Data-driven model for cross ventilation potential in high-density cities based on coupled CFD simulation and machine learning

Ding, Chao; Lam, Khee Poh

doi:10.1016/j.buildenv.2019.106394

Title: Data-driven model for cross ventilation potential in high-density cities based on coupled CFD simulation and machine learning

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Abstract

Effective urban ventilation through decent urban planning and building design can alleviate the deterioration of the urban built environment. Furthermore, natural ventilation requirements and guidelines in current building codes and standards are either qualitative or quantitative but subject to an absolute indoor airspeed threshold without considering the outdoor wind environment. To fill this gap, this paper develops an urban-scale coupled indoor and outdoor computational fluid dynamics (CFD) model and defines a novel ventilation index to assess natural ventilation potential. The index considers wind environments of both indoor and outdoor spaces.

Authors:

Ding, Chao ^[1]; Lam, Khee Poh ^[2]

Carnegie Mellon Univ., Pittsburgh, PA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Carnegie Mellon Univ., Pittsburgh, PA (United States); National Univ. of Singapore (Singapore)

Publication Date:: Thu Sep 05 00:00:00 EDT 2019

Research Org.:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Org.:: USDOE Office of Science (SC)

OSTI Identifier:: 1564080

Grant/Contract Number:: AC02-05CH11231

Resource Type:: Accepted Manuscript

Journal Name:: Building and Environment

Additional Journal Information:: Journal Volume: 165; Journal Issue: C; Journal ID: ISSN 0360-1323

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Urban ventilation; Coupled CFD simulation; High-density city; Machine learning; Data-driven model; Early design support

Citation Formats


                    Ding, Chao, and Lam, Khee Poh. Data-driven model for cross ventilation potential in high-density cities based on coupled CFD simulation and machine learning.  United States: N. p., 2019. 
Web.  doi:10.1016/j.buildenv.2019.106394.

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                    Ding, Chao, & Lam, Khee Poh. Data-driven model for cross ventilation potential in high-density cities based on coupled CFD simulation and machine learning.  United States.  https://doi.org/10.1016/j.buildenv.2019.106394

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                    Ding, Chao, and Lam, Khee Poh. Thu .  
"Data-driven model for cross ventilation potential in high-density cities based on coupled CFD simulation and machine learning".  United States.  https://doi.org/10.1016/j.buildenv.2019.106394.  https://www.osti.gov/servlets/purl/1564080.

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@article{osti_1564080,

  title        = {Data-driven model for cross ventilation potential in high-density cities based on coupled CFD simulation and machine learning},

  author       = {Ding, Chao and Lam, Khee Poh},

  abstractNote = {Effective urban ventilation through decent urban planning and building design can alleviate the deterioration of the urban built environment. Furthermore, natural ventilation requirements and guidelines in current building codes and standards are either qualitative or quantitative but subject to an absolute indoor airspeed threshold without considering the outdoor wind environment. To fill this gap, this paper develops an urban-scale coupled indoor and outdoor computational fluid dynamics (CFD) model and defines a novel ventilation index to assess natural ventilation potential. The index considers wind environments of both indoor and outdoor spaces.},

  doi          = {10.1016/j.buildenv.2019.106394},

  journal      = {Building and Environment},

  number       = C,

  volume       = 165,

  place        = {United States},

  year         = {Thu Sep 05 00:00:00 EDT 2019},

  month        = {Thu Sep 05 00:00:00 EDT 2019}

}

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