Estimation of the Kolmogorov constant by large-eddy simulation in the stable PBL

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Abstract

In this paper is evaluated the inertial subrange Kolmogorov constant C0 in a stable boundary-layer. The importance of the constant C0 is well known as predictions of turbulent dispersion by means of Lagrangian stochastic models depend upon its value. Different values of the C0 constant has been proposed along the years, most of them determined at low Reynolds number and/or under different techniques. Here we estimate the constant C0 by tracking an ensemble of Lagrangian particles injected in a stable planetary boundary-layer simulated with a large-eddy simulation model and analyzing the ensemble-averaged Lagrangian velocity structure function in the inertial subrange. Our estimative of C0,w is 3.7, which is consistent with values found in literature. The evaluation of C0,u and C0,v cannot be easily accomplished since it is difficult to identify an inertial subrange for the wind field horizontal components.

Introduction

Turbulent dispersion of scalars is most efficiently described in the Lagrangian framework as first suggested by the Taylor’s statistical theory of dispersion in homogeneous turbulence [1], [2]. Therefore, Lagrangian stochastic models constitute an important and effective tool to simulate the atmospheric dispersion of airborne pollutants. These models are based on the Langevin equation, which is derived from the hypothesis that the turbulent velocity is given by the combination between a deterministic and a stochastic term.

The second-order Lagrangian velocity structure function for the inertial subrange Kolmogorov scaling of turbulence assumes the form: D2L(τ)=[u(t+τ)u(t)]2C0ε̄τ for τηττL where u(t) is the Lagrangian velocity, denotes the ensemble average, τ is a time lag, ε̄ is the average dissipation rate of kinetic energy, τη is the Kolmogorov time scale (at which dissipation starts to be effective), τL is the Lagrangian integral time scale and finally C0 is the Lagrangian Kolmogorov constant supposed to be universal at high Reynolds number for an isotropic turbulence. Comparing the Lagrangian velocity structure functions obtained from Langevin equation with Eq. (1), it is possible to determine the stochastic term of the Langevin equation as b=(C0ε¯)1/2.

In this context it is evident that it is important to establish the numerical value of the constant C0 as the predictions of turbulent dispersion by means of the Lagrangian stochastic models depend upon its value [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]. In these models it is assumed that the evolution of a tracer particle’s state (velocity-position) is a Markovian process, so the particle’s trajectory can be statistically calculated. However, as C0 is a Lagrangian quantity there are serious technical difficulties associated with its measurements. The numerical value of C0 has been determined following a large number of techniques, like for example Lagrangian velocity measurements [13], [14], numerical experiments [15], observed dispersion of tracer particles in a flow [2] and Eulerian measurements [12]. This large variety of methodologies can in part explain the wide range of values found for the C0 constant and the fact that its value is rather uncertain [12], [16], [17]. Typical values of C0 obtained from both physical and numerical experiments vary within the range 2.0 to 7.0.

The objective of this work is to determine the C0value in a stable boundary-layer (SBL) using large-eddy simulation (LES) and the peak/plateau method [14], [15]. This method consists in tracking an ensemble of particle trajectories, obtained by considering the resolved velocity field from LES. In this context the Lagrangian structure function, D2L(τ), may be easily evaluated and the level of the peak/plateau of the D2L(τ)/ε̄τ versus τ curve allows the determination of the constant C0. The key point for this methodology is the presence of the inertial-subrange for the resolved velocity field generated by LES.

Section snippets

The large-eddy model

Large-eddy simulation of the planetary boundary-layer (PBL) is a well-established technique to describe geophysical turbulent flows [18], [19], [20], [21], [22], [15]. Most LES codes utilize the incompressible Boussinesq form of the Navier–Stokes equations and consider a horizontally homogeneous boundary layer. The PBL variables are spatially filtered to define resolved components and subgrid scale (SGS) components. This section describes the LES model as in Ref. [15].

The filtered i-th

The simulated stable PBL

To generate a SBL we have followed the guidelines and the computational setup described in the paper of Saiki et al. [24].

The calculations were realized on a one kilometer square domain with 128×128×128 grid points. The initial conditions consisted of a convective PBL with a geostrophic wind of 15.0 m s−1 and a positive surface heat flux of 0.05 m K s−1. The initial mean potential temperature profile forced an inversion at z=500m, where the temperature increased 8 K along twelve vertical

Lagrangian simulations and results

Before to release the Lagrangian particles, we have verified that (i) a nearly stationary Eulerian velocity field was obtained and (ii) the Kolmogorov inertial-subrange is present at least for the vertical turbulent velocity spectrum. Our analysis concerned the last stage of simulation in which the particles have been injected (STB5 in Table 1).

Fig. 5 shows that the vertical profile of Brunt–Väisälä frequency is not varying with time [N2=(g/θ0)(θ¯/z), where g/θ0 is a buoyancy coefficient]. In

Conclusions

In this paper we propose the evaluation of C0 constant in the SBL. The methodology was divided in two steps: the generation of the SBL with LES, and the successive injection of an ensemble of fictitious Lagrangian particles within the SBL. From the ensemble of the particle trajectories, which are obtained by considering the resolved velocity field from LES, the second order Lagrangian structure function D2L(τ) may be evaluated and the level of the plateau of the D2L(τ)/ε̄τ versus τ curve

Acknowledgements

The authors acknowledge the financial support provided by CNPq. (Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil). The authors are very grateful to Dr. P.Sullivan (NCAR) and Prof. B.L.Sawford (Monash University) for the scientific and technical support. Finally we would like to thank CESUP/UFRGS (Centro Nacional de Supercomputação, Porto Alegre, RS-Brasil) for the computational support.

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