air region higher than 1.1 m and condensation does not occur on the chilled ceiling. The latent load is controlled by the supply air humidity ratio that is adjusted to meet desired load and also prevent condensation on the ceiling. The maximum acceptable humidity ratio of supply air, ws,max , depends on the dew point of the chilled ceiling, the supply flow rate and temperature, and the number of people in the space. The maximum allowable humidity ratio of the air adjacent to the ceiling wa,max is evaluated at dew point Tdp,min equal to the ceiling temperature less 1.5 ◦ C (Tdp,min = Tc − 1.5) to prevent condensation on the ceiling [24]. The methodology for determining optimal set points is described in detail in the published work of the authors and is not repeated here [23,24]. The 100% fresh air exceeds the ASHRAE Standard 62.1 [4] minimum acceptable ventilation requirements of the space. The energy consumption cost of conditioning ambient air to appropriate supply condition of the displacement ventilation system is substantial given that Kuwait climate is characterized by high outdoor temperature that exceeds indoor comfort conditions by 20–30 ◦ C. The possibility of mixing part of the return air with the supply air is an opportunity to reduce overall system energy consumption without violating indoor air quality standard [4]. The developed transport model for CO2 will be used to determine the maximum hourly mixing ratio of return air with fresh air such that the CO2 concentration is less or equal to 700 ppm at a height z = 1.3 m. The reduction on the load on the DV system cooling coil and the effect of this reduction on power consumed by the chiller will be determined. In estimating the optimal mixing fraction of the fresh and recycled air, the system set points of supply air conditions and chilled ceiling temperature will use the same optimal set points determined for the case of 100% fresh air. This reduction on the cooling coil load can then easily be determined where the inlet conditions to the cooling coil will be the mixed air temperature and humidity using the following simple energy and mass balance for the air stream to the coil inlet: Tmix = xTe + (1 − x)T∞

(9a)

wmix = xwe + (1 − x)w∞

(9b)

where Tmix and wmix are the temperature and humidity of mixed air before passing through the cooling coil of the DV system. The supply humidity ratio in the simulation of the operation of the system has been always checked to be within constraints as to not cause condensation on the chilled ceiling. The dry climate in Kuwait has not caused any change in the reheat power of the model when that power was above zero. 3. Results and discussion 3.1. Model validation results The model and experimental results on contaminant concentration distribution in a conditioned space by chilled ceiling and displacement ventilation in presence of transient human subject thermal and contaminant generation load. The effect of supply concentration on indoor environmental quality in the clean zone is assessed using mixing fresh air ratios of the total supply air of 100%, 80%, and 60%. The experimental room conditioned by the CC/DV system described in the experimental section was simulated for experiments I, II and III described in Table 1 using our modified contaminant transport model described earlier. Transient simulations of three time interval (one person, three persons, and then one person present in the room) were performed using 21 layers (N = 21), of which 7 layers were below the CO2 generation level. The

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Fig. 4. The variation with time of the experimentally measured and numerically predicted room air CO2 concentration at person breathing height of 1 m for (a) the conditions of experiment I at 100% supply of fresh air, (b) experiment II at 80% fresh air fraction, and (c) experiment III at 60% fresh air fraction.

Fig. 5. (a–c) Concentration variation with height for the three experimental intervals at 100% fresh air fraction.

