CFD investigation of the statistical characteristics of NOx photo-catalytic degradation in a glass curtain wall in hazy winter weather

Sustainable Cities and Society 50 (2019) 101668

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CFD investigation of the statistical characteristics of NOx photo-catalytic degradation in a glass curtain wall in hazy winter weather

T

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Huiyuan Shena, , Fangli Dua, Yanhua Liub, Yu Huangc, Yufei Zhangc, Zhenyu Wangc, Qi Wua, Wenbo Hea a

School of Energy and Architecture, Xi'an Aeronautical University, Xi'an, 710077, China Department of Building Environment and Services Engineering, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an, China c Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Photo-catalysis Glass curtain wall Statistical characteristics Swirling flow NOx

In the hazy winter weather, in order to know the main factors impacting the statistical characteristics of NOx degradation in a photo-catalytic breathable glass curtain wall, CFD simulation technology was used to research the statistical characteristics of NOx migration from the outdoor environment to the indoor environment. In this paper, the relationship between swirling flow, temperature and NOx concentration distribution was emphatically investigated. The simulation results showed that the temperature distribution had no direct relationship with the statistical distribution of NOx concentration, while the statistical distribution of NOx concentration had a direct correlation with the swirling flow. The existence of the complex statistical characteristics for swirling flow and NOx concentration had been identified inside the photo-catalytic breathable glass curtain wall. Furthermore, a relationship was observed between the swirling flow, turbulence and NOx concentration in the glass curtain wall. The results showed that one simple functional relationship existed between the NOx statistical concentration and the turbulence intensity generated by the swirling flow in the curtain wall. Moreover, close to the swirling flow region, the optimal functional relationship was presented as CR = 0.85+0.1e−I, where CR was the NOx relative concentration and I was the turbulence intensity.

1. Introduction In winter, air pollution seriously affects the health of urban residents in China. Among the pollutants, NOx is discharged by vehicles and industrial equipment. NOx causes photochemical smog and haze (Verbruggen, Lenaerts, & Denys, 2015; Zhang, Johannes, & Ding, 2018). In winter, eliminating and preventing the entrance of NOx from the outdoor environment into the interior of buildings has received increasing attention from the scientific community and public. To improve the indoor air quality, the efficient elimination of NOx from buildings has become a crucial issue. However, in an effort to save energy heating−ventilation−and−air-conditioning (HVAC) systems in buildings usually do not control and eliminate of NOx. Fortunately, photo-catalysis has already been demonstrated to be an effective technology for the elimination of the NOx gaseous pollutants. In winter, NOx can be controlled by the TiO2 degradation technology without secondary pollution under normal temperature and pressure conditions (Gu & Tong, 2004; Yang, Liu, Wu, Wang, & Zhang, 2017). However, TiO2 degradation technology has a low utilization rate by visible light.

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Therefore, the combination of TiO2 photo-catalytic materials with the building surface, which having a larger area, can effectively increase NOx degradation in buildings and other semi-closed city spaces has been considered. (Weng & Huang, 2013). Many research institutions have combined TiO2 photo-catalytic materials with urban construction surfaces to degrade gaseous pollutants (Folli et al., 2015; Guerrini, 2012; Maggos et al., 2008). In order to solve the headache of air pollution in China, Chinese researchers have accelerated the research into urban building/construction surface technologies based on TiO2 photocatalytic materials (Huang et al., 2016, 2014; Zhang & Wang, 2014). In winter, in order to utilize the capacity of the TiO2 photo-catalytic building surfaces for degrading NOx more effectively, it is necessary to solve the problems of NOx separation from the building surface with airflow, the low efficiency of the sunlight and the adsorption saturation caused by the local high NOx concentration (And & Onaka, 2004; Nakayama, Hayashi, & Eguchi, 2002; Peng et al., 2017; Saeung & Boonamnuayvitaya, 2008; Yu et al., 2013). To solve the above problems, specific equipment or facilities can be used to control the NOx movement on building surfaces, prevent the NOx migration from

Corresponding author. E-mail address: [email protected] (H. Shen).

https://doi.org/10.1016/j.scs.2019.101668 Received 31 March 2019; Received in revised form 3 June 2019; Accepted 18 June 2019 Available online 21 June 2019 2210-6707/ © 2019 Elsevier Ltd. All rights reserved.

