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

Abstrakt

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.

Cytowania

0
CrossRef
0
Web of Science
0
Scopus

Informacje szczegółowe

Kategoria: Publikacja w czasopiśmie
Typ: artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w: Building Simulation nr 10, wydanie 2, strony 229 - 238,
ISSN: 1996-3599
Język: angielski
Rok wydania: 2017
Opis bibliograficzny: Arendt K., Krzaczek M., Tejchman J.: Influence of input data on airflow network accuracy in residential buildings with natural wind- and stack-driven ventilation// Building Simulation. -Vol. 10, iss. 2 (2017), s.229-238
DOI: 10.1007/s12273-016-0320-5
wyświetlono 18 razy
Meta Tagi