Computational Fluid Dynamics Analysis of 3D Wind Flow around

Rhizophora Mangrove Tree and Drag Force Acting on the Wall of the

Tree

SINI RAHUMAN1, MOHAMED ISMAIL2, SHYLA MANAVALAN VARGHESE3, BINSON V. A.4

Bahrain Polytechnic,

Isa Town,

KINGDOM OF BAHRAIN

Houston, Texas,

USA

Saintgits College of Engineering,

Abstract: - Many natural disasters, such as cyclones, typhoons, mudslides, and tsunamis, are currently plaguing

research, a model Rhizophora Mangrove tree from Pichavaram Mangrove forest, Tamilnadu, India is generated

using Ansys Workbench design modeler. A computational domain is created around this tree to reduce the

boundary effect. Grid-independent research was carried out using various mesh sizes to determine the best grid

resolution for the analysis. An unstructured triangular mesh was generated for the simulation. The focus of this

study is to determine the 3D flow around the tree in order to determine the wind velocity, as well as the

coefficient of drag and drag force on the wall tree. This research helps to give a clear understanding of wind

flow around the 3D Rhizophora Mangrove tree. The velocity profile, pressure distribution, drag force, and drag

coefficient around the tree are captured, analysed, and presented in detail. The results show that the Rhizophora

mangrove trees can significantly reduce the flow velocity of the wind and will be able to safeguard the coast

and communities nearby from natural disasters.

Natural disasters are a huge danger to human beings

all around the earth every year. To defend the

coastline from tsunamis, storms, floods, tornadoes,

and hurricanes, coastal erosion is a serious

challenge. A mangrove is a shrub or tree that lives

in salty or brackish water along the coast. They

shield the surrounding areas from tsunamis and poor

the Indian Ocean Tsunami caused damage to a

number of Asian and African countries. Many

villages located behind the forest were sheltered by

the mangrove forest during the Tsunami, according

to studies, [1].

[3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13],

[14], [15], [16], [17], [18] have been undertaken to

examine and discover the importance of mangrove

forests. To examine wave attenuation over a finite-

slope zone on the shore in the presence of stiff

vegetation. A 3-D numerical technique was used to

explore the interaction of tsunami waves with

mangrove ecosystems, [21]. In a recent work [22],

numerical analysis was utilized to investigate

periodic long-wave run-ups on sloping beaches with

stiff vegetation. Another study [23], used a 2-D

Numerical Wave Tank based on a porous body

model to show energy dissipation after contact with

a tsunami. The results reveal that when plant height,

and cause turbulence. According to the study,

friction between the mangrove forest and the waves

slows the velocity of the waves, as contrasted to

friction at the bottom. The wave height and drop in

water velocity differ depending on the type of

mangrove species, [25]. Some researchers, [26],

used a computer simulation to explore the features

of roots and their involvement in reducing the

velocity of water flow around Avicennia marina and

Rhizophora apiculate.

was conducted and included a detailed explanation

regarding the supplementary proof of the storm

protection ecosystem services offered by mangroves

in the area. This serves as an additional justification

breadth and density of the forest grew, according to

data acquired from Avicennia's numerical

simulation with the TUNA-RP model, [30].

According to a numerical simulation study, [31], on

energy dissipation within the mangrove forest, the

mean wave velocity decreased considerably for

every 20 m cross-section of the mangrove forest.

The flow's vertical and horizontal amplitude

water vortex power plants, [33].CFD investigation

of water flow patterns around the rhizophora tree

was conducted in [5] and it was revealed that the

fluid velocity was lowered by more than 70%.The

behavior and efficacy of stilt roots (prop roots) of

Rhizophora Mangroves in lowering the flow of the

velocity of the wind during severe tropical storms,

intense tropical cyclones, and extremely intense

tropical cyclones were analyzed in [34].

critical to strengthen and expand research in wave

velocity propagation across Mangrove forests in

actual weather scenarios.

2.1 Study Approach

Pichavaram Mangrove Forest stands as India's

renowned and the world's second-largest mangrove

expanse. The M.S. Swaminathan Research

of the tree's ecological dynamics, supporting

conservation initiatives and promoting sustainable

a computational model of the Rhizophora mangrove

tree, which is visualized in Figure 1. Ansys

Workbench design modeler 18.1 is used for this

process in general. The mesh is generated around

this geometry and applied to the boundary

condition. Fine mesh is generated near and around

the roots as visualized in Figure 2. Iso surface

created at y=0.25m, z=0.25m, y=0.75m &z=0.75m,

which are shown in Figure 3 and Figure 4. The

wind flow around the Rhizophora mangrove tree.

study and this problem is solved using the finite

volume method.

Case 1: Steady incompressible turbulent flow with

velocity 50m/s

Case2: Unsteady incompressible turbulent flow with

velocity 50m/s

Fig. 4: Iso surface created at Y=0.75m & Z=0.75m

Δx +

󰇡µ∂u

Δy 

Δx +

Δy =

Δy



ΔU (8)

ΔU is the u-control volume.

Δx is the width of the u-control volume.

Y-momentum:

󰇡ρvi,j+1

󰇡

Δx 

Δy



ΔV (9)

ΔV is the v-control volume.

Δy is the width of the v-control volume.

Similarly, Z - momentum equation also can be

calculated. The k-ε model is used to represent the

impacts of turbulence in turbulent flow. In addition

to the equations of continuity and momentum, the k-

ε model provides two transport equations (10) to



where ui is the x-direction velocity component, μ is

the viscosity, t is the turbulent viscosity, Mk is the

kinetic energy production due to shear stress,  is

the kinetic energy generation due to buoyancy, and

compressibility. The empirical constants are:

(13)



  (14)

The above equations (12) to (14) represent

discretized Navier Stokes Equations for steady state

flow where and  are the volume of u-

cell, v-cell and w-cell. For unsteady flow ap0 in (15)

is added to the central coefficients of the above

during each iteration. Which are the discretization

error (DE) and round off error(R)

De = A – S; R = N – S (16)

Where ‘A’ is the analytical solution of the partial

differential equation. ‘S’ is the exact solution of the

difference equation. ‘N’ is the Numerical solution

from a real computer with finite accuracy.

Discretization involves approximating continuous

mathematical models with discrete counterparts.

with a finite number of digits, leading to small

errors in calculations, especially with repeated

operations.

The solution is unstable if the Ri’s grow bigger

during the progression of the solution from nth step

to (n+1)th step. The stable solution is obtained if

during iterations, it indicates instability in the

solution. The stability criterion (Equation 17)

ensures that the errors do not accumulate rapidly,

preventing numerical instability.

process of approximating continuous problems with

discrete methods, while round-off errors stem from

stability through the defined criterion helps maintain

the reliability of numerical solutions in the iterative

process.