Printer Friendly

CFD Analysis of Dispersion of Particles and Gasses in Buildings for Optimal IAQ Sensor Positioning.

INTRODUCTION

In an occupied space, it is necessary to dilute the indoor pollutant concentrations by introducing adequate quantity of fresh air (Daisey et al. 2003). However, the energy consumption for providing conditioned outdoor fresh air is significant (Emmerich et al. 1998). Accordingly, achieving a proper balance between indoor air quality (IAQ) and energy saving is important.

Demand-controlled ventilation (DCV) is a control strategy of the ventilation rate to ensure the acceptable IAQ with maximized energy conservation for buildings which is increasingly used and studied for many possible application cases (Kusuda T 1976; Nielsen et al. 2010). In a DCV system, the supply airflow rate can be determined indirectly by monitoring the parameters which are able to indicate IAQ including gas concentration and particle concentration. For example, the C[O.sub.2]-based DCV strategy is most commonly examined and applied in many studies since C[O.sub.2] is proven to be a reliable indicator of IAQ by work of Persily (1997) (Krarti et al. 2004; Nassif et al. 2005; Sun et al. 2011).

To achieve the maximum efficiency of DCV, it is crucial to accurately estimate the IAQ parameter (e.g. C[O.sub.2] concentration) which requires the optimal positioning of IAQ sensors. Many studies have been focused on the relationship between the IAQ sensor positioning and the accuracy of measurement. Bulinska et al. (2014) conducted an experimentally validated numerical simulation and found that the positioning of the C[O.sub.2] sensors is crucial and the representative measurements of CO2 concentration can be successfully done in the center of a typical bedroom. However, the research of optimal sensor placement for particle case is somewhat limited and few studies examined the impact of different parameters on the pollutants distribution and optimal sensor positioning in the room.

Based on this background, this paper attempts to employ the experimentally validated Computational Fluid Dynamic (CFD) simulation to analyze the spatial distribution of gases and particles in building to determine the optimal IAQ sensor positioning. This numerical simulation is conducted under different conditions to investigate the effect of following three parameters on the pollutant distribution and sensor reading: 1) diffuser arrangement, 2) source location and 3) pollutant characteristics. In addition, the present study also compares the calculated pollutant concentrations within the breathing zone and at the exhaust to explore the performance of the IAQ sensor placed at exhaust.

METHODS

Validation of CFD model

Experimental set up. This study applied experimentally validated Computational Fluid Dynamic (CFD) methods to investigate gases concentration distribution and particles transport. Experimental measurements were conducted in a 4.27 m x 4.27 m x 3 m [14 ft x 14 ft x 9.84 ft] (length x width x height) chamber with one manikin (85 W [290 Btu/h]), one computer (134 W [457 Btu/h]) and two lights (190 W [648 Btu/h] of each) which totally generated 409 W [1395 Btu/h] heat load. The fresh air was supplied at 18 [degrees]C [64.4 [degrees]F] and 0.075 m/s [14.76 fpm] using a 1.215 m x 0.615 m [3.99 ft x 2.02 ft] displacement ventilation diffuser. The only source of carbon dioxide in the chamber during the experiment was a C[O.sub.2] supply tube near the manikin face with 0.0045 m [0.18 inch] inside diameter and 7.05E-06 [m.sup.3]/s [0.015 cfm] release rate.

Temperature were measured at the center of the chamber. At this measurement location, five temperature sensors were placed vertically at different heights (0.6 m, 1.1 m, 1.7 m, 2.2 m, and 2.6 m) [1.97 ft, 3.60 ft, 5.58 ft, 7.22 ft, and 8.54 ft]. Three [O.sub.2] sensors were placed 1) at the center of the chamber at 0.6 m [1.97 ft], 2) on the manikin at 0.6 m [1.97 ft] and 3) at the exhaust to measure the C[O.sub.2] concentration distribution. Figure 1 shows the chamber layout and sensors placement.

CFD model. A three dimensional CFD model of the chamber was built for the purpose of numerical simulation. The geometry was based on the dimensions and layout of the chamber showed in Figure 1 as well as the initial conditions and boundary conditions were provided by the experiments. Parameters for the CFD initial and boundary conditions such as: diffuser inlet velocity, surface temperature, C[O.sub.2] supply velocity, initial C[O.sub.2] concentration and inlet C[O.sub.2] concentration were experimentally measured in the chamber and are listed in Table 1.

All the simulations were carried out using CFD software STAR-CCM+ (12.02.011). The Reynolds Averaged Navier-Stokes equations were employed with the two-equation Realizable k-[epsilon] model. Temperature, velocity and age-of-air fields were calculated for steady-state boundary conditions. Multi-Component Gas steady model was used to calculate the C[O.sub.2] concentration distribution.

