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1. INTRODUCTION

Heat Up processing is the basic step for the workload in melting and heat treatment for

further processing. It is also an energy-intensive process. Thus, correct predicition of the

temperature variation and distrubution in the workload is of significance to ensure the final

quality of the parts and to reduce energy consumption and time as well.

There has been a dramatic increase in recent years in the use of comprehensive CFD

based computational tools to model industrial processes within the Chemical, Metallurgical,

and Power Generation Industries. For many in these industries, this has been a significant

change in philosophy. Historically, these industries relied on cold-flow experimental scale

models or 1-D zonal computational models to perform product/process R&D and to guide

scaling of pilot or laboratory data for full size facilities. The motivations for the paradigm

shift are varied: pressures for increased productivity; global expansion of markets; tighter

pollution emission regulations; reducing the time to develop new products; and reducing the

time to bring new processes plants into full production.

It is established that mathematical model for the heating of workpiece in drying furnace

heat treatment furnace, while, the workpiece is assumed to be three dimensional and only

single workpiece is considered.

Using a reliable computational model which provides full coupling of all relevant

physical processes allows the engineer to determine sensitivities to design changes without

relying on simplified empirical models that are often extrapolated beyond the conditions for

which their validity has been proved. To properly model industrial systems requires

computational tools that properly describe the first-order physics and chemistry that drive the

system.

In recent years, the computational fluid dynamics (CFD) based on conservation

equations has become a viable technique for process simulation So in this work we are going

to compute the temperature profile generated Using CFD and GAMBIT for a given

temperature source, it can help to Compute the energy required and to optimize it.

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1.1. Basics of Furnace

A furnace is an equipment used to melt metals for casting or to heat materials to change

their shape (e.g. rolling, forging) or properties (heat treatment).

Since flue gases from the fuel come in direct contact with the materials, the type of fuel

chosen is important. For example, some materials will not tolerate sulphur in the fuel. Solid

fuels generate particulate matter, which will interfere the materials placed inside the furnace.

For this reason:

Since flue gases from the fuel come in direct contact with the materials, the type of fuel

chosen is important. For example, some materials will not tolerate sulphur in the fuel. Solid

fuels generate particulate matter, which will interfere the materials placed inside the furnace.

For this reason:

Most furnaces use liquid fuel, gaseous fuel or electricity as energy input.

Induction and arc furnaces use electricity to melt steel and cast iron.

Melting furnaces for nonferrous materials use fuel oil.

Oil-fired furnaces mostly use furnace oil, especially for reheating and heat treatment of

materials.

Light diesel oil (LDO) is used in furnaces where sulphur is undesirable.

Furnace ideally should heat as much of material as possible to a uniform temperature

with the least possible fuel and labor. The key to efficient furnace operation lies in complete

combustion of fuel with minimum excess air. Furnaces operate with relatively low

efficiencies (as low as 7 percent) compared to other combustion equipment such as the boiler

(with efficiencies higher than 90 percent. This is caused by the high operating temperatures in

the furnace. For example, a furnace heating materials to 1200 oC will emit exhaust gases at

1200o C or more, which results in significant heat losses through the chimney.

For drying of solar cell metallization paste centrotherm photovoltaics provides drying

furnaces targeted for high volume production. Key features of centrotherm drying furnaces

include high throughput and uptime, powerful drying capabilities, modular build-up. Air

flows used for drying can be adjusted module-by-module precisely, which allows excellent

drying process setup and control. Our drying furnace comes with a powerful wafer cooling

stage. Cooling is achieved by closed loop air flows and air to cooling water heat exchangers.

This yields processed wafer delivery with nearly ambient temperature. Optionally, the

furnaces are available with exhaust condensers for exhaust gas cleaning.

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An industrial furnace or direct fired heater is equipment used to provide heat for a

process or can serve as reactor which provides heats of reaction. Furnace designs vary as to its

function, heating duty, type of fuel and method of introducing combustion air. However, most

process furnaces have some common features.

Fuel flows into the burner and is burnt with air provided from an air blower. There can

be more than one burner in a particular furnace which can be arranged in cells which heat a

particular set of tubes. Burners can also be floor mounted, wall mounted or roof mounted

depending on design. The flames heat up the tubes, which in turn heat the fluid inside in the

first part of the furnace known as the radiant section or firebox. In this chamber where

combustion takes place, the heat is transferred mainly by radiation to tubes around the fire in

the chamber. The heating fluid passes through the tubes and is thus heated to the desired

temperature. The gases from the combustion are known as flue gas. After the flue gas leaves

the firebox, most furnace designs include a convection section where more heat is recovered

before venting to the atmosphere through the flue gas stack.

