Improved k–ε model and wall function formulation for the RANS simulation of ABL flows

Parente, Alessandro; Gorlé, Catherine; Beeck, Jeroen van; Benocci, C.

doi:10.1016/j.jweia.2010.12.017

Cited by 149 publications

(104 citation statements)

References 15 publications

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“…Unfortunately, the method does not work for large domains because the transport equation for turbulent kinetic energy (TKE) k is still not in equilibrium with MOST, especially in unstable atmospheric conditions, which will be shown in the present paper. Parente et al [7] proposed to add a source term in the k-equation such that the k-ε model can sustain a k inlet profile that is variable with height. We will combine the ideas of Parente et al [7], and Alinot and Masson [5,6] into a new method where an additional analytical source term in the k-equation is added and a variable Cε,3 is used, such that stable and unstable MOST profiles are maintained over a long distance.…”

Section: Introductionmentioning

confidence: 99%

“…Parente et al [7] proposed to add a source term in the k-equation such that the k-ε model can sustain a k inlet profile that is variable with height. We will combine the ideas of Parente et al [7], and Alinot and Masson [5,6] into a new method where an additional analytical source term in the k-equation is added and a variable Cε,3 is used, such that stable and unstable MOST profiles are maintained over a long distance. In theory, one can also choose to parameterize other constants in the ε-equation, however, it is more natural to parameterize Cε,3 because this parameter does not exist in the k-ε model for neutral conditions.…”

Section: Introductionmentioning

confidence: 99%

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A new k‐epsilon model consistent with Monin–Obukhov similarity theory

2016

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A new k-ε model is introduced that is consistent with Monin-Obukhov similarity theory (MOST). The proposed k-ε model is compared with another k-ε model that was developed in an attempt to maintain inlet profiles compatible with MOST. It is shown that the previous k-ε model is not consistent with MOST for unstable conditions, while the proposed k-ε model can maintain MOST inlet profiles over distances of 50 km.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new k‐epsilon model consistent with Monin–Obukhov similarity theory

2016

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“…For an accurate study, the profiles should be horizontally homogeneous because the inlet profiles actually correspond to the measured data obtained at a location close to the building. Nevertheless, several researchers [54,[59][60][61][62][63] report a similar trend on the horizontal inhomogeneity of the kinetic energy profile even though the mean wind speed profile remains constant. The horizontal homogeneity is linked to the relation between the inlet boundary conditions, the wall boundary conditions, the surface roughness modeling and the computational grid [54].…”

Section: Geometry and Measurement Datamentioning

confidence: 74%

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

et al. 2014

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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%.

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“…The importance of correct ground roughness specification for the accuracy of CFD simulation results in general was mentioned and/or demonstrated in earlier research (e.g. Richards and Hoxey 1993, Blocken et al 2007a, 2007b, Hargreaves and Wright 2007, Franke et al 2007, Gorlé et al 2009, Yang et al 2009, Richards and Norris 2011, Parente et al 2011. The importance of surface roughness for wind flow over complex terrain in particular was also stressed by several previous authors (Lun et al 2003, Prospathopoulos and Voutsinas 2006, Cao and Tamura 2006, 2007,Wakes et al 2010, Cao et al 2012.…”

Section: Boundary Conditions and Solver Settingsmentioning

confidence: 79%