With our Pedestrian Wind Comfort (PWC) analysis, it is possible to input additional modeling of geometry in the form of surface roughness and porous media to better capture its effects on the wind comfort simulation for an accurate analysis.

Surface Roughness

Surface roughness has an influence on friction resistance. While the effect of roughness for laminar flows is negligible, turbulent flows are highly dependent on wall roughness. Because, roughness changes the thickness of the viscous sublayer and modifies the law-of-wall for mean velocity. As a result, the turbulent friction factor increases with the roughness ratio.

In PWC simulations, wind flow is affected by the frictious surfaces as well as the obstacles, such as terrain, buildings, trees, etc. To apply the roughness effects on any surface in PWC analysis, one should use equivalent sand grain roughness height. Simply type the roughness coefficient and select the surface or a component. This will apply roughness on surface elements of selected entities.

In the following table, equivalent sand-grain roughness of some materials can be seen:

Material

ε, equivalent sand-grain roughness \([m]\)

Concrete

0.0003048

Case iron

0.000254

Commercial or welded steel

0.00004572

PVC

0.000001524

Glass

0.000001524

Wood

0.0005

Cast iron

0.00026

Table 1: Materials and their respective sand-grain roughness in meters.

Porous Objects

Porous media is a medium filled with solid particles, which lets fluid to pass through. The arrangement of the flow path inside the porous medium can be regular or irregular.

A porous medium can be classified as follows:

Consolidated medium: Solid-body has internal pores. Fluid passes through the pores.

Unconsolidated medium: A pile of solid particles is packed inside a bed. Fluid flows around the particles.

Porous media is used to model permeable obstructions such as trees, hedges, windscreens, and other wind mitigation measures. When air flows through a porous body, a pressure gradient along the direction of the flow is generated. Using porous media simplification reduces CAD and mesh complexity, and saves computational time and expenses.

Within the PWC analysis type, SimScale allows users to model porous objects using the following two models:

Darcy-Forchheimer Model

The pressure loss due to porosity is modeled by the empirical Darcy-Forchheimer equation where, \(\Delta p/\Delta x\) is pressure gradient, \(\mu\) is dynamic viscosity, \(\rho\) is density, \(u\) is velocity vector, \(F_\varepsilon\) is friction form coefficient and \(K\) is permeability.

The first and the second term on the right-hand side of the equation are the Darcy and Forchheimer terms respectively. The Darcy term accounts for the friction drag which has a linear relation to the local velocity vector. The Forchheimer term accounts for the inertial drag or the form drag which has a quadratic relation to the local velocity vector.

To be able to define a porous media in SimScale, one should define \(K\) and \(F_\varepsilon\). Users can extract these coefficients. Just use a minimum of 3 data points and predict the \(K\) and \(F_\varepsilon\) to fit the line. An example is shown below:

u\([m/s]\)

dP/dx\([Pa/m]\)

1

9.88

4

123.33

16

1852.84

Table 2: Velocity and pressure gradient values for the curve fitting method

Curve equation: \(\frac{\mathrm dP}{\mathrm d x} = \frac{0.0000181}{K}.u+1.\frac{F_\varepsilon}{K^{0.5}}.u^2\)

Using the curve-fitting method, missing coefficients were calculated as follows:

\(K\) = 0.00007135065

\(F_\varepsilon\) = 0.01890935

Once the relevant coefficients are found, assign them in Darcy-Forchheimer porous object definition and select the porous media geometry. This selection can be in the form of faces, volumes or geometry primitives.

While the isotropic type adds specific resistance in every direction, directional adds the specific resistance only on specified direction/s and assigns the remaining direction/s an infinite resistance (such as a wall).

Tree Model

Tree models are used to model the vegetation (single trees, bushes, hedges, forest canopies, etc.) as porous mediums. The user can either define the porosity as a custom tree model or choose one of the 5 most common trees in EU cities:

Planetree,

Oak,

Sycamore,

Silver birch,

Chestnut.

Custom tree model is also possible, and requires user input for assigning:

Leaf Area Index (LAI),

Average tree height,

Drag coefficient

Leaf area index (LAI) is a dimensionless number, which is used to compare plant canopies. It can be simply defined as the total leaf area per unit ground area.

Default tree models require only the assignment of tree height since the solver applies related LAI and drag coefficient automatically. The following table displays the default trees, and their respective LAI along with the drag coefficient:

Tree Type

Drag Coefficient

Leaf Area Index (LAI)

Plane tree

0.2

5.28

Oak tree

0.2

5.1657

Sycamore

0.2

2.9675

Silver birch

0.2

3.2379

Chestnut

0.2

5.1972

Table 3: Default tree types with their respective drag coefficients and LAI.

The above information obtained\(^1\) is a compiled data ranging over 70 years from 500 different locations.

In the tree model, we used the modified Darcy-Forchheimer equation. By assigning a high permeability value, we neglected the Darcy portion and simplified the equation as follows:

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