# A Method toward Real-Time CFD Modeling for Natural Ventilation

^{*}

## Abstract

**:**

## 1. Introduction

_{2}concentration and air temperature [4] as feedback parameters. The link between the windows’ opening angle and the controlling parameters is the control algorithm, which is currently rule-based. Each feedback parameter is usually measured in each zone by one sensor positioned near a wall due to the limitation of installation. The local air temperature variation within a zone is not yet considered to identify the ideal set point temperature for the zone. Therefore, the state-of-art rule-based strategy fails to control natural ventilation at multiple spatial scales, e.g., from occupant zone to the whole building, due to the limited number and location of sensors [5,6]. Another drawback of the sensor-based control is the neglect of the natural ventilation rate as a feedback parameter. Unlike mechanically ventilated buildings, natural ventilation rate is very difficult to measure [7]. The current control strategy might be capable of maintaining the indoor CO

_{2}level, the rooms might still be excessively or insufficiently ventilated, which often occurs in naturally ventilated buildings [8]. Alternatively, the indoor air speed might replace natural ventilation rate to supplement the feedback parameters. Although the air speed can be monitored in a room, it is often impossible to locate the anemometer in the occupied zone. Unlike air temperature, the air speed usually has a much stronger gradient in naturally ventilated rooms. The air speed measured outside the occupied zone might provide misleading feedback to windows operation. Natural ventilation rate, local air temperature, and velocity distribution at a higher spatial resolution are needed to leverage the state-of-the-art natural ventilation control to implement more complex control algorithms at multiple spatial scales [5]. The high spatial resolution data includes air temperatures and velocities in the occupied zones. A recognized solution towards achieving high spatial resolution data is to apply computational fluid dynamics (CFD) modeling to buildings [6,7,9]. CFD modeling as a physics-based tool can incorporate sensor data for real-time performance assessment and fault detection [10,11]. Moreover, CFD modeling can incorporate predictions using weather forecasts to leverage model predictive and adaptive control [12].

## 2. The Concept of Real-Time CFD

## 3. The Real-Time CFD Modeling Framework

#### 3.1. Geometry, Domain, and Mesh

^{2}and a building height of about 9.63 m above ground level. The basement level contains a kitchen, two bathrooms, and a large meeting room connected to the solar chimney. A reception area, director’s office, and administration rooms are located on the first floor, while the second floor contains a meeting room, private phone booth, and large open office space for researchers. The third floor accommodates a private office, recreational lounge area, and laboratory space.

_{2}concentrations and indoor air temperatures. The windows are also programmed for cross-ventilation and night flushing during the cooling season. The automated upper panels provide the primary openings for ventilating the building. The opening angle of these automated panels can be adjusted from 0° to about 55° through a chain actuator and motor (WindowMaster A/S, Vedbæk, Denmark). The lower panels are tilt and turn windows that can be operated manually. They tilt up to a maximum of 25° from the bottom hinge and can be opened 90° when using the vertical hinge. Seven center-pivot hinged skylights (Figure 1b) sit on the roof with opening angles ranging from 0° to about 25°. Both window and skylight glazing panels use triple insulated glass with two 18 mm air cavities filled with a gas mixture of 90% argon and 10% air.

^{−2}·k

^{−1}and a solar transmittance of 0.78. The other two sides of the chimney (north and west) are made from reused bricks that have an absorption factor of 0.86 and that sit below the cornice of the house. A structural steel frame supports the glass and connects to the structure of the house. The structural and glazing material properties are summarized in Table 1 below.

#### 3.2. Mathematical and Physical Models

_{i}is mean air velocity component in the x

_{i}direction (m·s

^{−1}), x

_{i}is the Cartesian coordinate (m), ρ is density of air (kg·m

^{−3}), P is air pressure (Pa), ν is the kinematic viscosity of air (m

^{2}·s

^{−1}), T is air temperature (K), T

_{0}is reference temperature (K), g

_{i}is the gravity component in x

_{i}direction (m·s

^{−2}), and S

_{i}is the other forces or source terms for momentum. The Reynolds stress term is approximated by

_{t}is the turbulent dynamic viscosity of air (kg·m

^{−1}·s

^{−1}) and k is the turbulent kinetic energy. In order to solve the above equations, transport equations for k are needed. In this work, the standard k-epsilon turbulence model [26] is chosen to close the RANS equations.

^{2}·s

^{−1}), c

_{p}is specific heat capacity (J·kg

^{−1}·K

^{−1}), and S

_{e}is the source term. The fluctuating term is approximated by

_{t}is the turbulent diffusivity (m

^{2}·s

^{−1}). The q

_{r}is radiative heat flux. The P-1 radiation model [27] is used to calculate q

_{r},

#### 3.3. Real-Time Boundary Conditions

#### 3.4. Data Assimilation

_{obs}is the observed velocity; C

_{nud}is the nudging coefficient, which is the reciprocal of a time scale taken as 0.02 based on a sensitivity analysis; and W

_{nug}is the Cressman type spatial weighting function [31]. The nudging force term is included at the observation points, which are in general not mesh points. The interpolation of the force term is done by smooth and radial drop-off at a rate of e

^{−r/R}, where r is the radial distance from the observation point and the choice of R allows a smooth drop-off in influence over five grid points and avoids overlap in the areas of influence from other observation points. The nudging is applied after the convergence of initial flow and temperature calculations. Then CFD simulations run more iterations to achieve convergence with data assimilation.

#### 3.5. Workflow of the Modeling Framework

## 4. Proof of Concept

^{−1}to 37.8 h

^{−1}and the main influence on the ACH in such a single zone is the outdoor wind speed and direction. The first floor waiting area and open office space on the second level are connected by a staircase that allows for multi-zone ventilation to form. The ACH ranges from 12.8 h

^{−1}to 94.3 h

^{−1}(Figure 5c). The high ventilation rates occur at 4:00 p.m. when the wind direction favors the natural ventilation through the windows on the northern facade, although the wind speed is lower than that at 1:00 p.m. This indicates the significant impact of wind directions on natural ventilation rates. The normalized ventilation rate (Q/AU

_{ref}) at 4:00 p.m. is 0.11 for the second floor. The reported value for rooms with similar wall-to-window ratios (~10%) is 0.46 in literature [36]. The difference might be due to the fact that the authors did not consider the detailed window opening angles in their experiments. Figure 5d shows the predicted ACH for the rooms on the third floor. For the southern room, ACH is generally influenced by wind speeds. Whereas, the ACH for the northern room is more influenced by wind directions.

^{−1}. During the day, the indoor and outdoor air temperature difference is not significant in the entire house. Although the rooms on the second and third floors have large roof areas, the indoor air temperature is not higher than the other rooms. The reason is that the air circulation along the path from windows through the staircase and up to skylights is able to maintain the indoor air temperature cooler than the outdoor air.

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Cao, X.; Dai, X.; Liu, J. Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy Build.
**2016**, 128, 198–213. [Google Scholar] [CrossRef] - Torcellini, P.A. Zero Energy Buildings: A Critical Look at the Definition; National Renewable Energy Laboratory: Golden, CO, USA, 2006. [Google Scholar]
- EPBD. Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of Buildings (recast). Off. J. Eur. Union
**2010**, 18, 6. [Google Scholar] - Schulze, T.; Eicker, U. Controlled natural ventilation for energy efficient buildings. Energy Build.
**2013**, 56, 221–232. [Google Scholar] [CrossRef] - Wu, W.; Zhai, J.; Zhang, G.; Nielsen, P.V. Evaluation of methods for determining air exchange rate in a naturally ventilated dairy cattle building with large openings using computational fluid dynamics (CFD). Atmos. Environ.
**2012**, 63, 179–188. [Google Scholar] [CrossRef] - Wu, W.; Zhang, G.; Kai, P. Ammonia and methane emissions from two naturally ventilated dairy cattle buildings and the influence of climatic factors on ammonia emissions. Atmos. Environ.
**2012**, 61, 232–243. [Google Scholar] [CrossRef] - Zhou, Y.; Hiyama, K.; Kato, S.; Zhang, W. Study on Statistical Prediction and Design Method for Indoor Thermal Environment. J. Asian Archit. Build. Eng.
**2014**, 13, 255–262. [Google Scholar] [CrossRef] [Green Version] - Etheridge, D. A perspective on fifty years of natural ventilation research. Build. Environ.
**2015**, 91, 51–60. [Google Scholar] [CrossRef] - Chen, Q.; Lee, K.; Mazumdar, S.; Poussou, S.; Wang, L.; Wang, M.; Zhang, Z. Ventilation performance prediction for buildings: Model assessment. Build. Environ.
**2010**, 45, 295–303. [Google Scholar] [CrossRef] [Green Version] - Pang, X.; Wetter, M.; Bhattacharya, P.; Haves, P. A framework for simulation-based real-time whole building performance assessment. Build. Environ.
**2012**, 54, 100–108. [Google Scholar] [CrossRef] [Green Version] - Dong, B.; O’Neill, Z.; Li, Z.; Luo, D.; Madhusudana, S.; Ahuja, S.; Bailey, T. An integrated infrastructure for real-time building energy modeling and fault detection and diagnostics. Proc. SimBuild
**2012**, 5, 448–455. [Google Scholar] - Afram, A.; Janabi-Sharifi, F. Theory and applications of HVAC control systems—A review of model predictive control (MPC). Build. Environ.
**2014**, 72, 343–355. [Google Scholar] [CrossRef] - Muhsin, F.; Yusoff, W.F.M.; Mohamed, M.F.; Sapian, A.R. CFD modeling of natural ventilation in a void connected to the living units of multi-storey housing for thermal comfort. Energy Build.
**2017**, 144, 1–16. [Google Scholar] [CrossRef] - Wang, B.; Zhao, B.; Chen, C. A simplified methodology for the prediction of mean air velocity and particle concentration in isolation rooms with downward ventilation systems. Build. Environ.
**2010**, 45, 1847–1853. [Google Scholar] [CrossRef] - Zuo, W.; Chen, Q. Fast and informative flow simulations in a building by using fast fluid dynamics model on graphics processing unit. Build. Environ.
**2010**, 45, 747–757. [Google Scholar] [CrossRef] - Wang, Q.; Pan, Y.; Zhu, M.; Huang, Z.; Tian, W.; Zuo, W.; Han, X.; Xu, P. A state-space method for real-time transient simulation of indoor airflow. Build. Environ.
**2017**, 126, 184–194. [Google Scholar] [CrossRef] - Jin, M.; Liu, W.; Chen, Q. Simulating buoyancy-driven airflow in buildings by coarse-grid fast fluid dynamics. Build. Environ.
**2015**, 85, 144–152. [Google Scholar] [CrossRef] [Green Version] - Wang, H.; Zhai, Z. Application of coarse-grid computational fluid dynamics on indoor environment modeling: Optimizing the trade-off between grid resolution and simulation accuracy. HVAC&R Res.
**2012**, 18, 915–933. [Google Scholar] [CrossRef] - Gilani, S.; Montazeri, H.; Blocken, B. CFD simulation of stratified indoor environment in displacement ventilation: Validation and sensitivity analysis. Build. Environ.
**2016**, 95, 299–313. [Google Scholar] [CrossRef] - Ramponi, R.; Blocken, B. CFD simulation of cross-ventilation for a generic isolated building: Impact of computational parameters. Build. Environ.
**2012**, 53, 34–48. [Google Scholar] [CrossRef] [Green Version] - Lal, S. Experimental, CFD simulation and parametric studies on modified solar chimney for building ventilation. Appl. Sol. Energy
**2014**, 50, 37–43. [Google Scholar] [CrossRef] - ASHRAE. ANSI/ASHRAE Standard 55-2017, Thermal Environmental Conditions for Human Occupancy; ASHRAE: Atlanta, GA, USA, 2017. [Google Scholar]
- Tsangrassoulis, A.; Santamouris, M.; Asimakopoulos, D.N. On the air flow and radiation transfer through partly covered external building openings. Sol. Energy
**1997**, 61, 355–367. [Google Scholar] [CrossRef] - Tominaga, Y.; Mochida, A.; Yoshie, R.; Kataoka, H.; Nozu, T.; Yoshikawa, M.; Shirasawa, T. AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings. J. Wind Eng. Ind. Aerodyn.
**2008**, 96, 1749–1761. [Google Scholar] [CrossRef] - ANSYS, Ltd. Fluent Help Release 17.0.; ANSYS Inc.: Canonsburg, PA, USA, 2017. [Google Scholar]
- Launder, B.E.; Spalding, D.B. The numerical computation of turbulent flows. Comput. Methods Appl. Mech. Eng.
**1974**, 3, 269–289. [Google Scholar] [CrossRef] - Siegel, R.; Howell, J.R. Thermal Radiation Heat Transfer; Hemisphere Publishing Corp.: Washington, DC, USA, 1992. [Google Scholar]
- Richards, P.J.; Norris, S.E. Appropriate boundary conditions for computational wind engineering models revisited. J. Wind Eng. Ind. Aerodyn.
**2011**, 99, 257–266. [Google Scholar] [CrossRef] - Menter, F.; Esch, T. Elements of Industrial Heat Transfer Prediction. In Proceedings of the 16th Brazilian Congress of Mechanical Engineering (COBEM), Uberlândia, Brazil, 26–30 November 2001. [Google Scholar]
- Yamada, T.; Koike, K. Downscaling mesoscale meteorological models for computational wind engineering applications. J. Wind Eng. Ind. Aerodyn.
**2011**, 99, 199–216. [Google Scholar] [CrossRef] - Cressman, G.P. An Operational Objective Analysis System. Mon. Weather Rev.
**1959**, 87, 367–374. [Google Scholar] [CrossRef] - Larsen, T.S.; Heiselberg, P. Single-sided natural ventilation driven by wind pressure and temperature difference. Energy Build.
**2008**, 40, 1031–1040. [Google Scholar] [CrossRef] - Patki, N.; Wedge, R.; Veeramachaneni, K. The Synthetic Data Vault. In Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Montreal, QC, Canada, 17–19 October 2016; pp. 399–410. [Google Scholar]
- ASHRAE. ASHRAE Standard 55-2013, Thermal Environmental Conditions for Human Occupancy; American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.: Atlanta, GA, USA, 2013. [Google Scholar]
- Crawley, D.B.; Lawrie, L.K.; Winkelmann, F.C.; Buhl, W.F.; Huang, Y.J.; Pedersen, C.O.; Strand, R.K.; Liesen, R.J.; Fisher, D.E.; Witte, M.J.; et al. EnergyPlus: Creating a new-generation building energy simulation program. Energy Build.
**2001**, 33, 319–331. [Google Scholar] [CrossRef] - Karava, P.; Stathopoulos, T.; Athienitis, A.K. Airflow assessment in cross-ventilated buildings with operable façade elements. Build. Environ.
**2011**, 46, 266–279. [Google Scholar] [CrossRef]

**Figure 1.**Natural ventilation system for a zero-energy building including windows and skylights: (

**a**) Stack ventilation through staircase; (

**b**) Solar chimney for buoyancy driven ventilation.

**Figure 2.**Diagram of calculation procedure for real-time computational fluid dynamics (CFD) modeling.

**Figure 3.**(

**a**) Geometry and mesh of the real-time CFD model; (

**b**) octagonal domain; (

**c**) mixed mesh; (

**d**) air speed profiles of three mesh numbers; (

**e**) recirculation length of the circulation zone on the downwind side of the building for three mesh numbers.

**Figure 6.**Solar irradiation, and outdoor air, façade, floor and roof temperature, and indoor air temperature for different rooms for 24 h.

**Figure 7.**Comparison of observed, simulated, and real-time air speeds at two sensor positions during 24 h.

Material | Thickness (mm) | U-Value (W·m ^{−2}·K^{−1}) | Density (kg·m ^{−3}) | Specific Heat (J·kg ^{−1}·K^{−1}) | Absorptivity | Transmissivity | |
---|---|---|---|---|---|---|---|

Visible | IR | ||||||

External wall | 370 | 0.20 | 261 | 1150 | 0.26 | 0.90 | - |

Roof | 580 | 0.15 | 133 | 1069 | 0.81 | 0.92 | - |

Basement floor | 210 | 0.35 | 1017 | 810 | 0.81 | 0.92 | - |

Basement wall below grade | 400 | 0.17 | 1190 | 778 | 0.26 | 0.90 | - |

Basement wall above grade | 295 | 0.22 | 1666 | 778 | 0.26 | 0.90 | - |

Internal floors | 100 | 0.70 | 1003 | 808 | 0.81 | 0.92 | - |

Partition walls | 265 | 0.25 | 490 | 1181 | 0.26 | 0.90 | - |

Window glass (double pane) | - | 0.37 | 2500 | 840 | 0.49 | 0.49 | 0.3 |

Skylight glass (triple pane) | - | 0.23 | 2500 | 840 | 0.4 | 0.4 | 0.29 |

Door glass | - | 1.3 | 2500 | 840 | 0.13 | 0.13 | 0.78 |

Chimney glass | - | 1.3 | 2500 | 840 | 0.13 | 0.13 | 0.78 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Wu, W.; Wang, B.; Malkawi, A.; Yoon, N.; Sehovic, Z.; Yan, B.
A Method toward Real-Time CFD Modeling for Natural Ventilation. *Fluids* **2018**, *3*, 101.
https://doi.org/10.3390/fluids3040101

**AMA Style**

Wu W, Wang B, Malkawi A, Yoon N, Sehovic Z, Yan B.
A Method toward Real-Time CFD Modeling for Natural Ventilation. *Fluids*. 2018; 3(4):101.
https://doi.org/10.3390/fluids3040101

**Chicago/Turabian Style**

Wu, Wentao, Bing Wang, Ali Malkawi, Nari Yoon, Zlatan Sehovic, and Bin Yan.
2018. "A Method toward Real-Time CFD Modeling for Natural Ventilation" *Fluids* 3, no. 4: 101.
https://doi.org/10.3390/fluids3040101