A numerical study of the impact of vegetation on mean and turbulence fields in a European-city neighbourhood

Barbano, Francesco; Sabatino, Silvana Di; Stoll, Rob; Pardyjak, Eric R.

doi:10.1016/j.buildenv.2020.107293

Cited by 22 publications

(8 citation statements)

References 52 publications

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“…where Δx, Δy, and Δz are the cell sizes corresponding to the x, y, and z directions, respectively. The modelling strategy used by QES-Winds has been validated for several urban configurations with and without trees and gives quasi-steady-state velocity fields that are in good agreement with experimental data and full physics simulations (Hayati et al 2017(Hayati et al , 2019Barbano et al 2020). To adapt QES-Winds for wildfires, a parameterisation for the velocity field in and around fires is needed ('QES-Fire plume parameterisation' section).…”

Section: Microscale Windsmentioning

confidence: 94%

QES-Fire: a dynamically coupled fast-response wildfire model

Moody

Gibbs

Krueger

et al. 2022

Int. J. Wildland Fire

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A microscale wildfire model, QES-Fire, that dynamically couples the fire front to microscale winds was developed using a simplified physics rate of spread (ROS) model, a kinematic plume-rise model and a mass-consistent wind solver. The model is three-dimensional and couples fire heat fluxes to the wind field while being more computationally efficient than other coupled models. The plume-rise model calculates a potential velocity field scaled by the ROS model’s fire heat flux. Distinct plumes are merged using a multiscale plume-merging methodology that can efficiently represent complex fire fronts. The plume velocity is then superimposed on the ambient winds and the wind solver enforces conservation of mass on the combined field, which is then fed into the ROS model and iterated on until convergence. QES-Fire’s ability to represent plume rise is evaluated by comparing its results with those from an atmospheric large-eddy simulation (LES) model. Additionally, the model is compared with data from the FireFlux II field experiment. QES-Fire agrees well with both the LES and field experiment data, with domain-integrated buoyancy fluxes differing by less than 17% between LES and QES-Fire and less than a 10% difference in the ROS between QES-Fire and FireFlux II data.

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Section: Microscale Windsmentioning

confidence: 94%

QES-Fire: a dynamically coupled fast-response wildfire model

Moody

Gibbs

Krueger

et al. 2022

Int. J. Wildland Fire

Self Cite

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“…The details can be found in Brzozowska (2015a, b). This field, which can be obtained either by measurements, parametrization, and approximation (Girard et al 2018;Bauer 2019;Barbano et al 2020) or by earlier calculations, is denoted as u 0 (x 1 , x 2 , x 3 ). Usually it does not ensure mass consistency in the domain analyzed.…”

Section: Variational Method Conformal Variablesmentioning

confidence: 99%

“…The values depend on, among others, the distance of the grid nodes from measurements stations, terrain topography, the stability class of the atmosphere, and roughness of the ground. In order to capture the flow features, the parametrization is necessary (Girard et al 2018;Oettl 2019a, b;Barbano et al 2020). Having calculated the wind field u 0 , Eq.…”

Section: Iterative Proceduresmentioning

confidence: 99%

An Iterative Method for Calculation of Wind Profiles at the Mesoscale and Microscale

Adamiec–Wójcik

Brzozowska

Drąg

et al. 2022

Boundary-Layer Meteorol

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This paper presents the variational diagnostic model and iterative procedure, which enables the wind field in subdomains to be adjusted. Diagnostic models are not time dependent. Consideration of more complex features of the thermodynamic structure requires models with high resolution, which require large calculation times. The model presented applies the variational approach and enables topographical complexity of the terrain to be considered. The problem of adjusting the wind field is solved in two steps. The first step adjusts the initial wind field by means of experimental measurements or a prognosis in the larger domain, which includes smaller domains. Then the results obtained are used as the initial wind field when the grid refinement in the smaller domain is performed. This allows more precise mapping of the terrain and its architecture. Nevertheless the algorithm proposed ensures a considerable reduction in calculation time. This approach also allows us to eliminate the problem of the lack of initial data when the number of meteorological stations in the smaller domain is insufficient. The algorithm is described and validated, and numerical simulations for pollutant dispersion for a chosen town are described, followed by discussion of the iterative procedure.

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“…Compared with the case without trees on the streets, the results obtained by the explicit porous medium method demonstrated that trees reduced the wind speed in the street canyon. Barbano et al (2020) used a simplified CFD tool (QUIC) to simulate and evaluate the average wind and turbulence fields of a vegetated urban neighborhood. By comparing the simulation results with and without trees, they found that trees could reduce airflow by restricting local circulation and reducing the intensity of turbulence.…”

Section: Introductionmentioning

confidence: 99%

Study on Dispersion of Carbon Dioxide over the Shrubbery Region

2021

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In the carbon capture and storage (CCS) infrastructure, the risk of a high-pressure buried pipeline rupture possibly leads to catastrophic accidents due to the release of tremendous amounts of carbon dioxide (CO2). Therefore, a comprehensive understanding of the effects of CO2 dispersion pattern after release from CCS facilities is essential to allow the appropriate safety precautions to be taken. Due to variations in topography above the pipeline, the pattern of CO2 dispersion tends to be affected by the real terrain features, such as trees and hills. However, in most previous studies, the dynamic impact of trees on the wind field was often approximated to linear treatment or even ignored. In this article, a computational fluid dynamics (CFD) model was proposed to predict CO2 dispersion over shrubbery areas. The shrubs were regarded as a kind of porous media, and the model was validated against the results from experiment. It was found that shrubbery affected the flow field near the ground, enhancing the lateral dispersion of CO2. Compared with that of the shrub-free terrain, the coverage area of the three shrub terrains at 60 s increased by 8.1 times, 6.7 times, and 9.1 times, respectively. The influence of shrub height and porosity on CO2 dispersion is nonlinear. This research provides reliable data for the risk assessment of CCS.

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A numerical study of the impact of vegetation on mean and turbulence fields in a European-city neighbourhood

Cited by 22 publications

References 52 publications

QES-Fire: a dynamically coupled fast-response wildfire model

QES-Fire: a dynamically coupled fast-response wildfire model

An Iterative Method for Calculation of Wind Profiles at the Mesoscale and Microscale

Study on Dispersion of Carbon Dioxide over the Shrubbery Region

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