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Journal ArticleDOI

Appropriate boundary conditions for computational wind engineering models using the k-ϵ turbulence model

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TLDR
In this paper, velocity and turbulence profiles associated with the k-ϵ turbulence model are proposed which produce homogeneous conditions, and the cospectrum for the Reynolds stress exhibits a characteristics frequency n o ≈ u ∗ / z which is consistent with the suggested profile equations.
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This article is published in Journal of Wind Engineering and Industrial Aerodynamics.The article was published on 1993-08-01. It has received 1044 citations till now. The article focuses on the topics: K-omega turbulence model & K-epsilon turbulence model.

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Equilibrium Atmospheric Boundary-Layer Flow: Computational Fluid Dynamics Simulation with Balanced Forces

TL;DR: In this paper, a dynamical approach is proposed to develop a neutrally stratified equilibrium ABL flow, which is then applied as inflow conditions to simulate boundary-driven ABL flows.
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Study of Atmospheric Boundary Layer Flows over a Coastal Cliff

TL;DR: In this paper, the extent to which a wind energy site is affected by a coastal cliff is presented by studying numerically a neutral Atmospheric Boundary Layer (ABL) flow using an RNG κ-ϵ model and different geometries.
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Comparisons of Horizontal-Axis Wind Turbine Wake Interaction Models

TL;DR: In this article, Reynolds-averaged Navier-Stokes (RANS) simulations are performed using the k-e and k-ω shear stress transport (SST) turbulence closure schemes to investigate the interactions of horizontal-axis wind turbine (HAWT) models in the neutrally stratified atmospheric boundary layer (ABL).
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The Effect of Tree-Planting Patterns on the Microclimate within a Courtyard

TL;DR: In this article, the authors conducted numerical simulations for five different tree-planting patterns, including focused tree planting, cornered tree planting (C), multi-row tree planting and no tree planting.
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Designing roadside green infrastructure to mitigate traffic-related air pollution using machine learning.

TL;DR: Five machine learning methods are investigated, including linear regression (LR), support vector machine (SVM), random forest (RF), XGBoost (XGB), and neural networks (NN), to predict size-resolved and locationally dependent particle concentrations downwind of various vegetation barrier designs.
References
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Journal ArticleDOI

The numerical computation of turbulent flows

TL;DR: In this paper, the authors present a review of the applicability and applicability of numerical predictions of turbulent flow, and advocate that computational economy, range of applicability, and physical realism are best served by turbulence models in which the magnitudes of two turbulence quantities, the turbulence kinetic energy k and its dissipation rate ϵ, are calculated from transport equations solved simultaneously with those governing the mean flow behaviour.

Characteristics of turbulence in a boundary layer with zero pressure gradient

TL;DR: In this article, the results of an experimental investigation of a turbulent boundary layer with zero pressure gradient are presented and the importance of the region near the wall and the inadequacy of the concept of local isotropy are demonstrated.

Neutrally stratified boundary-layer flow over roughness changes and topography

TL;DR: In this paper, a linear model for neutral surface-layer flow over complex terrain is presented, which makes it possible to make high-resolution computations for an arbitrary distribution of surface roughness and topography.
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A mixed spectral finite-difference model for neutrally stratified boundary-layer flow over roughness changes and topography

TL;DR: In this paper, a linear model for neutral surface-layer flow over complex terrain is presented, which combines the simplicity and computational efficiency of linear methods with flexibility for closure schemes of finite-difference methods.
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Simulation of Effect of Wind Barriers on Airflow

TL;DR: In this article, the authors developed a quantitative, theoretical simulation of airflow normal to narrow wind barriers of various porosities and, when possible, verify the results using experimental data, using finite difference methods having a combination of upwind and central difference schemes.
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