Modelling of concentration fluctuations in canopy turbulence

Self Cite

122

The structure of turbulent flows along a transition between tall-forested canopies and forest clearings continues to be an active research topic in canopy turbulence. The difficulties in describing the turbulent flow along these transitions stem from the fact that the vertical structure of the canopy and its leaf area distribution cannot be ignored or represented by an effective roughness length. Large-eddy simulation (LES) runs were performed to explore the effect of a homogeneous variation in the forest leaf area index (LAI) on the turbulent flow across forest edges. A nested grid numerical method was used to ensure the development of a deep boundary layer above the forest while maintaining a sufficiently high resolution in the region close to the ground. It was demonstrated that the LES here predicted first-order and second-order mean velocity statistics within the canopy that agree with reported Reynolds-Averaged Navier-Stokes (RANS) model results, field and laboratory experiments. In the simulations reported here, the LAI was varied between 2 and 8 spanning a broad range of observed LAI in terrestrial ecosystems. By increasing the forest LAI, the mean flow properties both within the forest and in the clearing near the forest edge were altered in two fundamental ways: near the forest edge and into the clearing, the flow statistical properties resembled the so-called back-facing step (BFS) flow with a mean recirculation zone near the edge. Another recirculation zone sets up downstream of the clearing as the flow enters the tall forest canopy. The genesis of this within-forest recirculation zone can be primarily described using the interplay between the mean pressure gradients (forcing the flow) and the drag force (opposing the flow). Using the LES results, a simplified analytical model was also proposed to explain the location of the recirculation zone inside the canopy and its dependence on the forest LAI. Furthermore, a simplified scaling argument that decomposes the mean velocity at the outflow edge into a superposition of 'exit flow' and BFS-like flow with their relative importance determined by LAI was explored.

Section: Discussionmentioning

confidence: 99%

The Effects of Canopy Leaf Area Index on Airflow Across Forest Edges: Large-eddy Simulation and Analytical Results

Cassiani

Katul

Albertson

2007

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122

“…Once all the particles in the sub-ensemble have exited the spatial domain through x > x max , the mean micromixing time scale in bin (x I , y J , z K ) is calculated for use in the next stage of the simulation. If the micromixing time scale is larger than the turbulence time scale τ = k/ε, then t m is reset to τ , similar to that done in Cassiani et al (2007). Furthermore, for regions outside the plume, mixing still occurs and does so at a rate governed by the turbulence time scale, hence for these regions t m = τ .…”

Section: The Spmmm Micromixing Modelmentioning

confidence: 97%

“…non-buoyant), non-reactive (i.e. no chemistry) scalars within the neutral boundary layer (Cassiani et al 2005a), within the convective boundary layer (Cassiani et al 2005b;Luhar and Sawford 2005a,b) and within a canopy layer (Cassiani et al 2005c(Cassiani et al , 2007.…”

mentioning

confidence: 99%

Comparing Two Implementations of a Micromixing Model. Part I: Wall Shear-Layer Flow

Postma

Wilson

Yee

2011

A Lagrangian stochastic (LS) micromixing model is used for estimating concentration fluctuations in plumes of a passive, non-reactive tracer dispersing from elevated and ground-level compact sources into a neutral wall shear-layer flow. SPMMM (for sequential particle micromixing model) implements the familiar IECM (interaction by exchange with the conditional mean) micromixing scheme. The parametrization of the scalar micromixing time scale is identical to that proposed in a previously reported LS-IECM model (Cassiani et al., Atmos Environ 39:1457-1469. However, while SPMMM is mathematically equivalent to the previously reported model, it differs in its numerical implementation: SPMMM releases N independent particles sequentially, whereas the previously reported model releases N independent particles simultaneously. In both implementations, the trajectories of the N particles are governed by single-point velocity statistics. The sequential particle implementation is computationally efficient, but cannot be applied to the case of reacting species. Results from both implementations are compared to experimental wind-tunnel dispersion data and to each other.

“…The algorithm for calculating the micromixing time scales is very similar to that outlined in Cassiani et al (2005Cassiani et al ( , 2007. SPMMM calculates t m (x I , y J , z K ) before simulating mixing by releasing a small sub-ensemble of particles sequentially from the source region.…”

Section: Numerical Implementationmentioning

confidence: 99%

“…It was shown in Cassiani et al (2007) that the parametrization of the micromixing time scale used in both SPMMM and the CASS models performed well for simulating dispersion in a canopy flow with the simultaneous particle/on-the-fly implementation of the CASS model. In this article, we investigate whether this is also true for the sequential particle/precalculation implementation of SPMMM.…”

Section: Introductionmentioning

confidence: 99%

Comparing Two Implementations of a Micromixing Model. Part II: Canopy Flow

Postma

Wilson

Yee

2011

The sequential particle micromixing model (SPMMM) is used to estimate concentration fluctuations in plumes dispersing into a canopy flow. SPMMM uses the familiar single-particle Lagrangian stochastic (LS) trajectory framework to pre-calculate the required conditional mean concentrations, which are then used by an interaction by exchange with the conditional mean (IECM) micromixing model to predict the higher-order fluctuations of the scalar concentration field. The predictions are compared with experimental wind-tunnel dispersion data for a neutrally stratified canopy flow, and with a previously reported implementation using simultaneous particle trajectories. The two implementations of the LS-IECM model are shown to be largely consistent with one another and are able to simulate dispersion in a canopy flow with fair to good accuracy.