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Thangam, Gatski, & Speziale, 1992) model, were widely used in the RANS method. Among these family models, RNG k − ε model showed better performance and was widely used in predicting the flow field rather than the other models (Tominaga & Stathopoulos, 2009). In this paper, RNG k − ε model was chosen to simulate the flow around and in the glass curtain wall. In this article, firstly, a CFD simulation was conducted on a full-scale photo-catalytic glass curtain for the statistical characteristics of NOx. Secondly, the simulation results were studied to assess the factors which affected the NOx photo-catalytic degradation progress. Finally, the distribution rule of NOx concentration was deduced based on these factors. 2. Mathematical modelling 2.1. Degradation progress modelling and experimental validation Fig. 1. The principle of the full-scale glass curtain wall combined with TiO2.

2.1.1. Modelling In the present study, the NOx photo-catalytic degradation progress occurred in the glass curtain wall. The schematic photo-excitation of TiO2 particles was shown which by exposure to radiation with energy above the band gap energy as depicted in Fig. 2. An exciton produced by the absorption of a photon was shown by the red solid circle symbol. This was followed by the charge separation hole pair which was the production of an electron (John & Yates, 2009). Charge transported to the surface by the processes C and D leading to desirable reduction and oxidation reactions on the surface respectively. Except for the processes C and D, A and B processes represented the electron–hole pair recombination processes at the surface and in bulk, respectively. Based on the photo-excitation of TiO2, the principle of the photocatalytic degradation progress of the glass curtain wall was that the ultraviolet radiation was supplied by the sunlight or UV lamps installed in the curtain wall, and passed through the surfaces of the glass curtain wall. When the radiation energy was greater than the width of the forbidden band, the electrons in the valence band of TiO2 were excited and moved to the conduction band to become photogenic electrons (e-), and the glass curtain wall surfaces produced the electrons. At the same time, a photogenic hole (h+) was generated in the valence band, and the glass curtain wall surface produced the holes. Lastly, oxidation occurred on the photo-catalyst building surfaces, and NOx was degraded. The NOx reaction should be assumed to obey Langmuir–Hinshelwood (L-H) kinetics. It should be emphasized that the L-H mechanism should be thought of as an ideal, empirical limit. Assuming the simple Langmuir behavior, the fractional coverage of NOx on an illuminated TiO2 surface of the glass curtain wall was given by

outdoors to indoors and change the process of NOx adsorption/desorption (Qian, Li, & Li, 2015; Zhao, Gao, & Li, 2017). The glass curtain wall combined with the TiO2 photo-catalytic material is a facility which can control the air movement on the external surface of the building and eliminate the NOx on the glass surfaces effectively. The glass curtain wall combined with TiO2 photocatalytic material can also prevent NOx from entering the residential rooms. Fig. 1 outlines the principle of the glass curtain wall combined with the TiO2 photo-catalytic material. Because the glass curtain wall is composed of the inner and exterior glasses, a cavity is formed in the glass curtain wall. The NOx and oxygen around the building must flow into the building room through this cavity. During the progress, the glasses combined with the TiO2 photo-catalytic material can degrade the NOx concentration and prevent the pollutants from entering the room with the sun or ultraviolet (UV) lamp light. In addition, the glass curtain wall can also form the chimney effect inside the cavity, which can be used as the natural driving force to form the natural ventilation and accelerate the exchange of the indoor and outdoor air (Nasri, Alqurashi, Nciri, & Ali, 2018; Rahbar & Riasi, 2019). Finally, in winter, the greenhouse effect in the double-layer glass curtain wall can be utilized to preheat the external cold air entering the building and to reduce the building energy consumption (Pereza, Perezb, Rábagoc, & Putnama, 2019; Wang, Huang, & Cao, 2006; Xu & Ojima, 2007; Zeng, Li, & Li, 2012). Glass curtain walls combined with TiO2 materials can be modeled and researched using various approaches, and computational fluid dynamics (CFD) simulation is one of these methods. Recently, the CFD simulation method was successfully used as a tool to investigate the photo-reactor. Mohseni and Taghipour (2004) employed both CFD and experimental approaches to study pollution removal in an annular photo-catalytic reactor and reported good agreement of CFD with experimental data. Salvado-Estivill, Brucato, and Puma (2007) combined CFD methods with radiation field modeling to study a flat-plate reactor. Hossain, Raupp, Hay, and Obee (1999) presented a three-dimensional convection−diffusion−reaction model to simulate progress in a photocatalytic reactor. Different from the progress in a photo-reactor, the flow around a building is usually at high Reynolds number. The flow around and in a glass curtain walls is turbulent. For the CFD method, the turbulence model is an important and primary factor to accurately simulate the flow motion. In many turbulence models, Reynolds-averaged Navier-Stocks (RANS) method (Yakhot & Orszag, 1986) has been widely used to investigate the wind patterns around buildings. Compromising the accuracy and cost, the RANS method is an excellent tool to represent the flow statistical pattern. The family models including the Standard k − ε , Realizable k − ε (Shih, Liou, Shabbir, Yang, & Zhu, 1995) and Renormalization Group (RNG) k − ε (Yakhot, Orszag,