Radiation section:

Radiation section is where the tubes receive almost all its heat by radiation from the

flame. In a vertical, cylindrical furnace, the tubes are vertical. Tubes can be vertical or

horizontal, placed along the refractory wall, in the middle, etc., or arranged in cells.

Convection section:

The convection section is located above the radiant section where it is cooler to recover

additional heat. Heat transfer takes place by convection here, and the tubes are finned to

increase heat transfer. The first two tube rows in the bottom of the convection section and at

the top of the radiant section is an area of bare tubes (without fins) and are known as the

shield section, so named because they are still exposed to plenty of radiation from the firebox

and they also act to shield the convection section tubes, which are normally of less resistant

material from the high temperatures in the firebox.

Burner:

The burner in the vertical, cylindrical furnace as above is located in the floor and fires

upward. Some furnaces have side fired burners, e.g.: train locomotive. The burner tile is made

of high temperature refractory and is where the flame is contained in. Air registers located

below the burner and at the outlet of the air blower are devices with movable flaps or vanes

that control the shape and pattern of the flame, whether it spreads out or even swirls around.

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1.2. CFD Process

Preprocessing is the first step in building and analyzing a flow model. It includes

building the model (or importing from a CAD package), applying the mesh, and entering the

data. We used Gambit as the preprocessing tool in our project.

There are four general purpose products: FLUENT, Flowizard, FIDAF, and POLY

FLOW. FLUENT is used in most industries All Fluent software includes full post processing

capabilities.

1.3. GAMBIT CFD Processor

Fast geometry modeling and high quality meshing are crucial to successful use of

CFD.GAMBIT gives us both. Explore the advantage:

Ease of use

CAD/CAE Integration

Fast Modeling

CAD Cleanup

Intelligent Meshing

EASE-OF-USE

GAMBIT has a single interface for geometry creation and meshing that brings together all of

Fluent’s preprocessing technologies in one environment. Advanced tools for journaling let us

edit and conveniently replay model building sessions for parametric studies.

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2. THEORY

2.1. Heat Transfer

Heat is defined as energy transferred by virtue of temperature difference or gradient.

Being a vector quantity, it flows with a negative temperature gradient. In the subject of heat

transfer, it is the rate of heat transfer that becomes the prime focus. The transfer process

indicates the tendency of a system to proceed towards equilibrium. There are 3 distinct modes

in which heat transfer takes place:

2.1.1. Heat Transfer by Conduction

Conduction is the transfer of heat between 2 bodies or 2 parts of the same body through

molecules. This type of heat transfer is governed by Fourier`s Law which states that – “Rate

of heat transfer is linearly proportional to the temperature gradient”. For 1-D heat conduction.

qk=−kdTdx

(1)

2.1.2. Heat Transfer by Radiation

Thermal radiation refers to the radiant energy emitted by the bodies by virtue of their

own temperature resulting from the thermal excitation of the molecules. It is assumed to

propagate in the form of electromagnetic waves and doesn’t require any medium to travel.

The radiant heat exchange between 2 gray bodies at temperature T1 and T2 is given by:

Q1−2=σ A1 F1−2(T 14−T2

4) (2)

2.1.3. Heat Transfer by Conduction

When heat transfer takes place between a solid surface and a fluid system in motion, the

process is known as Convection. When a temperature difference produces a density difference

that results in mass movement, the process is called Free or Natural Convection.

When the mass motion of the fluid is carried by an external device like pump, blower or

fan, the process is called Forced Convection. In convective heat transfer, Heat flux is given

by:

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q ( x )=hx (T w−T ∞) (3)

2.1.3.1. Reynolds Number

It is the ratio of inertial forces to the viscous forces. It is used to identify different flow

regimes such as Laminar or Turbulent.

ℜ= ρVDμ

=VDv

= Inertial ForcesViscous Forces

(4)

2.1.3.2. Nusselt Number

The nusselt number is a dimensionless number that measures the enhancement of heat

transfer from a surface that occurs in a real situation compared to the heat transferred if just

conduction occurred.

NuL=hLk f

(5)

2.1.3.3. Prandtl Number

It is the ratio of momentum diffusivity (viscosity) and thermal diffusivity.

Pr= v∝

(6)

2.1.3.4. Grashof Number

It is the ratio of Buoyancy force to the viscous force acting on a fluid.

Gr=gβ (T s−T∞) L3

v2

(7)

2.1.3.5. Rayleigh Number

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It is the product of Grashof number and Prandtl number. It is acdimensionless number

that is associated with buoyancy driven flow i.e. Free or Natural Convection. When the

Rayleigh number is below the critical value for that fluid, heat transfer is primarily in the form

of conduction; when it exceeds the critical value, heattransfer is primarily in the form of

convection.