N = 21 produced grid independent results and the error in numerical results was less than ±2 ppm when compared to N = 28 and 35. Fig. 4(a)–(c) shows the variation with time of the experimentally measured and numerically predicted room air CO2 concentration at person breathing height of 1 m for (a) the conditions of experiment I at 100% supply of fresh air, (b) experiment II at 80% fresh air fraction, and (c) experiment III at 60% fresh air fraction. At the height of 1 m, CO2 concentration changes rapidly when the 1st person enters the room and reaches steady state at 460 ppm. When two more people enter the room, CO2 increases rapidly since this is the breathing zone and fast response is anticipated (the nose highest is from 1.05 to 1.2 m). It is noticed that the CO2 concentration over shoots for a brief period when compared to the 1 person interval. The concentration keeps on decreasing until it reaches 510 ppm at the end of the interval. In the third interval – as two people leave the room – the CO2 level decreases rapidly (during about 12 min’s) in a linear manner to reach 465 ppm which is just 5 ppm higher than the steady state level in the first interval. This can be explained due to the difference in the thermal conditions between first and last intervals caused by slightly higher external load due to increase in outdoor temperature. The transient period when sudden change in generation takes place is composed of two transients; the first is an overshoot for 1–2 min when two people enter followed by a measured descending transient due to forced circulation of the DV system. The mathematical model does predict the descending transient concentration well with errors less than 10 ppm, but does not capture the overshoot in concentration of the first and second minute due to the nature of the lumped air analysis that does

not provide local transient at the points where measurements are taken. The external load transients have little effect on the overshoot transient because the change in the inner surface temperature of the walls will vary very little over a period of few minutes, but it does affect the vertical gradient over longer time interval. A similar behavior of CO2 concentration in the case of 80% fresh air of supply air is observed but with higher concentration of about 10–20 ppm when three people are in the room compared to the 100% fresh air case. It seems that the response in the first interval is slower than that in the 100% fresh air case and this behavior is repeated in the 60% case as shown in Fig. 4(c) at 1 m and in the first interval the response looks slow. The transient region starts to deviate from the linear behavior a little especially when two people leave the room and the transition takes 1 or 2 min longer. In the case of 60% fresh air, the CO2 concentration reaches steady state values more rapidly due to high level of CO2 (3 people + 40% return air), the difference in CO2 concentrations between the supply air and the room air is therefore smaller. Experimentally, for fixed operating conditions (air flow rate, chilled ceiling temperature, and supply air temperature) we can detect the effect of transients mostly at the 1 m height. The over shoot in CO2 concentration takes place in the 100% fresh air, it lasts for 2 min and the system stabilizes for steady supply conditions of CO2 . For the 80% less overshoot occur than the 100% fresh air case and the transients take few minutes. The contaminant transport model agreed also well with experimental measurements at error less than 10 ppm during the stabilizing transient until steady con-

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Fig. 7. (a–c) Concentration variation with height for the three experimental intervals at 60% fresh air fraction. Fig. 6. (a–c) Concentration variation with height for the three experimental intervals at 80% fresh air fraction.

3.2. Case study and energy savings

ditions are reached, but the model as mentioned before does not capture the overshoot at the moment people enter the space. To clearly see the transient effect induced by the entering and subsequent leaven of two people on CO2 concentration over the transient interval, we show in Figs. 5(a)–(c), 6(a)–(c) and 7(a)–(c) the CO2 concentration vertical distribution during the beginning of each of the three test intervals (t = 2 s, t = 4 min, t = 6 min, and steady state) after the change in occupancy took place for the three experimental cases of 100%, 80%, and 60% fresh air fraction respectively. When we change the amount of recycled air, the CO2 concentration quickly adjusts to the steady state value when compared to the 100% fresh air case. During the period when CO2 builds up in the room, the transient period is longer than the period when CO2 generation is cleared by people leaving the space. Clearing the air happens at faster rate. The air quality in the room determined by CO2 concentration remained acceptable and less than 600 ppm which is way below ASHRAE standard 62.2 [4] of about 1000 ppm even when mixing is present with return air up to 40%. It was of interest to estimate the energy consumption in the three experiments and determine energy savings when mixing is introduced in the DV system. The values of energy consumption for the three experiments performed at Kuwait facilities for 100% fresh air, 80%, and 60% fresh air, were 132.3 W/m2 , 110.7 W/m2 , 83.6 W/m2 respectively. A saving of 16.4% for the 80% fresh air and 37% for the 60% fresh air is achieved compared to the 100% fresh air experiment. This realized saving in energy is high in these experiments because the number of people did not exceed three allowing a high perecnetage of recycled air.