Fig. 2. Schematic photo-excitation in a TiO2 particle. 2

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the known expression (Verbruggen et al., 2015):

θ=

KCNOx 1 + KCNOx

(1)

where K is the Langmuir adsorption equilibrium constant under illumination(m3 mol−1) and CNOx the NOx concentration (mol·m-3). The reaction rate on the glass curtain wall was generally written based on the L-H mechanism given by:

r=

kKCNOx 1 + KCNOx

(2) −2 -1

where k (mol·m ·s ) was the Langmuir–Hinshelwood rate coefficient. At sufficiently high intensity, the reaction rate became a constant independent of light intensity, and the mass transfer limit was encountered (Puddu, Choi, Dionysiou, & Puma, 2010). The reaction rate based on the L-H mechanism, to simulate the pollution, the turbulence was modeled using the classical Reynoldsaveraged Navier-Stocks (RANS) Renormalization Group (RNG) k − ε turbulence model (Kim & Baik, 2004; Yakhot et al., 1992).

∂k ∂k ∂U ∂ ⎛ Km ∂k ⎞ + Uj = − u i uj i + ⎜ ⎟ − ε ∂t ∂x j ∂x j ∂x j ⎝ σk ∂x j ⎠

(3)

∂ε ∂ε ∂U ∂ ⎛ Km ∂ε ⎞ ε ε2 + Uj = −C1ε ui uj i + −R ⎜ ⎟ − C2ε ρ ∂t ∂x j ∂x j ∂x j ⎝ σε ∂x j ⎠ k k

(4)

R=

η=

Fig. 3. The system used for photocatalytic surface activity test evaluation.

Cμ η3 (1 − η η0) ε 2 (1 + β0 η3) k ∂Uj ∂Ui 1 k ∂Ui [( ) ] + ∂x i ∂x j ε ∂x j

(5) 2

(6)

where σk , σε , C1ε and C2ε were empirical constants, 0.7179, 1.42, 1.42 and 1.68 respectively. The other constants Cμ , η0 and β0 were 0.0845, 4.377 and 0.012 respectively. The NOx was simulated using the species transport equation taking the form (Wang, Tan, & Yu, 2014):

∂ ∂ ∂ ∂ C+ (Ui C ) = (Γ C) ∂t ∂x i ∂x i ∂x i

(7)

Notice that in the CFD calculations, C was the NOx concentration calculated for the flow near the active surface. This was an anisotropic variable over the surface, thus C was simulated based on the wall model to describe the net rate of production or the reaction rate of NOx (Verbruggen et al., 2015; Walsem, Verbruggen, Modde, Lenaerts, & Denys, 2016; Wang et al., 2014).

Fig. 4. Visible-light induced photocatalytic activities of the as-prepared films for NOx removal.

2.1.2. Experimental validation In this paper, one photocatalytic activity experiment was implemented. Photocatalytic activity of the building surface with TiO2 were evaluated at PPb levels of continuous NOx flow in the photoreactor system and at ambient temperature. The system is shown in Fig. 3. The 300 mm × 150 mm × 100 mm reaction chamber was composed of a stainless steel vessel and a quartz window according to ISO 22197-1. Visible-light was supplied by a xenon lamp vertically passed through the quartz window. In this reactor, a piece of 100 mm diameter glass coated with the photocatalytic surface was positioned at the center of the reactor. The distance between the light source and samples was maintained at 100 mm. Initial NOx concentration for the photocatalytic test was diluted to 400 ppb at 3.0 l/min through the air stream supplied by a zero-air generator (Model 1001, Sabio Instruments LLC, Georgetown, TX, USA). NOx concentrations were continuously recorded through a chemiluminescence NOx analyzer (Ecotech, 9841). Lowconcentration (PPb level) NOx removal was surveyed in the specialized air purification system with the photocatalytic surface under visible light irradiation. During the photocatalytic reaction, most samples exhibited visible-light activity without any inactivity in 30 min, as shown

