Section A: Introduction

to CFD

Existence

of modern technologies had eased the application of science for humans. With

progression of computational performance, Computational Fluid Dynamics (CFD)

software was introduced to the modern society to study on flow of fluid without

any physical subject to be tested on. This software was implemented to a wide

variety of industries which includes automotive, aerospace, astrophysics,

chemical manufacturing and power generators. Before the introduction of CFD,

researchers had to rely solely on their knowledge of mathematics and fluid

mechanics to estimate an outcome of fluid flows. While the software is of aid

to modern society, CFD would need a computer to process its simulation and data

retrieval of the desired simulation. Therefore, a substantial amount of

computational performance was needed to ensure the data was processed quickly

and accurately to meet current industries standard of efficiency.

CFD

software is a tool where knowledge of fluid mechanics, mathematics and computer

science were applied in simulating a fluid flow motion. These motions are

derived by mathematical equations and represented in computer programming

language only readable by computers. The results were then converted to quantifiable

data that is displayed in readable human language. During the simulation in

CFD, all progress was predicted using numerical data as from derivation from

all available mathematical equations in fluid dynamic flow.

While

fluid is solved in mathematical solutions, there are still fundamental steps

required to be obeyed. (Fawehinmi et al., 2005). There are three

methods in this problem solving through Experimental, Analytical and

Computational solving where data are validated. Back when there is no

computational aid such as CFD, Experimental solving uses a more tedious

technique which is to build a scaled down replica model representing the real

object of study and the flow properties are to be measured and recorded. This

technique is frequently used, where experimental data were all recorded, and

the results were compared to theoretical values derived from mathematical

equations from fluid dynamics. It is different from Analytical solving as

analytical does not require any physical model to obtain theoretical data. The

method involved for analytical is by comparing measurements of the desired object

of study in mathematical modelling to related equations from equations of fluid

dynamics. But Analytical solving applies to a limited number of simplified flow

geometries. There were recorded errors from the above methods of solving which

leads to search for improvement in error reduction while accurately obtain

results subjected to fluid dynamics.

Through

years of testing and perfecting, CFD software is now able to display results

accurately while having little to no error. It applies to any fluid flow, be it

simple or complex fluid flow. With such accuracy, computational solving method

had to be ideal choice of studying fluid flow problems as the results were more

reliable albeit the other methods, Experimental and Analytical solving approach

were still being used.

Fluid

flows are controlled and influenced by partial differential equations that is

represented by laws of Conservation of Mass, Momentum and Energy. CFD uses the

above governing equations and below represented by following Navier-Stokes equations

based from conservation laws:

a) Conservation

of Mass (from continuity equation)

b) Conservation

of Momentum (from Momentum equation)

Where;

i: Local change of time

ii: Momentum convection

iii: Surface force

iv: Molecular-dependent momentum exchange

(diffusion)

v: Mass force

c) Conservation

of Energy (from Energy equation)

Where;

i: Local energy change with time

ii: Convection term

iii: Pressure work

iv: Heat flux (diffusion)

v: Irreversible transfer of mechanical energy into

heat

When all the

conservation equations were applied to Navier-Stokes equation, the following

simplified general form is formed:

Since

CFD was used due to its high efficiency and low risks, it also improves safety

while saving on production costs. An example would be the use of wind tunnel to

determine efficiency of a car model cutting through the air (fluid flow) will

increase costs in Research and Development sector of a company. By using CFD as

the base to study fluid flow on the desired model, less electricity would be

used on simulation compared to a physical wind tunnel. Costs of material will

also be saved since no models were needed and changes to the car model to

improve efficiency and performance can be done without wastage of any

materials. Besides that, CFD was also to be used in ensure minimal losses in

piping by simulating the pipe designs with different angles to reduce losses at

bends of pipe, attachment of valves, taper and other pipe attachments. (Gabryjonczyk, 2013).

Therefore, this CFD software can solve many fluid problems, preventing design

failure while ensuring safety of its design for prototype or production models.

Here

is the order of the process of CFD software based from the 5th slide

of (Choudhary,

2015):

Figure 1: Flow chart of

Computational Fluid Dynamics

The

cycle of CFD is not that complex to understand to begin with as it all begins

with the problem related to fluid where humans tend to solve from their

knowledge of Fluid Mechanics. This data is then brought into Navier-Stokes

Equations to determine the nature of the flow. This data is then translated

into Discretized Form as a computer only understood programming language. In

this form, the computer can analyse and compute the entered data, Grid by Grid

for each mesh on the model. After the meshing process, simulation is executed

to show every result obtained from fluid flow. The results are then converted

from programming language to human language for quantifying and displayed for

user to compare and analyse the data.