Guidelines for the required time resolution of meteorological input data for wind-driven rain calculations on buildings

被引:36
|
作者
Blocken, B. [1 ]
Carmeliet, J. [2 ,3 ]
机构
[1] Tech Univ Eindhoven, NL-5600 MB Eindhoven, Netherlands
[2] ETH Honggerberg, Swiss Fed Inst Technol ETHZ, Chair Bldg Phys, CH-8093 Zurich, Switzerland
[3] Lab Bldg Technol, Swiss Fed Labs Mat Testing & Res, Empa, CH-8600 Dubendorf, Switzerland
关键词
wind-driven rain; driving rain; wind flow; building; Computational Fluid Dynamics (CFD); sample size; time resolution; data averaging; climate data;
D O I
10.1016/j.jweia.2008.02.008
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
An important question in wind-driven rain (WDR) calculations on buildings, either with semi-empirical formulae or with Computational Fluid Dynamics (CFD), concerns the required time resolution of the meteorological input data: wind speed, wind direction and horizontal rainfall intensity. Earlier work has indicated that the use of 10 min input data can provide accurate results, while the use or arithmetically averaged hourly data can yield significant underestimations in the calculated WDR amounts. This paper builds further on this earlier work by providing a detailed investigation of the parameters that determine the required time resolution for WDR calculations on building facades: (1) the averaging technique, (2) the building geometry and the position at the building facade and (3) the type of the rain event. It is shown that all three parameters can have a large influence on the required time resolution. Depending on these parameters, hourly or even daily wind and rain input data could provide accurate results, while in other situations they can lead to very large errors. Finally, guidelines for the required time resolution as a function of the influencing parameters are provided. (c) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码:621 / 639
页数:19
相关论文
共 49 条
  • [41] Projected changes to risk of wind-driven rain on buildings in Canada under +0.5 °C to +3.5 °C global warming above the recent period
    Jeong, Dae Il
    Cannon, Alex J.
    CLIMATE RISK MANAGEMENT, 2020, 30
  • [42] GIS-based application to calculate directional wind-driven rain exposure on residential buildings at an urban scale: The case study of Zaragoza, Spain
    Cano-Sunen, Enrique
    Ruiz-Varona, Ana
    Perez-Bella, Jose M.
    BUILDING AND ENVIRONMENT, 2024, 249
  • [43] Combined Use of Wind-Driven Rain Load and Potential Evaporation to Evaluate Moisture Damage Risk: Case Study on the Parliament Buildings in Ottawa, Canada
    Kubilay, Aytac
    Bourcet, John
    Gravel, Jessica
    Zhou, Xiaohai
    Moore, Travis, V
    Lacasse, Michael A.
    Carmeliet, Jan
    Derome, Dominique
    BUILDINGS, 2021, 11 (10)
  • [44] Accuracy of semi-empirical models for wind-driven rain using different data processing methods for wind velocity and direction (vol 237, 110300, 2023)
    Lu, Biao
    Fang, Jinzhong
    Li, Yunjie
    Zhang, Huibo
    Gao, Yafeng
    Feng, Chi
    BUILDING AND ENVIRONMENT, 2023, 244
  • [45] Influence of input data on airflow network accuracy in residential buildings with natural wind- and stack-driven ventilation
    Krzysztof Arendt
    Marek Krzaczek
    Jacek Tejchman
    Building Simulation, 2017, 10 : 229 - 238
  • [46] Influence of input data on airflow network accuracy in residential buildings with natural wind- and stack-driven ventilation
    Arendt, Krzysztof
    Krzaczek, Marek
    Tejchman, Jacek
    BUILDING SIMULATION, 2017, 10 (02) : 229 - 238
  • [47] Impact of wind-driven rain on historic brick wall buildings in a moderately cold and humid climate: Numerical analyses of mould growth risk, indoor climate and energy consumption
    Abuku, Masaru
    Janssen, Hans
    Roels, Staf
    ENERGY AND BUILDINGS, 2009, 41 (01) : 101 - 110
  • [48] Erosion Rate of the Aliano Biancana Badlands Based on a 3D Multi-Temporal High-Resolution Survey and Implications for Wind-Driven Rain
    Marsico, Antonella
    De Santis, Vincenzo
    Capolongo, Domenico
    LAND, 2021, 10 (08)
  • [49] The impact of input data resolution on neural network forecasting models for wind and photovoltaic energy generation using time series data
    AlShafeey, Mutaz
    Csaki, Csaba
    ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY, 2023, 42 (03)