Printer Friendly

Simulation Study of Infiltration Effects on Demand Controlled Ventilation System with High-variant Occupancy Schedules.

INTRODUCTION

Among different factors of indoor environmental quality (IEQ), indoor air quality (IAQ) is one of the most significant factors to occupant health and productivity considered in the design of commercial buildings like office buildings or schools. Among different particles, indoor C[O.sub.2] is usually used as an indicator of IAQ in commercial buildings. In a case study, it was found that for every 100ppm decrease in differential between indoor and outdoor carbon dioxide concentration (dC[O.sub.2]), office workers experienced up to 70% in decreased odds of having certain mucous membrane or lower respiratory symptoms (Apte et al. 2002). Moreover, Myhrvold et al. (1996) identified a 23% improvement in performance of school work due to a reduction in C[O.sub.2] concentrations (from above 1500ppm to below 999ppm) in a field study of 22 classrooms and about 550 pupils (aged between 15-20) from 5 different schools in Norway.

In addition, compared to conventional ventilation system approaches with fixed ventilation schedule, DCV system modulates outdoor airflow based on actual or estimated number of occupants, in order to better match space ventilation need and avoid energy waste (ASHRAE 62.1 - 2013). A 2005 simulation study in Oslo (Mysen et al. 2005) identified 66% average ventilation energy savings with C[O.sub.2]-based DCV systems, as compared to constant air volume (CAV) systems without DCV. Moreover, Chenari et al. (2016) conducted simulations of occupancy and C[O.sub.2]-based DCV strategies in an office room using EnergyPlus and found that a primary energy savings of 22% can be achieved with DCV based on the number of occupants while the IAQ level is kept acceptable. Lastly, a building case study of an office building in Montreal, Haghighat, Zmeureanu, and Giovanna (Haghighat et al. 1993) identified a 12% reduction in HVAC energy consumption from C[O.sub.2]-based DCV, as compared to conventional control based on mixing air temperature, for a dual duct constant air volume system. Therefore, the C[O.sub.2]-based DCV system has a great advantage of reducing energy consumption while still achieving acceptable IAQ based on indoor C[O.sub.2] concentration.

Even if C[O.sub.2] itself may not result in bad indoor air quality as seriously as other airborne contaminants, it is a good indicator in that malodorous bioeffluents are generated proportionally to C[O.sub.2] production (ASHRAE 62.1 User's Manual 2007) and the occupant is a dominant C[O.sub.2] generation source. Therefore, ASHRAE 62.1-2007 User's manual (ASHRAE 62.1User's manual 2007) has prescribed that the indoor C[O.sub.2] concentration shall not exceed 525 ppm above outdoor levels. unlike conventional ventilation systems supplying outdoor air based on design occupant density, C[O.sub.2]-based DCV uses a surrogate measure of occupancy to modulate outdoor air intake rates in real-time. The performance of DCV systems can further be improved with better estimates of occupancy. Fortunately, with the development of occupancy estimation tools such as real-time and non-intrusive vision-based sensing systems (Lu et al. 2018; Munir et al. 2017), there is a great potential to realize more effective operation of DCV.

In addition to well-recognized energy-saving merit of DCV, there has been some research focusing on verifying the impact of DCV on indoor air quality, especially C[O.sub.2] concentration. Nordstro and Norba (2013) conducted an intervention study to compare the effect of DCV with constant flow ventilation in university computer classrooms. They found that, at cost of increased ventilation rate, DCV produced lower C[O.sub.2] concentration resulting in less set-point not-met time and slightly mitigated headache and tiredness. However, in a field study in Hong Kong, Sun, Wang, and Ma (2011) found out that DCV resulted in higher C[O.sub.2] concentration than fixed rate ventilation in winter. Moreover, Cwen et al. (2011) pointed out that the actual C[O.sub.2] concentration with DCV may accumulate to exceed set-point concentration when unoccupied time is prolonged during which ventilation rate is kept low.

Last but not least, under transient conditions, indoor C[O.sub.2] concentrations will typically lag behind occupancy due to diffusion and mixing mechanics, as described in (Lu et al. 2018). As a result, the actual indoor C[O.sub.2] concentration may keep at high level when the occupant number reduces suddenly and significantly. With different DCV strategies, such lag effect may either cause over-ventilation or excessive C[O.sub.2] concentration. However, dynamic infiltration schedules with occupancy profiles on IAQ in combination of DCV may mitigate such effect. Therefore, this paper aims to extend the above paper to evaluate the effects of lag between C[O.sub.2] concentration level and the actual number of occupants on IAQ with two different occupancy profiles as well as how infiltration will mitigate the lag effects through the whole-building energy simulations of an office building case.