in Fig. 4. The sharp decrease of NOx concentration in the first 20 min. can be fully explained by the L-H kinetics model, the photocatalytic degradation of NOx was fitted well with the mass-transfer-controlled pseudo-first-order rate reaction (Konstantinou & Albanis, 2003). A NOx removal ratio of 22.5% was obtained over pure TiO2. Excellent photodegradation efficiency of NOx was obtained. In this paper, the CFD method was used to simulate the progress of the photocatalytic surface activity test with the RANS RNG k − ε turbulence model (Yakhot et al., 1992). Based on the L-H kinetics model and the data in Fig. 4, the Langmuir adsorption equilibrium constant K was 95183.7 m3 mol−1, and the Langmuir–Hinshelwood rate coefficient k was 3.3 × 10-9 mol·m-2·s-1. The CFD numerical errors, based on the experimental results, were calculated according to the relative error measure of the NOx between the numerical values and the experimental values as

CRANS − CEXP CEXP

× 100%, where CRANS was the NOx concentration

(PPb) simulated by the RNG turbulence model and CEXP was the experimental value of the NOx concentration (PPb). The NOx concentration was 328.009 PPb simulated by the RNG turbulence model, and the 3

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Fig. 5. A single building with the photo-catalytic glass curtain wall.

experimental value of the NOx concentration was 310 PPb. The relative error measure of the NOx was 5.81% which was less than 10.0%, so the RNG turbulence model and the L-H kinetics model could be used in simulating the statistical characteristics of the NOx concentration.

2.2. Glass curtain wall CFD modelling and quality assessment

Fig. 7. The computational domain.

2.2.1. Modelling In this paper, as shown in Fig. 5, the glass curtain wall CFD modelling could be carried out for one single building with the photo-catalytic glass curtain wall in three-dimensions. Here, b was the width of the building, the width of the building was 50 m. H was the height of the building, the height of the building was 60 m. The Reynolds number of the flow field was 5.0 × 107. Re was determined by UH , U was the ν mean velocity at the height H . The photo-catalytic breathable curtain wall covering the building surface consisted of two layers of inner and exterior glass walls. The inner and exterior glass walls formed a semienclosed space, the solar radiation was 45° to the wall surface, as shown in Fig. 6. The glass was semi-transparent. The reflected radiation was considered 0.6. In winter, the photo-catalytic double-layer glass curtain wall had two main functions: preheating the external cold air and degrading the outdoor pollutants. The preheated outdoor cold air was discharged from the upper outlet to the building interior room. In this case, the building interior room was chosen on the 12th floor, at the height 36 m. The photo-catalytic curtain wall height of the room was 3 m. The width of the curtain wall was 3.5 m. The depth of the curtain wall was 0.3 m. The outlet of the curtain wall area was 0.75m (y ) × 0.16m (z ) . The inlet area was 3.5m (y ) × 0.2m (z ) . The computational domain covered 21b (x ) × 13.75b (y ) × 10.45b (z ) for the outdoor environment around the building. This computational domain contained 234,706 grids. The minimum grid width was 0.05b and 20 cells for building width and 22 cells for building height were used following the commendations by Architectural Institute of Japan (AIJ) guidelines (Tominaga et al., 2008). In the space away from the central building, the cell size was increased by a factor and kept around 1.2. The inflow boundary was set at the windward position, where x = −9.57b (as shown in Fig. 7). At the inflow location, the vertical profile for the mean velocity U, the turbulence kinetic energy k, the turbulence dissipation rate ε were (Gromke & Blocken, 2015):

U (z ) =

u* z + z0 ln ( ) k z0

(8)

k (z ) =

u2 * Cμ

(9)

ε (z ) =

u3 * κ (z + z 0)

(10)

with z the vertical position above ground, z0 is 1.0 m the aerodynamic roughness length representative for urban terrain (Wieringa, 1992), κ is 0.40 the van Karman constant, u* was 1.18 m·s−1, the Cμ is 0.09. The NOx concentration at the inflow location was assumed to be 100 PPb (ICS 13.040.20, GB 3095-2012). The temperature at the inflow location was set to be 0℃. In this paper, the temperature of the indoor air was assumed to be 15℃. At the lateral and upper surfaces of the computational domain, the normal gradients of tangential velocity components and the normal velocity components were set to be zero. For the downstream boundary, zero gradient condition was imposed. For the ground and building surface boundary, the standard wall function was used. Besides the above conditions, for the space scheme, a second order scheme was adopted for the spatial derivatives. For the curtain wall, the computational domain contained 98,368 grids. The minimum grid width was 0.016 m. The inlet boundary was set as pressure boundary. For the outlet boundary, zero gradient condition was also imposed. For ground and building surface boundary, the standard wall function was chosen. During the simulation, the sunlight intensity distribution in the glass curtain wall could be described by the following general radiative transfer equation (RTE):

