Evaluation of CFD Simulation Using Various Turbulence Models for Wind Pressure on Buildings Based on Wind Tunnel Experiments

被引:10
作者
Li, Biao [1 ]
Liu, Jing [1 ,2 ]
Luo, Feifei [1 ]
Man, Xiaoxin [3 ]
机构
[1] Harbin Inst Technol, Sch Environm & Municipal Engn, Harbin 150090, Peoples R China
[2] Harbin Inst Technol, State Key Lab Urban Water Resource & Environm, Harbin 150090, Peoples R China
[3] China Construction Engn Design Grp Corp Ltd Headq, Beijing 100037, Peoples R China
来源
9TH INTERNATIONAL SYMPOSIUM ON HEATING, VENTILATION AND AIR CONDITIONING (ISHVAC) JOINT WITH THE 3RD INTERNATIONAL CONFERENCE ON BUILDING ENERGY AND ENVIRONMENT (COBEE) | 2015年 / 121卷
关键词
CFD; wind tunnel experiment; wind pressure; buildings; K-EPSILON MODELS; BOUNDARY-LAYER; FLOW; LES;
D O I
10.1016/j.proeng.2015.09.094
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Various Reynolds Average Numerical Simulation (RANS) turbulent models of Computational Fluid Dynamics (CFD) are widely used in numerical simulation of wind environment with buildings areas, but none of them is the most accurate or applicable one. In order to evaluate different numerical simulation models, wind tunnel experiments were designed to validate the results of numerical simulations. Rigidity models surface pressure measurements were employed to get pressure values on several building surfaces among buildings, which accurately assessed different simulating results of wind field effects on buildings. By comparing and validating turbulent models with measurements of wind tunnel experiments, useful guidelines and references were given to choose appropriate turbulent model for numerical wind environment researches and practical application. This study presents the RNG k- model including the DVM treatment has a relative good predicting for both the plan view of velocity contour and the surface pressure. In addition, the Spalart-Allmaras models can be more appropriate predicting results for surface pressure measurements among all models, but its reliability of buildings wind environment prediction is not cleared yet. The future work will be needed. (C) 2015 Published by Elsevier Ltd.
引用
收藏
页码:2209 / 2216
页数:8
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