METHODOLOGY

In order to analyze the effects of lag and dynamic infiltration schedule on IAQ, a one-story office building has been simulated with Markov-chain-based occupancy profile and Gaussian-based occupancy profile. The proportional control ventilation system in terms of C[O.sub.2] simulation has been conducted in Energyplus v8.8 (Crawley et al. 2000). In addition, indoor C[O.sub.2] concentration is simulated with a module called Carbon Dioxide Predictor-Corrector in Energyplus v8.8 instead of CONTAM (CONTAM 2013), which is often used to simulate particle flow patterns.

Model configuration

Same as (Lu et al. 2018), the building considered in this paper contains three open plan office spaces and is located in Chicago, as shown in Figure 1. The total floor area of the building is 5601.54 [ft.sup.2] (520.4 [m.sup.2]), which is conditioned using a heat pump with proportional controlled DCV system. In addition, since the space is conditioned with HVAC system, unlike naturally ventilated building, the infiltration schedule of the building is considered to be constant as 0.4 air changes/hour. However, a dynamic infiltration schedule is also used where the air exchange rate keeps the same as fixed infiltration schedule during unoccupied mode while increases the infiltration rate dynamically based on the numbers of occupants during occupied mode so as to simulate the result of dynamic operation of windows.

Occupancy schedules

In order to compare results from (Lu et al, 2018), the occupancy profiles used the same settings. The occupancy profile based on Markov-chain model was generated with Occupancy Simulator developed by LBNL (Chen, Hong and Luo 2017) where the number of permanent users in north, east and west thermal zones are 16, 9 and 9, respectively so that the occupancy density is 15 [m.sup.2]/person (150 [ft.sup.2]/person). Moreover, the occupancy profile based on Gaussian model was generated with the mean and standard deviation to be (16, 8), (9, 4.5) and (9, 4.5) for north, east and west room, respectively. Figures 2 and 3 compare the occupancy profiles based on Markov-chain model and Gaussian model on a typical weekday. As shown in the figures, the Gaussian-based occupancy profile has larger variance during working hours than the Markov-chain-based occupancy profile. Markov-chain-based occupancy profile can be seen as schedules of regular office rooms while Gaussian-based occupancy profile can be seen as schedules of conference rooms where the variance is much higher.

Evaluation of DCV with proportional control

According to ASHRAE 62.1 User's manual, the breathing zone outdoor air flow rate at design occupancy and minimum outdoor air rate under steady-state conditions are shown in the following equations:

[V.sub.ot] = [[R.sub.p][P.sub.z]+[R.sub.a][A.sub.z]/[E.sub.i]] Eq.1

[V.sub.min] = [[R.sub.a][A.sub.z]/[E.sub.i]] Eq.2

where,

[V.sub.ot] is the outdoor air flow intake rate calculated with the number of occupants, [[m.sub.3]/s]

[V.sub.min] is the minimum outdoor air intake rate when the space is unoccupied [[m.sub.3]/s]

[R.sub.p] is the required outdoor air flow rate per person, [([m.sup.3]/s)/person]

[P.sub.z] is the number of people,[#]

[R.sub.a] is the required outdoor air flow rate per unit area, [([m.sub.3]/s) /[m.sup.2]]

[A.sub.z] is the zone area, [[m.sup.2]]

[E.sub.i] is the effective coefficient, [usually between 0.8 and 1.1]

Due to lag of C[O.sub.2] concentration, the outdoor air intake flow rate can be calculated based on the differences between actual indoor C[O.sub.2] concentration and the outdoor C[O.sub.2] concentration as well as the indoor C[O.sub.2] concentration with the actual number of occupants as shown in Eq.3. In proportional control strategy, [C.sub.ot] is assumed to be the maximum indoor concentration, which is dependent on the actual number of occupants. However, the actual concentration may be higher than [C.sub.ot] due to high variance. As a result, the ventilation rate may not be adjusted properly. Moreover, Table 1 shows the inputs related to the DCV system, particularly to calculate [C.sub.ot].

[V.sub.o] = [V.sub.min] + ([V.sub.ot] - [V.sub.min]) [[C.sub.actual]-[C.sub.outdoor]/[C.sub.ot]-[C.sub.outdoor]] Eq.3

where,

[V.sub.o] is the actual outdoor air flow intake rate, [[m.sup.3]/s]

[C.sub.actual] is the actual indoor C[O.sub.2] concentration, [ppm]

[C.sub.outdoor] is the outdoor C[O.sub.2] concentration, [ppm]

[C.sub.ot] is the design indoor C[O.sub.2] concentration calculated with the actual number of occupants, [ppm]

Based on the equations above, the outdoor air flow rate can be calculated every time step and Table 1 shows the inputs related to the DCV system according to ASHRAE 62.1.

