On the significance of the climate-dataset time resolution in characterising wind-driven rain and simultaneous wind pressure. Part II: directional analysis

Pérez-Bella, J.M. (Universidad de Zaragoza) ; Domínguez-Hernández, J. (Universidad de Zaragoza) ; Cano-Suñén, E. (Universidad de Zaragoza) ; Del Coz-Díaz, J.J. ; Álvarez Rabanal, F.P.

Resumen: Both semi-empirical methods and CFD simulations use real climate datasets as a basis for determining the building facade exposure to wind-driven rain and simultaneous wind pressure. The time resolution of these datasets and the number of variables considered (commonly rainfall intensity, wind speed and wind direction) determine the required calculation effort and the accuracy of the result. Omitting the wind direction, a former article (Part I of this research) has analysed the effect of this time resolution on two scalar exposure indices obtained by semi-empirical methods: driving rain index (aDRI) and driving-rain wind pressure (DRWP). However, the wind direction during precipitation events also causes significant exposure variations between possible facade orientations. Thus, it is also necessary to clarify the influence of the time resolution of the dataset, on the accuracy of the directional semi-empirical calculation of aDRI and DRWP. To meet this challenge, the article examines 10-min climate records collected between 2001 and 2016 at 6 Spanish locations, uses them to obtain hourly, daily, monthly and annual datasets, and analyses the accuracy of the directional exposure indices associated with each time resolution. The results show that a daily dataset would allow identifying the most exposed orientation with an error less than 45°. However, even the hourly datasets cause errors close to 10% in the exposure values identified on each facade orientation. Finally, adjustment relationships that allow estimating the maximum value of directional exposure from simple scalar indices are obtained.

Idioma: Inglés

DOI: 10.1007/s00477-017-1480-2

Año: 2017

Publicado en: STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 32 (2017), 1799 – 1815
ISSN: 1436-3240

Factor impacto JCR: 2.668 (2017)

Categ. JCR: STATISTICS & PROBABILITY rank: 9 / 123 = 0.073 (2017) - Q1 - T1
Categ. JCR: ENGINEERING, CIVIL rank: 22 / 127 = 0.173 (2017) - Q1 - T1
Categ. JCR: WATER RESOURCES rank: 19 / 90 = 0.211 (2017) - Q1 - T1
Categ. JCR: ENGINEERING, ENVIRONMENTAL rank: 22 / 50 = 0.44 (2017) - Q2 - T2
Categ. JCR: ENVIRONMENTAL SCIENCES rank: 87 / 241 = 0.361 (2017) - Q2 - T2

Factor impacto SCIMAGO: 1.096 - Environmental Science (miscellaneous) (Q1) - Environmental Engineering (Q1) - Water Science and Technology (Q1) - Safety, Risk, Reliability and Quality (Q1) - Environmental Chemistry (Q2)

Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/BIA2012-31609
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/FC-15-GRUPIN14-004

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Area Ingeniería Construcción (Dpto. Ingeniería Mecánica)