A scale-dependent Lagrangian dynamic model for large eddy simulation of complex turbulent flows

By E. Bou‐Zeid, Charles Meneveau, Marc B. Parlange. 2005

733 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computational Mechanics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/3f2a180037374582d12f3d24b8f99cb3841b989a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Computational Mechanics

In Environmental Engineering

Citations in the corpus

MicroHH 1.0: a computational fluid dynamics code for direct numerical simulation and large-eddy simulation of atmospheric boundary layer flows (2017, background): “This choice can lead to a overestimation of near-surface wind due to violation of the MOST assumption of horizontal homogeneity (Bou-Zeid et al., 2005, their Fig.”
Dynamic subgrid‐scale models for momentum and scalar fluxes in large‐eddy simulations of neutrally stratified atmospheric boundary layers over heterogeneous terrain (2006, methodology): “More recently, Bou-Zeid et al. [2005] used the observation that the Lagrangian dynamic model gives a reasonable estimate for the Smagorinsky coefficient at the test filter scale rather than the grid scale, to approximate its scale dependence.”
Large-Eddy Simulation of the Atmospheric Boundary Layer (2020, methodology): “where u∗ is the friction velocity, κ is the von Kármán constant, U ( x) = (〈ũ( x, t)〉2 +〈ṽ( x, t)〉2) is the resolved velocity magnitude at the lowest computational level determined over horizontal planes, locally at each grid point, or as the local filtered value (Bou-Zeid et al. 2005; Stoll and Por”
Large‐eddy simulation of a diurnal cycle of the atmospheric boundary layer: Atmospheric stability and scaling issues (2006, methodology): “Res., 42, W06D09, doi:10.1029/2005WR004651.”
Enhancing computational fluid dynamics with machine learning (2021, background): “[87] and the Moeng model [88] for the wall boundary condition.”
Large-Eddy Simulation of Wind-Turbine Wakes: Evaluation of Turbine Parametrisations (2011, methodology): “Later, a Lagrangian scale-dependent dynamic model version was performed in simulations of neutral ABL flow over heterogeneous (Bou-Zeid et al. 2005; Stoll and Porté-Agel 2006) flat surfaces as well as flows over wave-like topography (Wan et al. 2007).”
Air pollutants degrade floral scents and increase insect foraging times (2016, methodology): “To close the set of 112 equations, the SGS momentum flux is parameterized using the Lagrangian scale-dependent 113 dynamic Smagorinsky model (Bou-Zeid et al., 2005).”
Nested Mesoscale Large-Eddy Simulations with WRF: Performance in Real Test Cases (2012, methodology): “…wall, although the value at the peak is in agreement with observations and other simulations that suggest this variance (normalized by the squaredfriction velocity) should peak at a value of about 1.5–2.5 somewhere around the top of the atmospheric surface layer (Stull 1988; Bou-Zeid et al. 2005).”
Comparison of wind farm large eddy simulations using actuator disk and actuator line models with wind tunnel experiments (2018, methodology): “In our simulations we use the scale-dependent Lagrangian subgrid model [36].”
Numerical and experimental study of mechanisms responsible for turbulent secondary flows in boundary layer flows over spanwise heterogeneous roughness (2015, methodology): “Aerodynamic drag imposed on the flow by the surface was computed with the equilibrium logarithmic law (Monin & Obukhov 1954; Piomelli & Balaras 2002; Bou-Zeid et al. 2005).”
Adding complex terrain and stable atmospheric condition capability to the OpenFOAM-based flow solver of the simulator for on/offshore wind farm applications (SOWFA) (2014, methodology): “[19] devised a Lagrangian-averaged scale-dependent (LASD1) dynamic Smagorinsky model.”