The transient simulation of the CC/DV system operated under optimized control strategy at 100% fresh air is performed using the plume-multi-layer model of Ayoub et al. [19] and optimization tool of Ghaddar et al. [23] for the case study space to calculate the system cooling energy demand and power consumption from May to October in Kuwait climate. Fig. 8(a)–(c) demonstrates for the month of August for the 100% fresh air case of (a) the optimized hourly set points of supply air and chilled ceiling temperatures and the ambient temperature, (b) the optimized hourly set supply flow rate and (c) the system hourly chiller load in kW and the associated ambient temperature. Using the constraint that the CO2 concentration is less than or equal to 700 ppm at 1.3 m height, the optimal mixing ratios of recycled air and the associated power consumption is calculated for each of the months. Table 2 presents the hourly values of the optimal return mixing ratio, the cooling chiller load for mixed air case and % savings in chiller load over the 100% fresh air case of the CC/DV system for months from May to October. The optimal return fraction was lowest ranging 7–8% at peak load hours associated with occupancy while it reached 40% at hours of low occupancy. Fig. 9(a) shows the daily total cooling power consumption for the months from May till October when optimal mixing ratio of return air is implemented. Fig. 9(b) shows the percentage saving in system chiller load using mixed air over the 100% fresh air. If a mean coefficient of performance of 3 is used for the chiller, then the electrical power consumption is way below the standard code of practice in Kuwait which requires that the maximum electrical energy consumption for air conditioning space not to exceed 70 W/m2 [26]. The maximum chiller load occurred in the month of August with a corresponding saving of 19.5% in the cooling load on the chiller

Table 2 The hourly values of the optimal return mixing ratio, the cooling chiller load for mixed air case and percentage savings in chiller load over the 100% case for months from May to October. Hour

May

June

July

Chiller load with mixing return air (kW)

Saving in chiller load (%)

Return ratio

Chiller load with mixing return air (kW)

Saving in chiller load (%)

Return ratio

Chiller load with mixing return air (kW)

Saving in chiller load (%)

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

0.40 0.40 0.26 0.14 0.15 0.15 0.07 0.08 0.08 0.32 0.26 0.32 0.26 0.32 0.40

1.38 1.36 2.70 1.94 2.32 1.91 2.38 2.38 2.64 2.30 2.31 2.37 2.54 1.90 1.94

17.98 16.40 14.38 10.05 12.07 11.77 11.58 11.09 11.29 18.95 19.48 22.93 14.07 20.55 20.07

0.40 0.40 0.26 0.14 0.15 0.15 0.07 0.08 0.08 0.32 0.26 0.32 0.26 0.32 0.40

1.77 1.65 1.70 2.08 2.21 2.33 2.53 2.69 2.74 2.15 1.85 1.65 1.83 1.34 1.21

22.12 21.91 19.52 13.74 15.83 15.31 13.20 12.64 12.55 21.11 21.20 24.39 14.61 21.35 20.28

0.40 0.40 0.26 0.14 0.15 0.15 0.07 0.08 0.08 0.32 0.26 0.32 0.26 0.32 0.40

1.79 1.70 1.77 2.12 2.25 2.38 2.69 2.96 3.05 2.28 1.98 1.68 2.02 1.44 1.31

22.73 23.60 21.43 14.35 16.50 15.96 15.01 15.35 15.62 26.40 27.35 31.66 18.68 27.46 24.93

Hour

August

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

September

October

Return ratio

Chiller load with mixing return air (kW)

Saving in chiller load (%)

Return ratio

Chiller load with mixing return air (kW)

Saving in chiller load (%)

Return ratio

Chiller load with mixing return air (kW)

Saving in chiller load (%)

0.40 0.40 0.26 0.14 0.15 0.15 0.07 0.08 0.08 0.32 0.26 0.32 0.26 0.32 0.40

1.48 1.55 3.26 2.42 2.77 2.16 2.54 2.54 2.76 2.35 2.32 2.32 2.49 1.84 1.85

23.15 22.92 21.32 15.00 17.28 15.72 14.42 13.80 13.70 23.05 23.15 26.63 15.95 23.31 22.14