Validation of the CFD simulation was conducted by comparing the parameters from measurements with those from the simulations including temperatures at 5 measuring points and C[O.sub.2] concentrations at 3 measuring points in the testing chamber (Figure 1).

Simulation matrix for the study

After the completion of the validation process, the successfully validated CFD model were modified to simulate a larger variety of indoor environment scenarios. The ventilation strategy, source location and pollutant characteristics were changed from the CFD model used in validation in this parametric analysis.

Three ventilation strategies. Many studies showed that different ventilation strategies result in totally different flow patterns and particle trajectories (Rim et al. 2010; Ning et al. 2016). In present study, the gases and particles dispersion in three representative ventilation rooms: 1) room with a ceiling diffuser; 2) room with a side wall slot diffuser with height of 1.5 m [4.92 ft] and 3) room with a displacement ventilation diffuser at floor level were compared to examine the impact of the diffuser arrangement on the pollutant distribution. Figure 2 shows these three different diffuser arrangements.

Three pollutant characteristics. For each of the cases with different ventilation strategies, airflow and pollutant distribution were analyzed taking into account different pollutant characteristics. In each airflow condition, the concentration profiles of C[O.sub.2], ozone and particle were calculated. The ozone and particles were supplied at the same location as the C[O.sub.2] source to eliminate the impact of the source location.

For ozone case, the ozone inlet with 0.0081 [m.sup.2] [0.087 [ft.sup.2]] area, 0.4 m/s [78.74 fpm] airflow velocity and 100 ppb ozone emission concentration simulated an indoor air cleaner as an ozone generator based on previous study (Jakober et al. 2008). The initial ozone concentration was 30 ppb according to the ozone level in the air.

For particle case, particle source was assumed to be the particle generation by one person sitting indoor with a generation rate of 5000 particles/s based on previous study (Habchi et al. 2015; Zhao et al. 2007). The particle density was assumed to be 1000 kg/[m.sup.3] [62.5 lb/[ft.sup.3]] and particle diameter was 1 [micro]m [3.94E-05 inch]. The particle injection velocity was 0.9 m/s [177.17 fpm]. The steady Lagrangian transport model was used to calculate the trajectories of particles.

Three source locations. For the validated model with C[O.sub.2] as pollutant and displacement ventilation as ventilation strategy, three C[O.sub.2] source locations were selected to examine the impact of the source location on the pollutant dispersion. The first source was located farthest from the diffuser while the second source was located at the center of the room. The third source was placed in front of the diffuser. Figure 3 indicates these three different source locations.

In summary, 9 simulation cases with 3 ventilation strategies and 3 pollutant characteristics as well as other 2 simulation cases with 2 different source locations in chamber with C[O.sub.2] as pollutant and displacement ventilation as ventilation strategy, total 11 simulation cases were used to perform the parametric study.

Evaluating Variables

To examine the distribution of indoor pollutants and determine the optimal IAQ sensor positioning, for each of the 11 simulation cases, the pollutant concentration profiles in the whole chamber were generated and the mean values of pollutant concentrations were calculated in the breathing zone defined as the space between planes 7.55 and 180 cm [3 and 72 inch] above the floor and further than 60 cm [2 ft] from the walls.

The mean pollutant concentrations at the exhaust were calculated and compared to the pollutant concentrations in the breathing zone to test the performance of the IAQ sensor placed at the room exhaust.

RESULTS AND DISCUSSION

The study results are organized into two sections: validation of CFD simulation and parametric analysis.

Validation of CFD simulation

Figure 4 presents the CFD validation results comparing the temperature and C[O.sub.2] concentration distributions from CFD simulations with those of the experimental results. Figure 4a indicates a good agreement between the simulated and measured temperatures at the temperature sensor tree. The largest discrepancy observed is 0.84 [degrees]C [1.51 [degrees]F] at 1.7 m [5.58 ft]. Figure 4b shows the simulated CO2 concentrations agree well with the measured ones and the difference is largest at CO2 sensor 2 which is 15.65 ppm. Considering the accuracy of the temperature sensors is 1 [degrees]C [1.8 [degrees]F] and of the CO2 sensors is 25 ppm, the results in Figure 4 suggest this CFD model is sufficiently accurate to perform further numerical study.

Parametric analysis

Figure 5, Figure 6 and Figure 7 show the distributions of C[O.sub.2] concentration, ozone concentration and particle concentration in the chamber with three different diffuser arrangements which are (a) displacement ventilation diffuser, (b) side wall slot diffuser and (c) ceiling diffuser. It is observed that in each of the three cases with different pollutant types, the distributions of pollutant concentration show noticeable difference as a result of different diffuser arrangements. The different diffuser arrangements can cause different airflow patterns and temperature distributions which have notable impact on the dispersion of pollutants.