Rax=Gr x Pr=gβvα

(T s−T ∞)x3 (8)

2.2. Computational Fluid Dynamics (CFD) and Fluent

Computational fluid dynamics (CFD) is one of the branches of fluid mechanics that uses

numerical methods and algorithms to solve and analyze problems that involve fluid flows.

The fundamental basis of any CFD problem is the Navier-Stokes equations, which define any

single-phase fluid flow. These equations can be simplified by removing terms describing

viscosity to yield the Euler equations. Further simplification, by removing terms describing

vorticity yields the full potential equations. Finally, these equations can be linearized to yield

the linearized potential equations.

2.2.1. Governing Equations

Continuity Equation:

∂ p∂ t

+∇ ( ρv )=DρDt

+ ρ∇v=0 (9)

Conservation of Momentum equation for an incompressible fluid is:

ρDuDt

=ρ gx−∂ p∂ x

+μ(∆2u) (10)

Energy Equation:

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(u ∂ T∂ x

+v∂T∂ y

+w∂T∂ )=1

α ( ∂2T∂ y2 +

∂2 T∂ z2 )

(11)

2.2.2. Discretization Methods

The stability of the chosen discretization is generally established numerically rather than

analytically as with simple linear problems. Special care must also be taken to ensure that the

discretization handles discontinuous solutions gracefully. The Euler equations and Navier-

Stokes equations both admit shocks, and contact surfaces. Some of the discretization methods

being used are:

2.2.2.1. Finite Volume Method

This is the "classical" or standard approach used most often in commercial software and

research codes. The governing equations are solved on discrete control volumes. This integral

approach yields a method that is inherently conservative. (i.e., quantities such as density

remain physically meaningful)

A finite volume method (FVM) discretization is based upon an integral form of the PDE

to be solved (e.g. conservation of mass, momentum, or energy). The PDE is written in a form

which can be solved for a given finite volume (or cell). The computational domain is

discretized into finite volumes and then for every volume the governing equations are solved.

The resulting system of equations usually involves fluxes of the conserved variable, and thus

the calculation of fluxes is very important in FVM. The basic advantage of this method over

FDM is it does not require the use of structured grids, and the effort to convert the given mesh

in to structured numerical grid internally is completely avoided. As with FDM, the resulting

approximate solution is a discrete, but the variables are typically placed at cell centers rather

than at nodal points. This is not always true, as there are also face-centered finite volume

methods. In any case, the values of field variables at nonstorage locations (e.g. vertices) are

obtained using interpolation.

∂∂ t∫∫∫QdV +∫∫ FdA=0 (12)

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Where Q is the vector of conserved variables, F is the vector of fluxes (see Euler

equations or Navier-Stokes equations), V is the cell volume, and is the cell surface area.

2.2.2.2. Finite Element Method

This method is popular for structural analysis of solids, but is also applicable to fluids.

The FEM formulation requires, however, special care to ensure a conservative solution. The

FEM formulation has been adapted for use with the Navier-Stokes equations. In this method,

a weighted residual equation is formed:

Ri=∫∫∫W iQd V e (13)

A finite element method (FEM) discretization is based upon a piecewise representation

of the solution in terms of specified basis functions. The computational domain is divided up

into smaller domains (finite elements) and the solution in each element is constructed from

the basis functions. The actual equations that are solved are typically obtained by restating the

conservation equation in weak form: the field variables are written in terms of the basis

functions, the equation is multiplied by appropriate test functions, and then integrated over an

element. Since the FEM solution is in terms of specific basis functions, a great deal more is

known about the solution than for either FDM or FVM. This can be a double-edged sword, as

the choice of basis functions is very important and boundary conditions may be more difficult

to formulate. Again, a system of equations is obtained (usually for nodal values) that must be

solved to obtain a solution.

Comparison of the three methods is difficult, primarily due to the many variations of all

three methods. FVM and FDM provide discrete solutions, while FEM provides a continuous

(up to a point) solution. FVM and FDM are generally considered easier to program than FEM,

but opinions vary on this point. FVM are generally expected to provide better conservation

properties, but opinions vary on this point also.

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Where Ri is the equation residual at an element vertex i , Q is the conservation equation

expressed on an element basis, Wi is the weight factor and Ve is the volume of the element.

2.2.2.3. Finite Difference Method

This method has historical importance and is simple to program. It is currently only used in

few specialized codes. Modern finite difference codes make use of an embedded boundary for

handling complex geometries making these codes highly efficient and accurate. Other ways to

handle geometries are using overlappinggrids, where the solution is interpolated across each

grid.

