1、Reza Ghias is the Director of Advanced Simulation Center (ASC) at Southland Industries, Dulles, Virginia. Mikhail Koupriyanov is the Manager of CFD Services at Price Industries Ltd., Winnipeg, Canada. Ramin Rezaei is CFD Analyst at Southland Industries. CFD Validation and Optimization of Carbon Diox
2、ide Removal Efficiency in a Displacement Ventilation System Reza Ghias, DSc Member ASHRAE Mikhail Koupriyanov, Peng Ramin Rezaei ABSTRACT Displacement ventilation systems have found increased usage in recent years and have been shown to reduce energy consumption while providing good Indoor Air Quali
3、ty (IAQ). The system takes advantage of thermal buoyancy effect to displace warm air and light containments above the occupant breathing zone. This makes the displacement ventilation system a good candidate for cooling spaces with high ceilings. Despite the advantages of this system, there are still
4、 questions on the systems ability in removing Carbon Dioxide (CO2) and associated bioeffluents. The current paper investigates the CO2 concentration in an interior office with a displacement ventilation system. The temperature, velocity, and CO2 concentrations measured in several locations. The data
5、 was used to validate the corresponding Computational Fluid Dynamics (CFD) results and fine-tune the implemented numerical models. The CFD model was then used as a tool to study the impact of the exhaust locations and supply air volume on CO2 concentration in the space. INTRODUCTION Displacement ven
6、tilation is an energy efficient method for cooling spaces while providing good indoor air quality (IAQ) with respect to occupant-generated contaminants such as gases (CO2 and bio-effluents) and small particles (Rim (b) Sensor Set-Up with CO2, Temperature and Velocity Probes; (c) Sensor Tree; (d) Dis
7、placement Diffuser with Plenum and Supply Duct (e) CO2 Injection Point RESULTS AND DISCUSSION Validation The recorded variables showed some fluctuations during the tests (e.g. the CO2 concentration at the supply). We used the temperature and velocity data recorded by the sensor tree at different loc
8、ations (Figure 2) to tune the model. Figure 3 shows temperature at two different locations and it can be seen that the room temperature is under predicted by not considering the thermal radiation calculation in the simulation. In order to model the thermal radiation, the emissivity factors for the w
9、indows, walls and other opaque surfaces were included in the model. The results show reasonable agreement with the test with a slight over-prediction in temperature (Figure 3(a)-(b) and reasonable trends for velocity and CO2 concentration at different locations including two sample points presented
10、in Figure 3(c)-(d). 2016 ASHRAE Winter ConferencePapers 3Figure 2 CFD model of validation case (a) and layout of test chamber (b). Each measurement location includes points at 5 in 0.13m, 43 in 1.1m, 67 in 1.7m and 84 in 2.1m above the floor It is worth mentioning that due to buoyancy effect, small
11、eddies move in the room and make an unsteady flow field but averaged value of variables from steady state CFD simulation still shows a good agreement with test results. Some discrepancies in velocity can be justified by test conditions, for example, the sensor tree stand could have an impact on reco
12、rded velocities at the 5 in 0.13m height. In addition, the CO2 concentration at air supply was fluctuating between 637 and 700 PPM during the test which explains the differences between the test and simulation results (Figure 3(e)-(f). Figure 3 Temperature, velocity and CO2 concentration profiles, c
13、omparison between experiment and CFD (a) (b) (a) (b) (c) (d) (e) (f) 2016 ASHRAE Winter ConferencePapers 4The stratification of air due to buoyancy can be observed in Figure 4(a) by temperature contours in a plane through the dummies. Figure 4(b) shows how the temperature rises as the supply air pas
14、ses through the dummies. The CO2 concentration at the return was 853 PPM that is within the range of recorded values in test (798-886 PPM). In general, including a thermal radiation model in the CFD provided results in closer trends to corresponding recorded values in test. Figure 4 Temperature cont
15、ours for validation case (a) and flow streamlines (b) Optimization The main purpose of this study was to investigate the impact of the return location and supply air volume on CO2 removal in a small meeting room with a displacement ventilation system. After comparing the CFD results with test data (