σT 4 ∇⋅(I (→ r,→ s )→ s ) + (a + σs ) I (→ r,→ s )) = an2 +A π A=

σs 4π

∫0

4π

/ I (→ r,→ s ) Φ (→ s,→ s ) dΩ/

(11) (12)

→ → where I ( r , s ) was a beam of radiation intensity in the medium, which → depends on position r and direction → s ; σs and a were the absorption → →/ and scattering coefficients, respectively, of the medium; and Φ ( s , s ) was a phase function that indicates the in-scattering of incident radiation. In this article, the RTEs were solved using the discrete ordinate (DO) model. The DO radiation model solved an RTE for a finite number of discrete angles, each associated with a vector direction → s fixed in the global Cartesian system (x, y, z). Lastly, in this paper, all the simulation cases were conducted based on commercial code Fluent with the User Defined Function (UDF). The selected photo-catalytic curtain wall was at the height of 36 m, so the mean pressure at the height of 36 m should be selected to be the pressure at the curtain wall inlet. With this simulation, the value of the mean relative pressure was 95.65 Pa at the height of 36 m. The mean

Fig. 6. The photo-catalytic curtain wall. 4

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NOx concentration at the height 36 m should also be selected. The value of the mean NOx concentration was 0.00012 mol•m−3 which was simulated by the commercial code Fluent in this case. 2.2.2. Quality assessment To determine the required grid resolution of the environment around the building and in the curtain wall, a grid sensitivity study was performed in the computational domains. In order to obtain the quantitative metrics for the discretization error, the fractional error er according to (Gromke & Blocken, 2015):

er , i =

f2, i − f1, i f1, i

(13)

and the mean Grid Convergence Index (GCI) also according to:

GCIi =

α |(f2, i − f1, i ) f1, i | r21p r21p − 1

(14)

The GCI is based on the generalized Richardson Extrapolation. Here, f2,i is the solution on the coarse grid and f1,i the solution on the fine grid at position i, r21 is the grid refinement ratio (here r21 was 1.5), p is the order of the method (here p was 2.0), and α is a safety factor which is 3.0 (Gromke & Blocken, 2015). For the environment around the building, the grid sensitivity study involved the comparison of the mean wind velocity U and NOx concentration along the center vertical profiles at two locations with different flow regimes. One profile was located on the line x=-30 m; the other profile was located on the line x = 30 m. Three grids the 4.0 mgrid, 2.7 m-grid and 1.8 m-grid corresponding to cell counts of 13, 20 and 30 along the building height were generated. For the vertical profiles of velocity U and the NOx concentration at the intersection, average fractional errors were both less than 2%. The corresponding average GCI values were determined to be less than 10%. The percentage average fractional error and GCI value were accepted, and the 1.8m-grid was chosen to simulate the flow around the building. Similar to the grid resolution of the environment around the building, three grids: the 0.015 m-grid, the 0.01 m-grid and the 0.0067 m-grid were chosen for the curtain wall. For the profile of velocity U and the NOx concentration on the middle line in the x = 2.925 m section, average fractional errors were less than 2%. The corresponding average GCI values were determined to be less than 11%. The percentage average fractional error and GCI value were accepted, and the 0.0067 m-grid was chosen to simulate the flow in the glass curtain.

Fig. 8. Distribution of velocity on the sections in the curtain wall.

3. Results and discussion 3.1. Flow and dispersion statistical characteristics in the glass curtain wall In this Section, the flow and dispersion characteristics in the glass curtain wall are discussed. Fig. 8 shows the contours of the mean velocity in the different cross-sections A (x = 0.6 m), B (x = 2.0 m), C (x = 2.25 m) and D (y = 0.15 m). These results from the mean velocity distributions show that the air stream lines were perpendicular to the floor from the inlet to the outlet of the curtain wall. Because there was suction causing the air flowing to the outlet, on the cross-section D, the velocity was much higher close to the outlet of the curtain wall. The suction flow also affected the other velocity pattern results such as on the cross-section A, B and C. The different three sections had similar flow patterns, in the next discussion, the cross-section A, B, C and D are to be analyzed. The detailed discussion was shown in Section 3.2. The detailed distribution of the mean NOx concentration on the different cross-section A (x = 0.6 m), B (x = 2.0 m), C (x = 2.25 m) and D (y = 0.15 m) were illustrated in Fig. 9. It is worth mentioning that there was swirling flow structure in the lower region of the three sections, and the swirling flow could restrict the NOx dispersion and make the mean NOx concentration higher. However, with the coupling