The mean concentrations of C[O.sub.2], ozone and particle in the breathing zone and at the exhaust are listed in the Table 2, Table 3 and Table 4. In each of the three cases with different pollutant types, the mean values of the pollutant concentration vary significantly as altering the ventilation strategies. In the chamber with the displacement ventilation diffuser, the mean pollutant concentrations in the breathing zone are lowest and the mean pollutant concentrations at the exhaust are highest, indicating an improved performance of removing pollutant and better IAQ. This trend due to the airflow pattern of displacement ventilation which causes low value of mean age-of-air in the breathing zone.

Table 2, Table 3 and Table 4 also suggest a significant influence of the pollutant characteristics on the pollutant concentration profile. In the two cases with ozone and particle as pollutant, the mean pollutant concentration in the breathing zone is highest with the ceiling diffuser while in the case with C[O.sub.2] as pollutant, is highest with the side wall slot diffuser. Figure 5, Figure 6 and Figure 7 also display that with the same ventilation strategy, the distributions of pollutant concentration may be completely different with various pollutant types.

When comparing the mean pollutant concentration in the breathing zone to it at the exhaust in each case, the difference between these two values are smallest as the ceiling diffuser is used and significantly larger as the displacement ventilation and side wall slot diffuser are used. As a result, the IAQ sensor positioning at the room exhaust is able to precisely reflect the pollutant concentration within the breathing zone only in case with ceiling diffuser.

Figure 8 shows the C[O.sub.2] concentration profiles in the chamber with displacement ventilation diffuser for three different source locations. It suggests when the ventilation strategy and pollutant characteristics are fixed, the change of the source location can also cause large difference of the pollutant dispersion. The mean C[O.sub.2] concentrations in the breathing zone listed in Table 5 illustrate the impact of source location on the pollutant distribution. It seems that with the decrease in the distance between the source and diffuser, the C[O.sub.2] concentration in the breathing zone increases, leading to worse IAQ.

CONCLUSION

This paper reports an experimentally validated numerical study on the impact of three parameters including the diffuser arrangement, pollutant characteristics and source location on the dispersion of particles and gases in building to optimize the IAQ sensor positioning. To achieve this goal, the pollutant concentration profiles in the whole chamber were generated and the mean pollutant concentrations in the breathing zone and at the exhaust were calculated. The conclusions can be drawn as follows:

(1) Diffuser arrangement, pollutant characteristics and source location have significant influence on the dispersion of particles and gases which may cause considerable variation of the sensor reading.

(2) Change of the ventilation strategy may lead to a totally different pollutant concentration distribution. The results show the displacement ventilation diffuser can most effectively reduce the pollutant concentration in the breathing zone and improve the IAQ. When the pollutants are ozone or particles, the pollutant concentration within breathing zone is highest with the ceiling diffuser while in the case with C[O.sub.2] as pollutant, is highest with the side wall slot diffuser. Therefore, the dispersion of pollutant is also highly related to the pollutant characteristics.

(3) The distributions of the pollutant concentration vary significantly while change the source location. The results suggest that the pollutant concentration in the breathing zone increases as the pollutant source is placed closer to the diffuser.

(4) Generally, the difference between the mean values of the pollutant concentration in the breathing zone and at the exhaust is smallest in the case with ceiling diffuser. However, when the displacement ventilation diffuser or side wall slot diffuser are used, the difference between these two values is unacceptably large. Therefore, sensor positioning at the room exhaust can work in only limited cases such as a room with ceiling diffuser.

REFERENCES

Daisey JM, Angell WJ, Apte MG. 2003. Indoor air quality, ventilation and health symptoms in schools: an analysis of existing information. Indoor Air 13:53-64.

Emmerich SJ, Persily AK. 1998. Energy impacts of infiltration and ventilation in U.S. Office buildings using multizone airflow simulation. In: Proceedings of IAQ and energy, New Orleans, LA. p. 191-203.

Kusuda T. 1976. Control of ventilation to conserve energy while maintainingacceptable indoor air quality. ASHRAE Transactions 82:1169-81.

Nielsen TR, Drivsholm C. 2010. Energy efficient demand controlled ventilation in single family houses. Energy and Buildings 42:1995-8.

Persily AK. 1997. Evaluating building IAQ and ventilation with indoor Carbon Dioxide. ASHRAE Trans 103:193-204.

M. Krarti and M. Al-Alaw. 2004. Analysis of the Impact of CO2-Based Demand-Controlled Ventilation Strategies on Energy Consumption. ASHRAE Transactions 110(1), p. 274.

Nassif N, Kajl S, Sabourin R. 2005. Ventilation control strategy using the supply CO2 concentration Setpoint. HVAC&R Res 11:239-62.

Sun ZW, Wang SW, Ma ZJ. 2011. In-situ implementation and validation of a CO2-based adaptive demand-controlled ventilation strategy in a multi-zone office building. Building Environ 46:124-33.