16、referred as original case), the authors performed simulations for several locations of the return shown in Figure 5. Double returns were considered for cases (4) and (5) to investigate the impact of multiple returns in this study while the total return flow rate was kept constant throughout the expe
17、riments. All other boundary conditions were set up similar to the original case. CO2 concentration was calculated at 33 points at each height level corresponding to probe locations in the experiment (Figure 2(b) and averaged values were used in the graph. Figure 7(a)-(b) shows the iso-surface of 850
18、, 870, and 890 PPM of CO2 concentration in the room for original case and case (4) with two returns. Even though the location of the return made some differences in local CO2 distribution in the room (Figure 7(a)-(b), the average value changed very little as result of the return location or using mu
19、ltiple returns in cases (1) to (5). This can be observed in Figure 6 that presents the average value of CO2 concentration at different heights. This is an interesting result since previous studies show that the return location has an effect on CO2 concentration in a room with single CO2 source as we
20、ll as on contaminant particle concentration with a single particle source, such as a patient (Yin et al. 2009) where small particles (1 m) behave similarly to gases and follow the airflow pathlines. The test was set up such that CO2 was distributed in the room from multiple sources uniformly (throug
21、h the dummies). It can be concluded that in a conference room with uniformly located attendees (CO2 generation), the location and number of the returns have little impact on average value of the CO2 concentration. In addition, we investigated the impact of the supply airflow rate on CO2 concentratio
22、n. The original case was simulated with a 10%, 20% and 30% increase in the supply air volume. Figure 8(a)-(c) compares the CO2 concentration contours in the plane passing through the dummies for the original, 10%, and 20% increased supply air cases. Figure 9 compares the average CO2 concentration at
23、 different heights for original, 10%, 20%, and 30% increased supply air. In all cases, the CO2 concentration at the inlet was kept constant (666 PPM). The graph shows that increasing air supply decreases the average CO2 concentration at different heights. It is noted that increasing the supply air v
24、olume would likely adversely affect draft risk and thermal comfort in the space. The focus of the paper, however, is on the CO2 levels, which is why thermal comfort results are not included. Figure 9 also shows a trend that can used as a simple rule of thumb that for every percentage of air volume s
25、upply in to the room there would be a quarter percent reductuion of CO2 concentration in a uniformly populated meeting room. (a) (b) 2016 ASHRAE Winter ConferencePapers 5Figure 5 Return locations for the various optimization cases Figure 6 Average CO2 concentration in the room at different heights F
26、igure 7 Iso-surface of CO2 concentration (blue: 850 PPM, green: 870 PPM, yellow: 890 PPM) Figure 8 CO2 concentration contours at plane passing through the dummies for a) original, b) 10% more supply air, and c) 20% more supply air. (a) (b) (a) (b) (c) 2016 ASHRAE Winter ConferencePapers 6Figure 9 Av
27、erage CO2 concentration profile in the room at various air flows CONCLUSIONS An experiment was performed in a mock-up room with eight dummies, computers and lights as heat sources with a CO2 flow rate equivalent to eight people. The temperature, velocity and CO2 concentration were recorded at differ
28、ent heights in the room using proper sensors. The CFD models were set up and tuned to consider the thermal radiation in the room. It was found that ignoring thermal radiation under-predicts the room temperature. The CFD results were in acceptable agreement with test. We used the tuned CFD model to i
29、nvestigate the impact of the return location and supply air volume changes on CO2 concentration in the room. The results showed that with the current meeting room layout the return location has minor impact on average CO2 concentration in the room when the generation of CO2 is evenly distributed in
30、the space. It was also found that the CO2 concentration is more sensitive to air supply flow rate and as a rule of thumb, every 10 percent increase in air supply reduces the CO2 concentration by 2.5 percent. Possible future work can include using more sensors at different heights and taking measurem
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