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Embedded large eddy simulation approach for pollutant dispersion around a model building in atmospheric boundary layer

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Abstract

In the present article, the potential of embedded large eddy simulation (ELES) approach to reliably predict pollutant dispersion around a model building in atmospheric boundary layer is assessed. The performance of ELES in comparison with large eddy simulation (LES) is evaluated in several ways. These include a number of qualitative and quantitative comparisons of time-averaged and instantaneous results with wind tunnel measurements supplemented by statistical data analyses using scatter plots and standard evaluation metrics. Results obtained by both LES and ELES approaches show very good agreement with the experiment. However, addition of turbulence to mean flow at Reynolds averaged Navier–Stokes (RANS)–LES interface in ELES approach not only increases the turbulence intensity, it also results in larger values of turbulent kinetic energy (TKE) as well as a shorter reattachment length in the wake region. Accordingly, higher levels of TKE predicted by ELES increase the local intensity of concentration leading to shorter plume shapes as compared with LES. In general, ELES shows better agreement with experiment on the surfaces of model building and also in the downstream wake region. In terms of computational costs, the CPU time required to obtain statistical values in ELES is about 49 % lower than that of LES and the number of iterations per time step is also reduced by 55 % as compared with LES.

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Abbreviations

\(\langle \rangle\) :

Time-averaged value

c :

Concentration

C s :

Smagorinsky constant

CFL :

Courant–Friedrichs–Lewy number

D :

Molecular diffusivity

D SGS :

SGS turbulent diffusivity

D t :

Turbulent diffusivity

FB :

Fractional bias

\(\hat{g}\) :

Unit vector aligned with streamwise mean velocity gradient

H b :

Model building height

Ic :

Local intensity

Ic abs :

Absolute intensity

I ε :

Dissipation length scale

I μ :

Viscous length scale

\(J_{i}^{SGS}\) :

SGS turbulent scalar flux

K:

Dimensionless concentration

N :

Number of vortex points

NMSE :

Normalized mean square error

p :

Pressure

q :

Hit rate

Q e :

Effluent release rate

\(q_{j}^{RANS}\) :

Turbulent scalar flux

\(q_{j}^{SGS}\) :

Sub-grid scale scalar flux

\(Q_{x}^{C}\) :

Streamwise convective scalar flux

\(Q_{x}^{T}\) :

Streamwise total turbulent scalar flux

R :

Pearson correlation coefficient

\(RE \equiv (\langle K_{Num} \rangle - \langle K_{Exp} \rangle )/\langle K_{Exp} \rangle\) :

Relative error of K

RNMSE :

Root normalized mean square error

S :

Area of interface

S ij :

Rate of strain tensor

Sc SGS :

Sub-grid scale Schmidt number

Sc t :

Turbulent Schmidt number

TKE :

Turbulent kinetic energy

U b :

Velocity at model building height

u i :

Velocity in ith direction

U ∞ :

Free stream velocity

W :

Absolute error threshold

x :

Streamwise direction

\(\hat{x}\) :

Unit vector in the streamwise direction

y :

Vertical direction

y + :

Dimensionless wall distance

z :

Lateral direction

Γ:

Circulation

Δ:

Grid filter width

Δt:

Time step

δ :

Boundary layer thickness

δ ij :

Kronecker delta

ε :

Turbulent dissipation rate

η :

Spatial distribution of vortex

κ :

Von-Karman constant

ν :

Molecular kinematic viscosity

ν t :

Eddy viscosity (turbulent viscosity)

ν SGS :

Sub-grid scale turbulent viscosity

ρ :

Density

σ i :

Local vortex size

τ :

Characteristic time scale

\(\tau_{ij}^{RANS}\) :

Turbulent stress tensor

\(\tau_{ij}^{SGS}\) :

Sub-grid scale stress tensor

ω :

Vorticity

b:

Building

e:

Effluent

ELES:

Embedded large eddy simulation

Exp:

Experimental

LES:

Large eddy simulation

Num:

Numerical

SGS:

Sub-grid scale

t:

Turbulent

x:

Streamwise direction

∞:

Free stream

C:

Convective

T:

Turbulent

–:

Filtered

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Authors and Affiliations

  1. School of Mechanical Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran

    Mohammad Jadidi, Farzad Bazdidi-Tehrani & Mohsen Kiamansouri

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  1. Mohammad Jadidi
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  2. Farzad Bazdidi-Tehrani
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  3. Mohsen Kiamansouri
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Correspondence to Farzad Bazdidi-Tehrani.

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Jadidi, M., Bazdidi-Tehrani, F. & Kiamansouri, M. Embedded large eddy simulation approach for pollutant dispersion around a model building in atmospheric boundary layer. Environ Fluid Mech 16, 575–601 (2016). https://doi.org/10.1007/s10652-016-9444-5

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