Fig. 9. Distribution of mean NOx concentration on the sections in the curtain wall.

degradation, the mean NOx concentration was lower close to the swirling flow. Thus the flow did not influence the NOx distribution independently. The complex effects of the dispersion coupling with the degradation in sections A, B and C will be particularly discussed in Section 3.2. Compared with the distribution of the mean NOx concentration on the different cross-sections (Fig. 9), Fig. 10 shows a different picture regarding the formation of temperature in the glass curtain wall. In the 5

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pollutant concentration and the pollutants would be accumulated in the corner areas. However, the concentration dropped and the NOx concentration percentage declined by 20%. This indicated that the NOx removal progress coupling with the diffusion was obvious close to the swirling flow region. The values of the decreased NOx concentration percentage were significant as the other research (Chen & Chu, 2011; Chen & Liu, 2010). Based on the above discussion, the swirling flow structure and the TiO2 wall boundary condition must be considered together to describe the degradation and removal process. An obvious diffusion process existed close to the swirling flow region. Fig. 12 shows a detailed and interesting view that an obvious diffusion effect existed. Next, the results are further discussed. The distribution of vorticity in sections A, B and C is illustrated in Fig. 13. The distributions of vorticity in the three sections had the similar distribution, thus only the section A was discussed. In Fig. 13(a), close to the region where the two swirling flow structures appeared, on the dashed line ab, the value of vorticity was higher. Accompanied by the vorticity, the distributions of turbulence kinetic energy (k) on the vertical sections are illustrated in Fig. 14. The turbulence kinetic energy distributions in the three sections had the similar distribution, thus only the section A was discussed. In Fig. 14(a), at the corner, turbulence kinetic energy (k) kept a higher value. The region in which the higher value of the turbulence kinetic energy (k) existed was covering the region of the swirling flow structures. From the above discussion, the two swirling flow structures produced higher values of k, and the turbulence kinetic energy led to the higher diffusion coefficient. Then the higher diffusion coefficient leaded the lower NOx concentration with the degradation by the TiO2 wall. From the above discussion, the relationship was shown between the swirling flow and the distribution of NOx in the curtain wall (Fig. 15). The whole NOx removal process could be shown as the swirling flow generated the higher vorticity, as shown in Fig. 13. Next, the higher vorticity generated the higher fluctuation and the higher turbulence kinetic energy (k), as shown in Fig. 14. Lastly, the higher turbulence kinetic energy (k) generated the higher diffusion coefficient. Then coupling with the degradation by the TiO2 wall, the higher diffusion coefficient produced the distribution of the lower NOx concentration. Based on the above qualitative conclusion, quantitative relationships must be found between the fluctuation and the distribution of NOx concentration, such as shown in Fig. 15.

Fig. 10. Distribution of temperature on the sections in the curtain wall.

inlet region, the temperature was lower. The temperature at the inflow location was 0℃ and in most areas of the curtain wall the temperature also kept to 0℃. The temperature distribution was much different with the mean NOx concentration distribution. Fig. 10 shows that no direct linking existed between the mean NOx concentration and the temperature distribution. In this paper, the main theme was to discuss the law of the mean NOx concentration, so the temperature distribution is not particularly discussed in the following Sections. In this paper, the temperature of the indoor air was used to be 15℃, thus the curtain wall had the preheating function, and the temperature in the top area was higher. 3.2. Detailed statistical characteristics in the sections

3.3. Relation between the swirling flow fluctuation and NOx concentration As discussed in the Section 3.1, the conclusion shows that the swirling flow in the bottom of the curtain wall did not influence the NOx distribution independently, so the complex effects of these NOx dispersion couplings with the degradation in sections A, B and C must be particularly discussed. Fig. 11 shows the vertical distribution of velocities in sections A, B and C in the building glass curtain wall. The velocities in the three sections had the similar distribution, thus only the section A was discussed. In Fig. 11(a), the peak value of velocity in the sections was reproduced at the corner of the bottom of the curtain wall. In this area, the mean velocity was 1.5 m·s−1. However, close to the corner points, the swirling flow obviously existed and the mean velocity was close to 0 m·s-1. Intuitive thinking indicates that the corner generated swirling flow, and the swirling flow impeded the air flow. The swirling flow caused the flow cross-section to be smaller in the rectangular duct, so the value of mean velocity was larger based on the principle of continuity. Therefore the swirling flow directly affected the distribution of the mean velocity in the curtain wall. The distributions of the NOx in the vertical sections A, B and C are illustrated in Fig. 12. The distributions of the NOx in the three sections had the similar distribution, thus only the section A was discussed. With regard to the above discussion about the mean velocity distribution, one fact was clearly shown: that two swirling flow structures existed at points a and b in the corner areas, as shown in Fig. 12(a). The swirling flow impeded the air flow, thus the two swirling flow caused the NOx