A finite difference method (FDM) discretization is based upon the differential form of

the PDE to be solved. Each derivative is replaced with an approximate difference formula

(that can generally be derived from a Taylor series expansion). The computational domain is

usually divided into hexahedral cells (the grid), and the solution will be obtained at each nodal

point. The FDM is easiest to understand when the physical grid is Cartesian, but through the

use of curvilinear transforms the method can be extended to domains that are not easily

represented by brick-shaped elements. The discretization results in a system of equation of the

variable at nodal points, and once a solution is found, then we have a discrete representation

of the solution.

∂ Q∂ t

+ ∂ F∂ x

+ ∂G∂ y

+ ∂ H∂ z

=0

(14)

Where Q is the vector of conserved variables, and F, G, and H are the fluxes in the x, y, and z

directions respectively.

2.2.3. Schemes to Calculate Face Value for variables

2.2.3.1. Central Difference Scheme

This scheme assumes that the convective property at the interface is the average of the

values of its adjacent interfaces.

2.2.3.2. The UPWIND Scheme

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According to the upwind scheme, the value of the convective property at the interface is

equal to the value at the grid point on the upwind side of the face. It has got 3 sub-schemes i.e.

1st order upwind which is a 1st order accurate, 2nd order upwind which is 2nd order accurate

scheme and QUICK (Quadratic Upwind Interpolation for convective kinematics) which is a

3rd order accurate scheme.

2.2.3.3. The EXACT Solution

According to this scheme, in the limit of zero Peclet number, pure diffusion or

conduction problem is achieved and φ vs x variation is linear. For positive values of P, the

value of φ is influenced by the upstream value. For large positive values, the value of φ seems

to be very close to the upstream value.

2.2.3.4. The EXPONENTIAL Scheme

When this scheme is used, it produces exact solution for any value of Peclet number and

for any value of grid points. It is not widely used because exponentials are expensive to

compute.

2.2.3.5. The HYBRID Scheme

The significance of hybrid scheme can be understood from the fact that (i) it is

identical with the Central Difference scheme for the range -2 ≤ Pe ≤ 2 (ii) outside, it

reduces to the upwind scheme.

2.3. How Does a CFD Code Work?

CFD codes are structured around the numerical algorithms that can be tackle fluid

problems. In order to provide easy access to their solving power all commercial CFD

packages include sophisticated user interfaces input problem parameters and to examine the

results. Hence all codes contain three main elements:

1. Pre-processing

2. Solver

3. Post-processing

2.3.1. Pre-processing

This is the first step in building and analyzing a flow model. Preprocessor consist of

inputof a flow problem by means of an operator –friendly interface and subsequent

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transformation of this input into form of suitable for the use by the solver. The user activities

at the Preprocessing stage involve:

Definition of the geometry of the region: The computational domain.

Grid generation the subdivision of the domain into a number of smaller,

nonoverlapping sub domains (or control volumes or elements Selection of physical or

chemical phenomena that need to be modeled).

Definition of fluid properties

Specification of appropriate boundary conditions at cells, which

coincide with or touch the boundary. The solution of a flow problem (velocity,

pressure, temperature etc.) is defined at nodes inside each cell. The accuracy of CFD

solutions is governed by number of cells in the grid. In general, the larger numbers of

cells better the solution accuracy. Both the accuracy of the solution & its cost in terms

of necessary computer hardware & calculation time are dependent on the fineness of

the grid. Efforts are underway to develop CFD codes with a (self) adaptive meshing

capability. Ultimately such programs will automatically refine the grid in areas of

rapid variation.

GAMBIT (CFD PREPROCESSOR): GAMBIT is a state-of-the-art preprocessor for

engineering analysis. With advanced geometry and meshing tools in a powerful, flexible,

tightly-integrated, and easy-to use interface, GAMBIT can dramatically reduce preprocessing

times for many applications. Complex models can be built directly within GAMBIT’s solid

geometry modeler, or imported from any major CAD/CAE system. Using a virtual geometry

overlay and advanced cleanup tools, imported geometries are quickly converted into suitable

flow domains. A comprehensive set of highly-automated and size function driven meshing

tools ensures that the best mesh can be generated, whether structured, multiblock,

unstructured, or hybrid.

2.3.2. Solver

The CFD solver does the flow calculations and produces the results. FLUENT,

FloWizard.

