CFD simulation of the atmospheric boundary layer: wall function problems

Blocken, Bje Bert; Stathopoulos, T.; Carmeliet, Jan

doi:10.1016/j.atmosenv.2006.08.019

Cited by 1,073 publications

(588 citation statements)

References 24 publications

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“…All the solid surfaces were treated using standard wall functions (see, e.g., Blocken et al, 2007). The same wall functions were applied to the lateral boundaries in the 0 and 90° cases.…”

Section: Figurementioning

confidence: 99%

Influence of urban morphology on air flow over building arrays

Carpentieri

Robins

2015

Journal of Wind Engineering and Industrial Aerodynamics

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“…All the solid surfaces were treated using standard wall functions (see, e.g., Blocken et al, 2007). The same wall functions were applied to the lateral boundaries in the 0 and 90° cases.…”

Section: Figurementioning

confidence: 99%

Influence of urban morphology on air flow over building arrays

Carpentieri

Robins

2015

Journal of Wind Engineering and Industrial Aerodynamics

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“…The roughness length imposed on the upstream terrain is also used for the definition of inlet profiles (Eqs. (6)- (8)) in order to avoid unintended streamwise gradient associated with roughness modification in the upstream part of the computational domain [28]. The Wind speeds and wind directions at the inlet of the CFD simulation are sensitive parameters and are difficult to estimate in urban areas [30].…”

Section: 3) Wind Predictions At Unmeasured Locationsmentioning

confidence: 99%

A model-based data-interpretation framework for improving wind predictions around buildings

Vernay

Raphael

Smith

2015

Journal of Wind Engineering and Industrial Aerodynamics

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Abstract:Although Computational Fluid Dynamics (CFD) simulations are often used to assess wind conditions around buildings, the accuracy of such simulations is often unknown. This paper proposes a datainterpretation framework that uses multiple simulations in combination with measurement data to improve the accuracy of wind predictions. Multiple simulations are generated through varying sets of parameter values. Sets of parameter values are falsified and thus not used for predictions if differences between measurement data and simulation predictions, for any measurement location, are larger than an estimate of uncertainty bounds. The bounds are defined by combining measurement and modelling uncertainties at sensor locations. The framework accounts for time-dependent and spatially-distributed modelling uncertainties that are present in CFD simulations of wind. The framework is applied to the case study of the CREATE Tower located at the National University of Singapore. Values for timedependent inlet conditions, as well as values for the roughness of surrounding buildings, are identified with measurements carried out around the CREATE Tower. Results show that, on average, ranges of

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“…A distinction is made between two types of roughness (Blocken et al 2007a, Blocken 2015: (1) the roughness of the terrain that is included in the computational domain and (2) the roughness of the terrain that is not included in the computational domain. The knowledge of the roughness of the terrain that is situated outside the computational domain is important because it determines the shape of the inlet profiles of mean wind speed and turbulence properties.…”

Section: Boundary Conditions and Solver Settingsmentioning

confidence: 99%

“…8); (2) estimation of the local z0 using the roughness classification (Wieringa 1992); and (3) conversion of z0 into the corresponding wall function parameters kS and CS (Blocken et al 2007a). These parameters can be calculated from z0 using the appropriate conversion equation, which, for Fluent 6.3, was derived by (Blocken et al 2007a):…”

Section: Boundary Conditions and Solver Settingsmentioning

confidence: 99%