With the mass conservation equation for an infinitely small region, the NOx removal over the entire length of the active surface shows that the NOx concentration is affect with the airflow in a power function with a negative exponent (Verbruggen et al., 2015; Yang, Zhang, & Zhao, 2004). Then in this paper, a power function with a negative exponent was assumed as:

CR ∝ Bf (e−P )

(15)

where B is coefficient, f (⋅) is one function, P is a parameter to describe the airflow in the building glass curtain wall, CR is the relative concentration (NOx concentration/0.00012 mol·m−3). To complete Eq. (15), twenty-seven points were selected on the vertical sections A, B and C to obtain the NOx concentration and airflow data for completing the data-fitting. The points were shown in Fig. 16. The points were set in the three lines (y = 0.05, y = 0.15 and y = 0.25 m) on each section. The z coordinate value was from 0.2 m to 0.4 m. The increment value was 0.1 m. At last, on each section, three points (y = 0.05 and z = 0.05 m) were set to verify the concentration law. The turbulence intensity should describe the flow fluctuation in the curtain wall, thus the airflow data was selected to be turbulence intensity based on the above discussion (Fig. 15). To complete Eq. (15), with the relative concentration and turbulence intensity data based on the points were set in the lines 6

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Fig. 11. Distribution of velocity in the sections of the curtain wall.

(y = 0.05 m) in the swirling flow on each section. With these data shown in Fig. 17, a new equation was given as:

CR = A + Be−I

where CR is the relative concentration, I is the turbulence intensity, A and B are the fit coefficients. In this paper, A was 0.85, and B was 0.1. The goodness of fit (R2) was larger than 0.96. To verify Eq. (16), the turbulence intensity data of the three verification points (shown in

(16)

Fig. 12. Distribution of NOx in the sections of the curtain wall. 7

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Fig. 13. Distribution of vorticity in the sections of the curtain wall.

Fig. 14. Distribution of turbulent kinetic energy (k) in the sections of the curtain wall.

CR = A + Be−I + Ce−2I

Fig. 16) were taken into Eq. (16). Comparing the simulation result and the relative concentration CR calculated with Eq. (16), the largest error was 8.69%. Based on the points were set in the lines (y = 0.15 m) on each section. The z coordinate value was from 0.2 m to 0.4 m. The increment value was 0.1 m. With these data shown in Fig. 18, a new equation was given as:

(17)

where CR was the relative concentration, I was the turbulence intensity, A, B and C were the fit coefficients. However, in this paper, the fit coefficient had no fixed value in different sections. A was between 1.0 and 3.0. B was between -10.0 and 0. C was between 4.0 and 9.0. From the above discussion, no constant coefficient existed on the different sections. Lastly, based on the points were set in the lines (y = 0.25 m) 8

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Fig. 15. Relationship between the swirling flow and the distribution of NOx.

Fig. 16. Schematic locations of the points.

on each section, there was no relation between the fluctuation turbulence intensity and the NOx concentration. Based on the above phenomena, the swirling flow affected the NOx dispersion directly. The three curtain regions could be divided into three parts by the swirling flow and identified as the region close to the curtain wall, the core flow region, and the swirling flow region. Fig. 19 shown the principle of NOx transport and removal affected by the swirling flow. In the region close to the wall, the swirling flow force was very weak to affect the NOx transport. Coupling with the wall degradation, NOx was mainly affected by the wall boundary condition, thus with the different boundary condition, there was no general law in the region close to the curtain wall. In the swirling flow region, the swirling flow generated the higher turbulence kinetic energy (k). The higher turbulence kinetic energy generated the higher diffusion coefficient to impel more NOx migration from the core flow to the wall. Therefore, more NOx could be degraded by the wall, and the swirling flow more obviously affected the distribution of NOx concentration. There was the particular coherent structure or swirling flow in one specific curtain, thus a determinate law existed between the concentration and turbulence intensity in different sections, such as the CR = A + Be−I . Lastly, in the core flow region, a more complicated law

Fig. 17. Fitted curves on the lines (y = 0.05 m).