RESULT ANALYSIS

Comparisons of actual indoor C[O.sub.2] concentration w/o dynamic infiltration schedule

Considering the North thermal zone, Figure 4.a and Figure 4.b compare the monthly indoor C[O.sub.2] concentrations of the two different occupancy profiles with and without dynamic infiltration schedule. As shown in the figures, for both occupancy profiles, the actual indoor C[O.sub.2] concentration with fixed infiltration rate is always higher than that with dynamic one. That is because the occupant-responsive window operation during occupied mode helps dilute C[O.sub.2] concentration. Moreover, the C[O.sub.2] concentration with Markov-chain-based occupancy profile is always lower than that with Gaussian-based occupancy profile due to lower variance of occupant number.

Even if with DCV system, indoor C[O.sub.2] concentration is lower than that without DCV system, it still has risk of exceeding the design value of [C.sub.ot]. Therefore, Figure 5 compares the proportion of hours when the actual C[O.sub.2] concentration exceeds the design value over the whole year with two different occupancy profiles without dynamic infiltration schedule. For all zones, the proportion of hours when the actual concentration exceeds the design value with Markov-chain-based occupancy model is 84%, 95% and 95% less than that with Gaussian-based occupancy model due to higher variance of the occupant number, respectively.

However, for Gaussian-based occupancy profile, if the building is operated with dynamic infiltration schedule, the actual concentration is always below the acceptable range throughout a year.

Last but not least, Figure 6 and Figure 7 show the changes of the actual C[O.sub.2] concentration compared with the changes of occupant number over a certain period based on Gaussian model with or without dynamic infiltration schedules, respectively. As a result, the overall C[O.sub.2] level in more variant occupied mode with the dynamic infiltration schedule in Figure 7 is smaller than that with static infiltration schedule in Figure 6. However, the actual C[O.sub.2] concentration both lag behind the actual occupant number, as shown in figures.

DISCUSSIONS

Real-time occupant estimation tools provide a good opportunity to develop DCV with more accurate information in terms of occupant number. However, as mentioned in the result analysis, with high-variant occupancy schedule, indoor C[O.sub.2] concentration indeed lags behind the actual number of occupants, which results in excessive concentration than the design value in the studied proportional control strategy. However, the dynamic infiltration rate owing to operation of windows could mitigate the lag effect. Therefore, it is suggested C[O.sub.2]-based DCV be integrated with dynamic operation of building envelopes such as operable windows or advanced real-time recognition of occupant behaviors such as window open status, etc.

CONCLUSION

This paper evaluates the effects of lag on IAQ and provide suggestions on adjustment of ventilation strategies beyond proportional control with different occupancy schedules and infiltration schedules. The results showed that the lag of C[O.sub.2] concentration behind actual occupant number is more obvious in the space with high variant occupancy profiles. Fortunately, dynamic infiltration could mitigate such lag effect. Therefore, with real-time occupant estimation tools, it is suggested to operate DCV by integrating the system with dynamic operations of building envelopes in order to improve the indoor air quality.

ACKNOWLEDGEMENT

This paper is sponsored by Chinese Scholarship Council.

REFERENCES

Apte, G.; and Erdmann, A. (2002): Associations of Indoor Carbon Dioxide Concentrations, VOC's, and Environmental Susceptibilities with Mucous Membrane and Lower Respiratory Sick Building Symptoms in the BASE Study; Analyses of the 100 Building Dataset: LBNL-53842.

ASHRAE, S. (2007). Standard 62.1-2007 User's Manual. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta.

ASHRAE, A. (2010). ASHRAE 62.1-2010 Ventilation for Acceptable Indoor Air Quality. Atlanta, GA.

CONTAM. 2013. http://www.bfrl.nist.gov/IAQanalysis/CONTAM/

Chen, Y., Hong, T., & Luo, X. (2018, February). An agent-based stochastic Occupancy Simulator. In Building Simulation (Vol. 11, No. 1, pp. 37-49). Tsinghua University Press.

Chenari, B., Lamas, F.E., Gaspar, A.R. & Silva, M.G. (2016, October). Simulation of occupancy and C[O.sub.2]-based demand-controlled ventilation strategies in an office room using Energyplus. 2016 CONECT, Riga, Latvia.