FIDAP, CFX and POLYFLOW are some of the types of solvers. FLUENT is used in

most industries. FloWizard is the first general-purpose rapid flow modeling tool for design

and process engineers built by Fluent. POLYFLOW (and FIDAP) are also used in a wide

range of fields, with emphasis on the materials processing industries. FLUENT and CFX two

solvers were developed independently by ANSYS and have a number of things in common,

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but they also have some significant differences. Both are control-volume based for high

accuracy and rely heavily on a pressure-based solution technique for broad applicability. They

differ mainly in the way they integrate the fluid flow equations and in their equation solution

strategies. The CFX solver uses finite elements (cell vertex numerics), similar to those used in

mechanical analysis, to discretize the domain. In contrast, the FLUENT solver uses finite

volumes (cell centered numerics). CFX software focuses on one approach to solve the

governing equations of motion (coupled algebraic multigrid), while the FLUENT product

offers several solution approaches (density-, segregated- and coupled-pressure-based

methods)

The FLUENT CFD code has extensive interactivity, so we can make changes to the

analysis at any time during the process. This saves time and enables to refine designs more

efficiently. Graphical user interface (GUI) is intuitive, which helps to shorten the learning

curve and make the modeling process faster. In addition, FLUENT's adaptive and dynamic

mesh capability is unique and works with a wide range of physical models. This capability

makes it possible and simple to model complex moving objects in relation to flow. This solver

provides the broadest range of rigorous physical models that have been validated against

industrial scale applications, so we can accurately simulate real-world conditions, including

multiphase flows, reacting flows, rotating equipment, moving and deforming objects,

turbulence, radiation, acoustics and dynamic meshing. The FLUENT solver has repeatedly

proven to be fast and reliable for a wide range of CFD applications. The speed to solution is

faster because suite of software enables us to stay within one interface from geometry

building through the solution process, to postprocessing and final output.

The numerical solution of Navier–Stokes equations in CFD codes usually implies a

discretization method: it means that derivatives in partial differential equations are

approximated by algebraic expressions which can be alternatively obtained by means of the

finite-difference or the finite-element method. Otherwise, in a way that is completely different

from the previous one, the discretization equations can be derived from the integral form of

the conservation equations: this approach, known as the finite volume method, is

implemented in FLUENT (FLUENT user’s guide, vols. 1–5, Lebanon, 2001), because of its

adaptability to a wide variety of grid structures. The result is a set of algebraic equations

through which mass, momentum, and energy transport are predicted at discrete points in the

domain. In the freeboard model that is being described, the segregated solver has been chosen

so the governing equations are solved sequentially. Because the governing equations are non-

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linear and coupled, several iterations of the solution loop must be performed before a

converged solution is obtained and each of the iteration is carried out as follows:

(1) Fluid properties are updated in relation to the current solution; if the calculation is at

the first iteration, the fluid properties are updated consistent with the initialized solution.

(2) The three momentum equations are solved consecutively using the current value for

pressure so as to update the velocity field.

(3) Since the velocities obtained in the previous step may not satisfy the continuity

equation, one more equation for the pressure correction is derived from the continuity

equation and the linearized momentum equations: once solved, it gives the correct pressure so

that continuity is satisfied. The pressure–velocity coupling is made by the SIMPLE algorithm,

as in FLUENT default options.

(4) Other equations for scalar quantities such as turbulence, chemical species and

radiation are solved using the previously updated value of the other variables; when

interphase coupling is to be considered, the source terms in the appropriate continuous phase

equations have to be updated with a discrete phase trajectory calculation.

(5) Finally, the convergence of the equations set is checked and all the procedure is

repeated until convergence criteria are met.

The conservation equations are linearized according to the implicit scheme with respect

to the dependent variable: the result is a system of linear equations (with one equation for

each cell in the domain) that can be solved simultaneously. Briefly, the segregated implicit

method calculates every single variable field considering all the cells at the same time. The

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Figure 2.1: Algorithm of numerical approach used by simulation softwares

code stores discrete values of each scalar quantity at the cell centre; the face values must be

interpolated from the cell centre values. For all the scalar quantities, the interpolation is

carried out by the second order upwind scheme with the purpose of achieving high order

accuracy. The only exception is represented by pressure interpolation, for which the standard

method has been chosen.

2.3.3. Post-processing

This is the final step in CFD analysis, and it involves the organization and interpretation

of the predicted flow data and the production of CFD images and animations. Fluent's

software includes full post processing capabilities. FLUENT exports CFD's data to third-party

postprocessors and visualization tools such as Ensight, Fieldview and TechPlot as well as to

VRML formats. In addition, FLUENT CFD solutions are easily coupled with structural codes

such as ABAQUS, MSC and ANSYS, as well as to other engineering process simulation

tools.

Thus FLUENT is general-purpose computational fluid dynamics (CFD) software

ideallysuited for incompressible and mildly compressible flows. Utilizing a pressure-based

segregated finite-volume method solver, FLUENT contains physical models for a wide range

of applications including turbulent flows, heat transfer, reacting flows, chemical mixing,

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combustion, and multiphase flows. FLUENT provides physical models on unstructured

meshes, bringing you the benefits of easier problem setup and greater accuracy using

solution-adaptation of the mesh.

FLUENT is a computational fluid dynamics (CFD) software package to simulate fluid

flow problems. It uses the finite-volume method to solve the governing equations for a fluid.

It provides the capability to use different physical models such as incompressible or

compressible, inviscid or viscous, laminar or turbulent, etc. Geometry and grid generation is

done using GAMBIT which is the preprocessor bundled with FLUENT. Owing to increased

popularity of engineering work stations, many of which has outstanding graphics capabilities,

the leading CFD are now equipped with versatile data visualization tools. These include:

Domain geometry & Grid display.

Vector plots.

Line & shaded contour plots.

2D & 3D surface plots.

Particle tracking.

View manipulation (translation, rotation, scaling etc.)

2.3.4. Advantages of CFD

Major advancements in the area of gas-solid multiphase flow modeling offer substantial

process improvements that have the potential to significantly improve process plant

operations. Prediction of gas solid flow fields, in processes such as pneumatic transport lines,

risers, fluidized bed reactors, hoppers and precipitators are crucial to the operation of most

process plants. Up to now, the inability to accurately model these interactions has limited the

role that simulation could play in improving operations. In recent years, computational fluid

dynamics (CFD) software developers have focused on this area to develop new modeling

methods that can simulate gas-liquid-solid flows to a much higher level of reliability. As a

result, process industry engineers are beginning to utilize these methods to make major

improvements by evaluating alternatives that would be, if not impossible, too expensive or

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time-consuming to trial on the plant floor. Over the past few decades, CFD has been used to

improve process design by allowing engineers to simulate the performance of alternative

configurations, eliminating guesswork that would normally be used to establish equipment

geometry and process conditions. The use of CFD enables engineers to obtain solutions for

problems with complex geometry and boundary conditions. A CFD analysis yields values for

pressure, fluid velocity, temperature, and species or phase concentration on a computational

grid throughout the solution domain. Advantages of CFD can be summarized as:

It provides the flexibility to change design parameters without the expense of

hardware changes. It therefore costs less than laboratory or field experiments, allowing

engineers to trymore alternative designs than would be feasible otherwise.

It has a faster turnaround time than experiments.

It guides the engineer to the root of problems, and is therefore well suited for

troubleshooting.

It provides comprehensive information about a flow field, especially in regions where

measurements are either difficult or impossible to obtain.

3. SETUP

3.1. Furnace

The furnace that is used in the project is a drying furnace. It is operated to heat material,

generally aluminum profiles, with intend to harden the strength of material ant its quality.

There are some identifications of the furnace:

FURNACE QUANTITYExterior Width 1,300 mm.Exterior Height 3,300 mm.Exterior Length 20,850 mm.Isolatıon Fibreglass

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Thermoblock Isolation 150 mm FibreglassTypes of Heat Natural Gas or LPG BurnerFurnace Firebox AISI 309-AISI 310Thermal Capacity 350,000 kcal / hCirculation Fan 20,000 m3/hExhaust Fan 1,500 m3/h

Table 3.1: Furnace Quantities

The furnace has a symetric profile, two inlets having a dimension of 700mmx400mm

and one outlet having a dimension of 2825mmx700mm. Hot air is blowed through the two

inlet with a high velocity to the furnace. On the other hand, it is vacuumed from the velocity

outlet. Profiles go through the furnace via conveyor, where profiles are hung on its hooks, wih

a constant amount of speed. The both side of the furnace has a single air curain to avoid the

heat loss. At bottom side of the furnace, there are 18 interoior cavities wihch are sized at

1000mmx150mm. Interior cavities oparetes circulation motion by directing particles of air.

These interiors are to serve air into to chamber of the furnace homogenously.

Figure3.1: Technical Drawing

3.2. Geometry

To draw the furnace in Gambit, first of all, we started with creating faces at the bottom

side. The face created was copied by 20 times to get the bottom region ant cavities, then

complied the body part in Step 3. In Step 4 we dealed with curvelinear shape. Then, we

completed the parts below body part. After that, we substracted related faces from the whole

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part.Finally, we created whole faces that we are going to use for boundry conditions. To

complete the above instructions, we followed the below commands:

Step 1

Geometry→Face→Create Face

Figuere 3.2: Face Command Figure3.3: Face

Step 2:

Geometry→Face→Move/Copy Faces

Figure3.4: Move/Copy Command

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Figure 3.5: Copied Faces

Step 3:

Figure 3.6: Furnace Body Faces

Step 4

Geometry→Edge→Create Circular Arc

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Figure 3.7: Circular Arc Command

Step 5:

Geometry→Volume→Form Volume

Figure 3.8: Volume Form Command

Figure 3.9: Faces with Arc

Step 6:

Geometry→Volume→Substract Volume

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Figure3.10: Substract Volumes Command

Figure 3.11: Susbtracted Faces

Figure 3.12: The Final Drawing

3.3. Zone

After finising geometry, we determine the boundry conditions to use them for next

operations. Also there are another type of boundry conditions called as continuum types. In

continuum condition, all volumes are selected as fluid. In the furnace, we have specified that;

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Air Curtains → {Velocity Inlet 1Velocity I nlet 2

Hot Air Inlet → {Velocity Inlet 3Velocity Inlet 4

Air Curtains → {PressureOutlet 1PressureOutlet 2

Hot Air Outlet → Pressure Outlet 3

Surrounding → {PressureOutlet 4Pressure Outlet 5

Walls and Interiors

Figure 3.13: Specifying Boundry Types

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Figure 3.14: Boundry Conditions

Figure 3.15: Continuum Boundry

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3.4. Meshing

The last operation in GAMBIT is to be meshing. The object is meshed with 1.2 growth

rate, maximum size 2 and spacing 1. It also use Tet/Hybrid and TGrid.

For meshing, we gave size functions changing between a range. Size function are

applied because it is very hard and sometimes imposible to mesh because of details of

geometry. Size Function helps with resize more sensitively rather than normal size.

Size Function 1

It was chosen as from 30 to 250 with a1.2 growth rate. It is used on interiors as faces

and up and down volumes.

Size Function 2 – Size Function 3

Velocity inlet 1 and velocity inlet 2 are faces. Air curtains are said to be volumes and

size functions was from 30 to 250 with a growth rate of 1.2.

Size Function 4 – Size Function 5

Velocity inlet 3 and velocity inlet 4 were selected as faces and volumes are bottom large

volumes. It ranges from 40 to 100 with 1.2 growth rate.

Size Function 6

Pressure outlet 3 is selected as the face and volume is the upper CO (BURASI NE

ERAY?) It has a size function range of 50-250.

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Figure 3.16: Mesh Volumes

Figure 3.17: Mesh

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Figure 3.18: Size Function 1

Figure 3.19: Size Function Function 2-3

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Figure 3.20: Size Function 4-5 Figure 3.21: Size Function 6

3.5. Fluent

3.5.1. Case 1

In Case 1, we have used the boundry conditions symmetrically. The two inlets have the

same amount of mass of rate. Pressure outlet and velocity inlets boundries are to be as:

Pressure Outlet 1

Constant Gauge Pressure = - 19.97 Pa

Blackflow Turbulent Intensity= 10%

Blacflow Total Temperature= 430K

Hydraulic Diameter: 0.181

Pressure Outlet 3

Constant Gauge Pressure: - 5.81

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Blackflow Turbulent Intensity: 10%

Blacflow Total Temperature: 430K

Hydraulic Diameter: 1.12

Velocity Inlet 1

Velocity Magnitude: 7 m/s

Turbulent Intensity: 10%

Hydraulic Diameter: 0.181

Velocity Inlet 3

Velocity Magnitude: 9.92 m/s

Turbulent Intensity: 10%

Hydraulic Diameter: 0.51

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Figure 3.22: Grid Check

Figure 3.23: Grid Size

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Figure 3.24: Solver

Figure3.25: Memory Usage

31

Figure 3.26.a: Materials

Figure 3.26.b: Materials

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Figure 3.26.c: Materials

Figure 3.27: Thermal Expansion Coefficient

Boundry Conditions:

Figure 3.28: Air as Fluid Material

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Pressure Outlet

Figure 3.29.a: Pressure Outlet

Figure 3.29.b: Pressure Outlet

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Figure 3.29.c: Pressure Outlet

Figure 3.29.d: Pressure Outlet

35

Figure 3.30.a: Velocity Inlet

Figure 3.30.b: Velocity Inlet

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Figure 3.31.a: Wall Properties

Figure 3.31.b: Wall Properties

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3.5.2. CASE 2

In first case, we assumed that boundries are to be equal.On the other hand, the second

case we have changed mass flow rate of the two inlets as 9/10 proportion. To stabilize the

pressure, we have changed also the pressure outlet boundries.