existed. It was affected by the swirling flow, so the law had the same form for the specific flow in one curtain. However, the coefficient was also not general in different sections which was affected by the wall boundary. 4. Conclusion CFD was applied in researching the statistical characteristics of NOx migration from the outdoor environment to the indoor room through the photo-catalytic glass curtain wall. This paper showed the numeric CFD method could be used to simulate the degradation of the NOx on the curtain wall surface covered by TiO2. CFD had its merits in showing all the details of the flow and pollution in the photo-catalytic glass 9

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curtain wall. Based on the CFD method, the swirling flow and the distribution of NOx were shown. The CFD approach is therefore well suited for the design of the photo-catalytic glass curtain wall geometries once a good estimation of the kinetic parameters is known. In this article, based on the CFD approach, the effect of degradation on the photo-catalytic glass curtain wall with TiO2 was investigated: 1) First, the statistical characteristics of the flow motion and the degradation of NOx were conducted and simulated by the CFD simulation technology in the photo-catalytic glass curtain wall. The analytic approach was based on a mass transfer model combined with the L-H reaction kinetics and turbulence modelling. The threedimensional statistical distribution of NOx concentration showed that the NOx degradation rate could be 20%. A building with additional photo-catalytic glass curtain walls should degrade NOx well. With the huge surface areas of the buildings in one city, considerable amounts of NOx should be degraded by the photo-catalytic glass curtain walls. 2) Second, the CFD simulation results were studied to ensure that the three-dimensional statistical distribution of NOx concentration and the flow behavior had complex distributions in the glass curtain wall. In order to derive the parameters for designing and optimizing the photo-catalytic glass curtain structure, the numeric CFD approach could be used to research the law of the three-dimensional statistical distribution of NOx concentration in the glass curtain wall. This was based on the feasibility and advantages of the CFD method in accurately calculating the spatial variation of the flow rate, reaction rate and NOx concentration close to the curtain wall surfaces. The simulation results showed that the temperature had no obvious direct effect on the statistical distribution of NOx concentration, while the swirling flow produced a direct effect on the statistical distribution of NOx concentration. 3) Third, the complex relation between the NOx concentration dispersion and the swirling flow was shown in the glass curtain wall. From the discussion, the swirling flow generated the higher vorticity, and the higher vorticity generated the higher fluctuation and the higher turbulence kinetic energy. Next, the higher turbulence kinetic energy generated a higher diffusion coefficient. Lastly, the higher diffusion coefficient produced the distribution of NOx concentration. Thus the swirling flow affected the NOx concentration degradation, dispersion and removal in the curtain. 4) Based on the above qualitative conclusions, three regions could be identified as the region close to the curtain wall, the core flow region and the swirling flow region. A quantitative relationship was also found between the fluctuation and the NOx concentration. In the swirling flow region, the turbulence intensity of fluctuating flow yielded one specific connection on the nature of the behavior through the relative NOx concentration. The flow fluctuation could be described by the turbulence intensity in the curtain wall, then the relative NOx concentration and turbulence intensity followed CR = A + Be−I . CR is the relative concentration, I is the turbulence intensity, A and B are the determinate coefficients. In the other two regions, there were no general laws. 5) The principle of NOx transport affected by the swirling flow was given. In the swirling flow region, the swirling flow generated the higher diffusion coefficient. Therefore, the swirling flow affected the distribution of NOx concentration. Because there was a coherent structure or swirling flow for specific flow in the curtain, there was a determinate law between the concentration and turbulence intensity describing the flow. Coupling With the wall degradation, NOx was affected by the wall boundary condition, thus with the different boundary condition there was no general law in the region close to the curtain wall. In the core flow region, the law had the same form which was affected by the coherent structure or swirling flow for the specific flow in the curtain. However, the coefficient was different in the different sections affected by the peculiar boundary condition.

Fig. 18. Fitted curves in the lines (y = 0.15 m).

Fig. 19. The principle of NOx transport affected by the swirling flow.

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6) This research was an attempt to combine the photocatalytic process with a building including a curtain wall. This study will give a better understanding of how NOx pollutants can be reduced more effectively. This paper had made some progress, such as three regions could be identified to describe the law and producing swirling flow could more obviously reduce the NOx. However, there were limitations of the research and much work still needs to be done in the future. One experiment platform (the 1:1 curtain wall with the TiO2) needs to be set up to obtain more experimental data to research the progress of the photodegradation of NOx related to the light intensity. In addition, to obtain more detailed swirling flow and coherent structure transient information, Large Eddy Simulation (LES) method could be used to research the relation between the transient flow and the NOx diffusion.

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