Anna Bulinska, Zbigniew Popiolek, Zbigniew Bulinski. 2014. Experimentally validated CFD analysis on sampling region determination of average indoor carbon dioxide concentration in occupied space. Building and Environment 72:319-331.

Donghyun Rim, Atila Novoselac. 2010. Ventilation effectiveness as an indicator of occupant exposure to particles from indoor sources. Building and Environment 45:1214-1224.

Mao Ning, Song Mengjie, Chan Mingyin, Pan Dongmei, Deng Shiming. 2016. Computational fluid dynamics (CFD) modelling of air flow field, mean age of air and CO2 distributions inside a bedroom with different heights of conditioned air supply outlet. Applied Energy 164:906-915.

Chris Jakober, Tom Phillips. 2008. Evaluation of Ozone Emissions from Portable Indoor Air Cleaners: Electrostatic Precipitators and Ionizers. California Environmental Protection Agency.

Carine Habchi, Kamel Ghali, Nesreen Ghaddar, Alan Shihadeh. 2015. Chair fan-enhanced displacement ventilation for high IAQ: Effects on particle inhalation and stratification height. Building and Environment 84:68-79.

Bin Zhao, Ping Guan. 2007. Modeling particle dispersion in personalized ventilated room. Building and Environment 42:1099-1109

Gen Pei, MS

Student Member ASHRAE

Donghyun Rim, PhD

Associate ASHRAE Member

Matthew Vannucci

Student Member ASHRAE
Table 1. Initial and Boundary Conditions

Initial CO2    Diffuser Inlet        Diffuser Inlet
Concentration  Velocity              CO2 Concentration

470 ppm        0.075 m/s[14.76 fpm]  470 ppm

Initial CO2    Wall Temperature                   CO2 Supply Velocity
Concentration

470 ppm        23.5 [degrees]C [74.3 [degrees]F]  0.4 m/s [78.74 fpm]

Table 2. C[O.sub.2] Concentrations in a Chamber with Different Diffuser
Arrangement

Diffuser Arrangement               Mean CO2 Concentration in
                                   Breathing zone (ppm)

Displacement Ventilation Diffuser  561.66
Side Wall Slot Diffuser            745.14
Ceiling Diffuser                   668.34

Diffuser Arrangement               Mean CO2 Concentration at
                                   Exhaust (ppm)

Displacement Ventilation Diffuser  659.27
Side Wall Slot Diffuser            659.12
Ceiling Diffuser                   658.20

Table 3. Ozone Concentrations in a Chamber with Different Diffuser
Arrangement

Diffuser Arrangement               Mean Ozone Concentration in
                                   Breathing zone (ppb)

Displacement Ventilation Diffuser  33.09
Side Wall Slot Diffuser            33.97
Ceiling Diffuser                   35.33

Diffuser Arrangement               Mean Ozone Concentration at
                                   Exhaust (ppb)

Displacement Ventilation Diffuser  35.06
Side Wall Slot Diffuser            35.05
Ceiling Diffuser                   35.02

Table 4. Particle Concentrations in a Chamber with Different Diffuser
Arrangement

Diffuser Arrangement               Mean Particle Concentration in
                                   Breathing zone (kg/m3) [lb/ft3]

Displacement Ventilation Diffuser  1.1E1-11 [6.94E-13]
Side Wall Slot Diffuser            1.1E1-11 [6.94E-13]
Ceiling Diffuser                   2.19E-11 [1.37E-12]

Diffuser Arrangement               Mean Particle Concentration
                                   at Exhaust (kg/m3) [lb/ft3]

Displacement Ventilation Diffuser  3.04E-11 [1.9E-12]
Side Wall Slot Diffuser            2.24E-11 [1.4E-12]
Ceiling Diffuser                   1.78E-11 [1.E1-12]

Table 5. C[O.sub.2] Concentrations in a Displacement Ventilation
Chamber with Different Source Locations

Diffuser Source Location  Mean CO2 Concentration in
                          Breathing zone (ppm)

Source Location 1         561.66
Source Location 2         576.71
Source Location 3         664.30

Diffuser Source Location  Mean CO2 Concentration at
                          Exhaust (ppm)

Source Location 1         659.27
Source Location 2         658.70
Source Location 3         658.39
COPYRIGHT 2018 American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. (ASHRAE)
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2018 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:computational fluid dynamic and indoor air quality
Author:Pei, Gen; Rim, Donghyun; Vannucci, Matthew
Publication:ASHRAE Conference Papers
Article Type:Report
Date:Jan 1, 2018
Words:3070
Previous Article:On the Modeling of Flow Regimes around Cooling Towers.
Next Article:Characteristics and Causes of Outliers in Inverse Modeling of Residential Building Energy Use Data.
Topics:

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters