Influence of input data on airflow network accuracy in residential buildings with natural wind- and stack-driven ventilation

Krzysztof Arendt, Marek Krzaczek, Jacek Tejchman

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

The airflow network (AFN) modeling approach provides an attractive balance between the accuracy and computational demand for naturally ventilated buildings. Its accuracy depends on input parameters such as wind pressure and opening discharge coefficients. In most cases, these parameters are obtained from secondary sources which are solely representative for very simplified buildings, i.e. for buildings without facade details. Although studies comparing wind pressure coefficients or discharge coefficients from different sources exist, the knowledge regarding the effect of input data on AFN is still poor. In this paper, the influence of wind pressure data on the accuracy of a coupled AFN-BES model for a real building with natural wind- and stack-driven ventilation was analyzed. The results of 8 computation cases with different wind pressure data from secondary sources were compared with the measured data. Both the indoor temperatures and the airflow were taken into account. The outcomes indicated that the source of wind pressure data had a significant influence on the model performance.
OriginalsprogEngelsk
TidsskriftBuilding Simulation
Vol/bind10
Udgave nummer2
Sider (fra-til)229-238
ISSN1996-3599
DOI
StatusUdgivet - 2017

Fingeraftryk

Ventilation
Facades
Temperature

Citer dette

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title = "Influence of input data on airflow network accuracy in residential buildings with natural wind- and stack-driven ventilation",
abstract = "The airflow network (AFN) modeling approach provides an attractive balance between the accuracy and computational demand for naturally ventilated buildings. Its accuracy depends on input parameters such as wind pressure and opening discharge coefficients. In most cases, these parameters are obtained from secondary sources which are solely representative for very simplified buildings, i.e. for buildings without facade details. Although studies comparing wind pressure coefficients or discharge coefficients from different sources exist, the knowledge regarding the effect of input data on AFN is still poor. In this paper, the influence of wind pressure data on the accuracy of a coupled AFN-BES model for a real building with natural wind- and stack-driven ventilation was analyzed. The results of 8 computation cases with different wind pressure data from secondary sources were compared with the measured data. Both the indoor temperatures and the airflow were taken into account. The outcomes indicated that the source of wind pressure data had a significant influence on the model performance.",
keywords = "airflow network, natural ventilation, numerical calculations, pivoted window, wind pressure",
author = "Krzysztof Arendt and Marek Krzaczek and Jacek Tejchman",
year = "2017",
doi = "10.1007/s12273-016-0320-5",
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journal = "Building Simulation",
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publisher = "Tsinghua University Press",
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Influence of input data on airflow network accuracy in residential buildings with natural wind- and stack-driven ventilation. / Arendt, Krzysztof ; Krzaczek, Marek; Tejchman, Jacek.

I: Building Simulation, Bind 10, Nr. 2, 2017, s. 229-238.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Influence of input data on airflow network accuracy in residential buildings with natural wind- and stack-driven ventilation

AU - Arendt, Krzysztof

AU - Krzaczek, Marek

AU - Tejchman, Jacek

PY - 2017

Y1 - 2017

N2 - The airflow network (AFN) modeling approach provides an attractive balance between the accuracy and computational demand for naturally ventilated buildings. Its accuracy depends on input parameters such as wind pressure and opening discharge coefficients. In most cases, these parameters are obtained from secondary sources which are solely representative for very simplified buildings, i.e. for buildings without facade details. Although studies comparing wind pressure coefficients or discharge coefficients from different sources exist, the knowledge regarding the effect of input data on AFN is still poor. In this paper, the influence of wind pressure data on the accuracy of a coupled AFN-BES model for a real building with natural wind- and stack-driven ventilation was analyzed. The results of 8 computation cases with different wind pressure data from secondary sources were compared with the measured data. Both the indoor temperatures and the airflow were taken into account. The outcomes indicated that the source of wind pressure data had a significant influence on the model performance.

AB - The airflow network (AFN) modeling approach provides an attractive balance between the accuracy and computational demand for naturally ventilated buildings. Its accuracy depends on input parameters such as wind pressure and opening discharge coefficients. In most cases, these parameters are obtained from secondary sources which are solely representative for very simplified buildings, i.e. for buildings without facade details. Although studies comparing wind pressure coefficients or discharge coefficients from different sources exist, the knowledge regarding the effect of input data on AFN is still poor. In this paper, the influence of wind pressure data on the accuracy of a coupled AFN-BES model for a real building with natural wind- and stack-driven ventilation was analyzed. The results of 8 computation cases with different wind pressure data from secondary sources were compared with the measured data. Both the indoor temperatures and the airflow were taken into account. The outcomes indicated that the source of wind pressure data had a significant influence on the model performance.

KW - airflow network

KW - natural ventilation

KW - numerical calculations

KW - pivoted window

KW - wind pressure

U2 - 10.1007/s12273-016-0320-5

DO - 10.1007/s12273-016-0320-5

M3 - Journal article

VL - 10

SP - 229

EP - 238

JO - Building Simulation

JF - Building Simulation

SN - 1996-3599

IS - 2

ER -