Comparison of various revised k–ε models and LES applied to flow around a high-rise building model with 1:1:2 shape placed within the surface boundary layer

Tominaga, Yoshihide; Mochida, Akashi; Murakami, Shuzo; Sawaki, Satoshi

doi:10.1016/j.jweia.2008.01.004

Cited by 314 publications

(146 citation statements)

References 20 publications

(32 reference statements)

Supporting

Mentioning

127

Contrasting

Unclassified

Order By: Relevance

“…In accordance with previous numerical simulations for simplified building models (e.g. Murakami et al, 1992;Tominaga et al, 2008a), the recirculation zone in the wake of the Faubourg building (denoted by A) extends farther downstream with SKE than with LES because of the under-estimation by SKE of the turbulent kinetic energy at this location. In this region, the backflow tends to transport the pollutant towards the Faubourg building.…”

Section: Comparison Between Standard K-ε and Les On Medium Grid For Csupporting

confidence: 89%

CFD simulation of near-field pollutant dispersion on a high-resolution grid: A case study by LES and RANS for a building group in downtown Montreal

Gousseau¹,

Blocken²,

Stathopoulos³

et al. 2011

Atmospheric Environment

300

148

View full text Add to dashboard Cite

Turbulence modeling and validation by experiments are key issues in the simulation of micro-scale atmospheric dispersion. This study evaluates the performance of two different modeling approaches (RANS standard k-ε and LES) applied to pollutant dispersion in an actual urban environment: downtown Montreal. The focus of the study is on near-field dispersion, i.e. both on the prediction of pollutant concentrations in the surrounding streets (for pedestrian outdoor air quality) and on building surfaces (for ventilation system inlets and indoor air quality). The high-resolution CFD simulations are performed for neutral atmospheric conditions and are validated by detailed wind-tunnel experiments. A suitable resolution of the computational grid is determined by grid-sensitivity analysis. It is shown that the performance of the standard k-ε model strongly depends on the turbulent Schmidt number, whose optimum value is case-dependent and a priori unknown. In contrast, LES with the dynamic subgrid-scale model shows a better performance without requiring any parameter input to solve the dispersion equation.

show abstract

Section: Comparison Between Standard K-ε and Les On Medium Grid For Csupporting

confidence: 89%

CFD simulation of near-field pollutant dispersion on a high-resolution grid: A case study by LES and RANS for a building group in downtown Montreal

Gousseau¹,

Blocken²,

Stathopoulos³

et al. 2011

Atmospheric Environment

300

148

View full text Add to dashboard Cite

show abstract

“…For such complex geometries, deficiencies in the RANS modeling become more significant. Certain deficiencies of RANS k-ε modeling around the windward facade edges and in the wake of the buildings have been reported [49,[64][65][66], such as the size of the wake and the location of reattachment. Blocken et al [15] compared the standard k-ε model, the Renormalisation Group (RNG) k-ε model, the realizable k-ε model and the Reynolds stress model (RSM) with wind-tunnel measurements using three different building geometries.…”

Section: Discussionmentioning

confidence: 99%

Numerical modeling of turbulent dispersion for wind-driven rain on building facades

et al. 2014

View full text Add to dashboard Cite

Wind-driven rain (WDR) is one of the most important moisture sources with potential negative effects on the hygrothermal performance and durability of building facades. The impact of WDR on building facades can be understood in a better way by predicting the surface wetting distribution accurately. Computational Fluid Dynamics (CFD) simulations can be used to obtain accurate spatial and temporal information on WDR. In many previous numerical WDR studies, the turbulent dispersion of the raindrops has been neglected. However, it is not clear to what extent this assumption is justified, and to what extent the deviations between the experimental and the numerical results in previous studies can be attributed to the absence of turbulent dispersion in the model. In this paper, an implementation of turbulent dispersion into an Eulerian Multiphase (EM) model for WDR assessment is proposed. First, CFD WDR simulations are performed for a simplified isolated high-rise building, with and without turbulent dispersion. It is shown that the turbulence intensity field in the vicinity of the building, and correspondingly the turbulence kinetic energy field, has a strong influence on the estimated catch ratio values when turbulent dispersion is taken into account. Next, CFD WDR simulations are made for a monumental tower building, for which experimental data are available. It is shown that taking turbulent dispersion into account reduces the average deviation between simulations and measurements from 24% to 15%.

show abstract

“…LES has been found to be more accurate than RANS-based simulation in regions of flow separation and recirculation when compared to wind-tunnel experiments because flow is highly unsteady in these regions [7]. However, LES takes two order of magnitude longer to execute than a RANS-based simulation [33] and, therefore, it has not been employed for the generation of model instances The geometry and grid settings described in Section 3 are used for both LES and the RANS-based simulation.…”

Section: 1) Uncertainties Associated With Rans-based Simulation In mentioning

confidence: 99%

“…These equations are time-averaged or ensembleaveraged equations of the fluid-flow motion. Large discrepancies have been observed in wakes of buildings when predictions of RANS-based simulations are compared with wind-tunnel experiments [7,8]. Wind-tunnel experiments are usually employed to evaluate the performance of approximate equations of fluid-flow solved in CFD simulations because values of parameters are known (e.g.…”

Section: ) Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Vernay

Raphael

Smith

2015

Journal of Wind Engineering and Industrial Aerodynamics

View full text Add to dashboard Cite

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

show abstract

Comparison of various revised k–ε models and LES applied to flow around a high-rise building model with 1:1:2 shape placed within the surface boundary layer

Cited by 314 publications

References 20 publications

CFD simulation of near-field pollutant dispersion on a high-resolution grid: A case study by LES and RANS for a building group in downtown Montreal

CFD simulation of near-field pollutant dispersion on a high-resolution grid: A case study by LES and RANS for a building group in downtown Montreal

Numerical modeling of turbulent dispersion for wind-driven rain on building facades

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

Contact Info

Product

Resources

About