0.40 0.40 0.26 0.14 0.15 0.15 0.07 0.08 0.08 0.32 0.26 0.32 0.26 0.32 0.40

1.39 1.39 2.64 1.90 2.22 1.86 2.32 2.36 2.56 2.30 2.26 2.30 2.36 1.78 1.87

19.00 18.05 16.96 11.79 13.73 13.69 13.45 13.18 13.09 20.53 20.63 23.03 15.59 21.79 20.88

– 0.40 0.26 0.14 0.15 0.15 0.07 0.08 0.08 0.32 0.26 0.32 0.26 0.32 0.40

– 1.21 1.28 1.48 1.57 1.75 1.98 2.21 2.28 1.81 1.56 1.32 1.51 1.11 1.00

– 15.18 14.30 9.47 10.79 11.08 10.51 10.85 11.03 19.02 19.55 21.78 13.00 18.93 17.40

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Return ratio

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while the maximum energy saving occurred in July reaching 20.6%. The energy saving is strong function of outdoor air temperature which reaches values above 50 ◦ C in July and August. However, in the milder months the savings ranged between 14% and 17%. In previous work of Bahman et al. [3] it was shown that a 100% fresh air CC/DV system consumes 50% less energy than conventional system at 100% fresh air, but has slightly higher energy consumption when compared with the performance of the 30% fresh air conventional system and the CC/DV system did not offer any advantage in terms of cost over the conventional system. The use of mixed air in the CC/DV system has reduced the consumption by 15–20% which would make it attractive for energy savings with high indoor air quality. The overall quality of the air in the occupied zone determined by the CO2 concentration remained below 700 ppm which is still below the maximum permitted concentration by ASHRAE [4]. In this work, the contaminant transport model predicts the vertical CO2 concentration in the CC/DV conditioned space for given supply air CO2 concentration and hence the maximum fraction of mixing that we can use in supply air can be determined while meeting indoor air quality for minimum energy consumption. Note that the developed model relies on diffusion and convective transport of CO2 which would change significantly the CO2 distribution in the room when the supply concentration and supply air flow rate changes. 4. Conclusions

Fig. 8. Plots for the month of August for the 100% fresh air case of (a) the optimized hourly set points of supply air and chilled ceiling temperatures and the ambient temperature, (b) the optimized hourly set supply flow rate and (c) the system hourly chiller load in kW and the associated ambient temperature.

A transient coupled thermal and contaminant transport model has been developed to assess the indoor air quality based on predicted levels of carbon dioxide concentrations in the radiant-cooled space under varying load conditions. The model was validated by conducting experiments in a facility in Kuwait at mixing ratios of recirculated air of 0%, 20%, and 40%. Each experiment is divided to three intervals where in the first interval one person is present in the room then two more people enter at the beginning of the second interval and they leave at the beginning of the third interval. The developed model incorporated the effect of external load on associated wall plumes. The experimental results agreed well with the transport model predictions in terms of concentration. The energy consumption of the combined chilled ceiling and displacement ventilation is found to be substantially less when mixing is present. A mixing fraction of 60% resulted in 37% less measured energy consumption of the system when compared with 100% fresh air CC/DV system energy consumption. The performance of the CC/DV system has been applied to a test office space in Kuwait and the energy consumption of the system is evaluated over the cooling season from May till October and results are compared with the conventional system energy consumption at the same indoor air quality level. The energy consumption of mixed air system was less by 15–20% from the conventional system. Acknowledgment The authors express their appreciation to Kuwait University Research Administration for financially supporting this work through a university research grant (RE 01/08). References

Fig. 9. Plots for the months May till October of (a) the daily total cooling power consumption when optimal mixing ratio of return air is implemented and (b) the percentage saving in chiller load using mixed air over the 100% fresh air.

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