Crawley, D. B., Lawrie, L. K., Army, U. S., Champaign, C., Curtis, I., Pedersen, O., & Winkelmann, F. C. (2000). EnergyPlus: Energy Simulation Program. ASHRAE Journal, 42, 49-56.

Haghighat, F., Zmeureanu, R., Giovanna, D. (1993). Energy Saving in Buildings by Demand Controlled Ventilation System. Indoor Air, v.5, pp 51-56.

Lu, S., Hameen C. E., Aziz, A. (2018). Dynamic HVAC Operations with Real-time Vision-based Occupant Recognition System. 2018 ASHRAE Winter Conference, Chicago.

Lu, S., Yang, J., Hameen C. E., Karaguzel, O. (2018). Simulation of dynamic ventilation with real-time occupancy estimation. 2018 ACEEE summer study, Monterey.

Munir, S., Arora, R., et al. (2017). "Real-Time Fine-Grained Occupancy Estimation using Depth Sensors on ARM Embedded Platforms." In RTAS, Pittsburgh, USA, 2017

Mysen, M., Berntsen, S., Nafstad, P., & Schild, P. G. (2005). Occupancy density and benefits of demand-controlled ventilation in Norwegian primary schools. Energy and Buildings, 37(12), 1234-1240.

Myhrvold, A.; Olsen, E.; and Lauridsen, O. (1996): Indoor Environment in Schools--Pupils Health and Performance in Regard to CO2 Concentrations: Indoor Air; 4, pp. 369-371.

Nordstro, K., & Norba, D. (2013). Carbon dioxide (CO2) demand-controlled ventilation in university computer classrooms and possible effects on headache, fatigue and perceived indoor environment: an intervention study, 199-209.

Owen, M., Qu, M., Zheng, P., Li, Z., & Hang, Y. (2011). CO2-based demand controlled ventilation under new ASHRAE Standard 62. 1-2010: a case study for a gymnasium of an elementary school at West. Energy & Buildings, 43(11), 3216-3225.

Sun, Z., Wang, S., & Ma, Z. (2011). In-situ implementation and validation of a CO2-based adaptive demand-controlled ventilation strategy in a multi-zone of fi ce building. Building and Environment, 46(1), 124-133. https://doi.org/10.1016/j.buildenv.2010.07.008

Shendell, D. G., Prill, R., Fisk, W. J., Apte, M. G., Blake, D., & Faulkner, D. (2004). Associations between classroom CO2 concentrations and student attendance in Washington and Idaho. Indoor Air, 14(5), 333-341.

Mi, Y.-H., Norback, D., Tao, J., Mi, Y.-L., & Ferm, M. (2006). Current asthma and respiratory symptoms among pupils in Shanghai, China: influence of building ventilation, nitrogen dioxide, ozone, and formaldehyde in classrooms. Indoor Air, 16(6), 454-464.

Siliang Lu

Student Member ASHRAE

Dr. Omer Karaguzel

Dr. Erica Cochran Hameen

Siliang Lu is a PhD candidate in Building Performance and Diagnostics, School of Architecture, Carnegie Mellon University, Pittsburgh, Pennsylvania. Dr. Karaguzel is an Assistant Professor in Building Performance and Diagnostics, School of Architecture, Carnegie Mellon University, Pittsburgh, Pennsylvania. Dr. Cochran Hameen is an Assistant Professor in Building Performance and Diagnostics, School of Architecture, Carnegie Mellon University, Pittsburgh, Pennsylvania.
Table 1. The inputs related to DCV simulations

Required outdoor   Required outdoor     Required outdoor
air flow rate per  air flow rate per    air flow rate per
person[([m.sup.3]  person[(cfm/person]  area[([m.sup.3]/s)/[m.sup.2]]
/s)/person]

0.002952           6.255                0.000381

Required outdoor   Required outdoor      Acceptable indoor
air flow rate per  air flow rate per     concentration [ppm]
person[([m.sup.3]  area[cfm/[m.sup.2]]
/s)/person]

                                         less than 525 above
0.002952           0.8073                outdoor
                                         concentration
COPYRIGHT 2019 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 2019 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Lu, Siliang; Karaguzel, Dr. Omer; Hameen, Dr. Erica Cochran
Publication:ASHRAE Transactions
Article Type:Report
Date:Jan 1, 2019
Words:2852
Previous Article:Performance Evaluation of Energy--Efficient Hybrid Ventilation Systems for Office Buildings.
Next Article:Further Simplifying ASHRAE Standard 62.1 for Application to Existing Buildings: Comparing Informative Appendix D and Section 6.2.5.2 with Real-World...
Topics:

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |