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Defining the Influence Region in neighborhood-scale CFD simulations for natural ventilation design


  • Tong, Zheming
  • Chen, Yujiao
  • Malkawi, Ali


Natural ventilation is one of the most important design options for green buildings, which reduces energy use and improves thermal comfort. Computational Fluid Dynamics (CFD) simulations have been used increasingly for natural ventilation design in urban neighborhoods. The accuracy of such simulations relies largely on how the CFD domain is chosen. In the domain, we define the Influence Region as the area where the surrounding buildings must be modeled explicitly to predict the ventilation flow rate accurately. This study presents the early efforts to determine the adequate size of the Influence Region in the CFD domain using a coupled indoor-outdoor CFD simulation, in which the air change rate (ACH) no longer varies noticeably with increasing number of surrounding obstacles. Convergence charts of ACH as a function of an increasing number of surrounding building layers are generated using various urban parameters (e.g., wind condition, aspect ratio, building height relative to surroundings, downstream obstacles, and non-idealized surroundings). Our analysis demonstrated that only including the adjacent layer of surrounding obstacles is not sufficient for predicting correctly the ACH because of the artificial channeling effect between buildings. For both normal and oblique wind directions, three layers of surroundings are required for regular street canyons with an aspect ratio H/W=1. In the case of wide canyons (H/W=1/3), two layers of surroundings are needed because there is less flow interference between upstream and downstream obstacles. For the urban configuration, where the target building is significantly taller than nearby structures, the ACH on higher floors does not vary much with increasing amount of surroundings, which significantly reduces the required number of buildings in the Influence Region. In addition, buildings at the side and downstream of the target building can be moderately excluded in the Influence Region as long as the most adjacent downstream layer of obstacles is modeled. A real urban configuration with non-uniform spacing among buildings is evaluated. We showed that the required size of the Influence Region that is derived from uniform building arrays still generally applies to non-idealized landscapes. This study demonstrates the importance of assessing the sensitivity of the selected Influence Region in CFD simulations to reduce unintended modeling errors and computing expense.

Suggested Citation

  • Tong, Zheming & Chen, Yujiao & Malkawi, Ali, 2016. "Defining the Influence Region in neighborhood-scale CFD simulations for natural ventilation design," Applied Energy, Elsevier, vol. 182(C), pages 625-633.
  • Handle: RePEc:eee:appene:v:182:y:2016:i:c:p:625-633
    DOI: 10.1016/j.apenergy.2016.08.098

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    Cited by:

    1. Alberto Meiss & Miguel A. Padilla-Marcos & Jesús Feijó-Muñoz, 2017. "Methodology Applied to the Evaluation of Natural Ventilation in Residential Building Retrofits: A Case Study," Energies, MDPI, Open Access Journal, vol. 10(4), pages 1-19, April.
    2. Mao, Ning & Hao, Jingyu & He, Tianbiao & Song, Mengjie & Xu, Yingjie & Deng, Shiming, 2019. "PMV-based dynamic optimization of energy consumption for a residential task/ambient air conditioning system in different climate zones," Renewable Energy, Elsevier, vol. 142(C), pages 41-54.
    3. Chen, Yujiao & Tong, Zheming & Wu, Wentao & Samuelson, Holly & Malkawi, Ali & Norford, Leslie, 2019. "Achieving natural ventilation potential in practice: Control schemes and levels of automation," Applied Energy, Elsevier, vol. 235(C), pages 1141-1152.
    4. Fan, Yuling & Xia, Xiaohua, 2018. "Building retrofit optimization models using notch test data considering energy performance certificate compliance," Applied Energy, Elsevier, vol. 228(C), pages 2140-2152.
    5. Zheming Tong & Zhewu Cheng & Shuiguang Tong, 2019. "Preliminary Design of Multistage Radial Turbines Based on Rotor Loss Characteristics under Variable Operating Conditions," Energies, MDPI, Open Access Journal, vol. 12(13), pages 1-15, July.
    6. Tong, Shuiguang & Cheng, Zhewu & Cong, Feiyun & Tong, Zheming & Zhang, Yidong, 2018. "Developing a grid-connected power optimization strategy for the integration of wind power with low-temperature adiabatic compressed air energy storage," Renewable Energy, Elsevier, vol. 125(C), pages 73-86.
    7. Nutkiewicz, Alex & Jain, Rishee K. & Bardhan, Ronita, 2018. "Energy modeling of urban informal settlement redevelopment: Exploring design parameters for optimal thermal comfort in Dharavi, Mumbai, India," Applied Energy, Elsevier, vol. 231(C), pages 433-445.
    8. Tong, Zheming & Chen, Yujiao & Malkawi, Ali, 2017. "Estimating natural ventilation potential for high-rise buildings considering boundary layer meteorology," Applied Energy, Elsevier, vol. 193(C), pages 276-286.
    9. Mao, Ning & Hao, Jingyu & Cui, Borui & Li, Yuxing & Song, Mengjie & Xu, Yingjie & Deng, Shiming, 2018. "Energy performance of a bedroom task/ambient air conditioning (TAC) system applied in different climate zones of China," Energy, Elsevier, vol. 159(C), pages 724-736.
    10. Mao, Ning & Song, Mengjie & Pan, Dongmei & Deng, Shiming, 2018. "Comparative studies on using RSM and TOPSIS methods to optimize residential air conditioning systems," Energy, Elsevier, vol. 144(C), pages 98-109.
    11. Abolfazl Heidari & Sadra Sahebzadeh & Zahra Dalvand, 2017. "Natural Ventilation in Vernacular Architecture of Sistan, Iran; Classification and CFD Study of Compound Rooms," Sustainability, MDPI, Open Access Journal, vol. 9(6), pages 1-19, June.


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