Velocity Inlets

Figure 3.32.a: Velocity Inlet 3

Figure 3.32.b: Velocity Inlet 3

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Figure 3.33.a: Velocity Inlet 4

Figure 3.33.b: Velocity Inlet 4

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Pressure Outlets

Figure3.34.a: Pressure Outlet 1

Figure3.34.b: Pressure Outlet 1

40

Figure 3.35.a: Pressure Outlet 3

Figure 3.35 .b: Pressure Outlet 3

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4. RESULTS

4.1. Case1

Residuals are shown in Figure 3.21, x y and z velocities decreases linerarly and moves

constantly at 10-5 since 3000 iterations. Continuity and epsilon values become constant faster

at about 2000 iterations versus 10-4. Energy moves up and down in a frequency at first, then it

goes straight when iteration is about 1000 versus 10-5.

Figure 4.1: Residuals

Figure 4.2: Temperature Distribution

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Figure 4.3: Velocity Distribution

Figure 4.4: Pressure Distribution

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Figure 4.5: Wall Temperature Distribution (Outer)

Figure 4.6: Wall Temperature Distribution (Inner)

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4.2. Case 2

Figure 4.7: Residuals

Figure 4.8: Temperature Distribution

,

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Figure 4.9: Velocity Distribution

Figure 4.10: Dynamic Pressure Distribution

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Figure 4.11: Wall Temperature Distribution (Outer)

Figure 4.12: Wall Temperature Distribution (Inner)

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Mid-point Temperature 476 K

Inner Wall Temperature (min) – (max) 311 K -453 K

Outer Wall Temperature (min) – (max) 358 K – 458 K

Table 4.1: Results – Case 1

Inner Wall Temperature (min) – (max) 311 K – 451 K

Outer Wall Temperature (min) – (max) 341 K – 458 K

Table 4.2: Results – Case 2

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5. CONCLUSION

In this study, we have investigated the hot air motion of drying furnace and heat

transfer simulation.

There are two cases of furnace design to analyse. The first case represent a furance

control volume that has equal boundries acording to symetry axis. It can be said that it is the

uniform case. On the other hand, the second case of furnace analysis is seen that mass flowe

rayes of velocity inlets are changed with a proportion of 9 to 10. To balance the equilibrium

of pressure, air curtains on both sides has to have verse proportional of that.

We analysed the temperature at the center point of furnace. Because it would be the

average of the furnace especially for the first case. In addition, we have dealed with isolation

of the walls, chosen an inner and outer wall temperature.

In this result, we choose maximum values of inner and outer wall temperature as in

furnace firebox. It apperantly comes that the final results are aplicable and close to each case.

By contrast with Fluent, the inner and outer walls were chosen vice versa. Therefore,

results of inner temperature represent the outer wall temperature of the furnace.

For a new subject of thesis, it would be investigated that efficiency of heat transfer

could be investigated when aluminum profiles go through the hotspot. This report could be a

guide to the new project.

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6. REFERENCES

[1] Peeyush Agarwal. 2009 “Simulation of Heat Transfer Phenomenon in Furnace Using

FLUENT-GAMBIT” Page 23-25, National Institute of Technology Rourkela

[2] Amritraj Bhanja, 2009 “Numerical Solution of Three Dimensional Conjugate Heat

Transfer in a Microchannel Heat Sink and CFD National Institute of Technology Rourkela

[3] Suhas V. Patankar, Numerical Heat Transfer and Fluid Flow

[4] www.fluent.com/ software/ gambit

[5] FLUENT documentation.

[6] Michael J. Bockelie, Bradley R. Adams, Marc A. Cremer, Kevin A. Davis,Eric G.

Eddings, James R. Valentine, Philip J. Smith, Michael P. Heap. ‘’Computational

Simulations of Industrial Furnaces’’

[7] www.cfd-online.com

[8] Yunus A. Çengel, Michael A. Boles. ‘‘ Thermodynamics An Engineering Aproach’’

5th Edition.

[9] Incropera. ‘‘Fundamentals Heat and Mass Transfer’’ 6th Edition.

[10] hyperphysics.phy-astr.gsu.edu/hbase/tables/thrcn.html

[11] Zinedine Khatir, Joe Paton, Harvey Thompson, Nik Kapur, Vassili Toropov, Malcolm

Lawes, Daniel Kirk. 2011 ‘‘Computational fluid dynamics (CFD) investigation of air flow

and temperature distribution in a small scale bread-baking oven’’ School of Mechanical

Engineering, University of Leeds, LS2 9JT, United Kingdom.

[11] confluence.cornell.edu/display/SIMULATION/FLUENT+Learning+Modules

[12] web.mst.edu/~isaac/aeme339.html/fs05_files/cyl_wake_tg.pdf

[13] Cornell University – Online Cources – Fluent

[14] Mark White, ‘‘Fluid Mechanics’’ 5th Edition.

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