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  1. 1. 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 theuse of comprehensive CFD based computational tools to model industrial processes within the Chemical, Metallurgical, andPower 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 laboratorydata 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 thatproperly describe the first-order physics and chemistry that drive the system. In recent years, the computational fluid dynamics (CFD) based on conservation equations hasbecome a viable technique for process simulation So in this work we are going to compute thetemperature profile generated Using CFD and GAMBIT for a given temperature source, it canhelp to Compute the energy required and to optimize it. 1
  2. 2. 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 1200oC 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. 2
  3. 3. 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 avertical, cylindrical furnace, the tubes are vertical. Tubes can be vertical or horizontal, placedalong 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 hightemperature refractory and is where the flame is contained in. Air registers located below theburner and at the outlet of the air blower are devices with movable flaps or vanes that controlthe shape and pattern of the flame, whether it spreads out or even swirls around. 3
  4. 4. 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 ofCFD.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 usedit and conveniently replay model building sessions for parametric studies. 4
  5. 5. 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. (1) 2.1.2. Heat Transfer by Radiation Thermal radiation refers to the radiant energy emitted by the bodies by virtue oftheir 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: (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: (3) 5
  6. 6. 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. (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. (5) 2.1.3.3. Prandtl Number It is the ratio of momentum diffusivity (viscosity) and thermal diffusivity. (6) 2.1.3.4. Grashof Number It is the ratio of Buoyancy force to the viscous force acting on a fluid. (7) 2.1.3.5. Rayleigh Number 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 inthe form of conduction; when it exceeds the critical value, heattransfer is primarily in the form of convection. 6
  7. 7. (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: (9) Conservation of Momentum equation for an incompressible fluid is: (10) Energy Equation: (11) 7
  8. 8. 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 NavierStokes 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 theeffort 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 aretypically placed at cell centers rather than at nodal points. This is not always true, as there arealso face-centered finite volume methods. In any case, the values of field variables at nonstoragelocations (e.g. vertices) are obtained using interpolation. (12) 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. 8
  9. 9. 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: (13) A finite element method (FEM) discretization isbased upon a piecewise representation of the solution in terms of specified basis functions. Thecomputational domain is divided up into smaller domains (finite elements) and the solution ineach element is constructed from the basis functions. The actual equations that are solved aretypically obtained by restating the conservation equation in weak form: the field variables arewritten 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 canbe a double-edged sword, as the choice of basis functions is very important and boundaryconditions may be more difficult to formulate. Again, a system of equations is obtained (usuallyfor 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 (upto a point) solution. FVM and FDM are generally considered easier to program than FEM, butopinions vary on this point. FVM are generally expected to provide better conservation properties, but opinions vary on this point also. 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. 9
  10. 10. 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. (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 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 2ndorder 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 tobe very close to the upstream value. 10
  11. 11. 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 transformationof 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 11
  12. 12. 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 manyapplications. Complex models can be built directly within GAMBIT‟s solid geometry modeler, orimported 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, 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 analysisat any time during the process. This saves time and enables to refine designs more 12
  13. 13. 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 isunique 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 broadestrange 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 widerange of CFD applications. The speed to solution is faster because suite of software enables usto stay within one interface from geometry building through the solution process, to postprocessingand 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 implementedin FLUENT (FLUENT user‟s guide, vols. 1–5, Lebanon, 2001), because of its adaptability to a widevariety 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 nonlinearand coupled, several iterations of the solution loop must be performed before a convergedsolution 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 continuityequation 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 13
  14. 14. 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 phaseequations 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 foreach 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 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. 14
  15. 15. 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, 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. Itprovides 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 ofengineering 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.) 15
  16. 16. 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 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 hardwarechanges. 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. 16
  17. 17. 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 QUANTITY Exterior Width Exterior Height Exterior Length Isolatıon Thermoblock Isolation Types of Heat Furnace Firebox Thermal Capacity Circulation Fan Exhaust Fan 1,300 mm. 3,300 mm. 20,850 mm. Fibreglass 150 mm Fibreglass Natural Gasor LPGBurner AISI 309-AISI 310 350,000 kcal / h 20,000 m3/h 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 2825mm x700mm. 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. 17
  18. 18. 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 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 18
  19. 19. Step 2: Geometry→Face→Move/Copy Faces Figure3.4: Move/Copy Command Figure 3.5: Copied Faces Step 3: Figure 3.6: Furnace Body Faces 19
  20. 20. Step 4 Geometry→Edge→Create Circular Arc Figure 3.7: Circular Arc Command Step 5: Geometry→Volume→Form Volume Figure 3.8: Volume Form Command 20
  21. 21. Figure 3.9: Faces with Arc Step 6: Geometry→Volume→Substract Volume Figure3.10: Substract Volumes Command Figure 3.11: Susbtracted Faces 21
  22. 22. 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; Air Curtains → Hot Air Inlet → Air Curtains → Hot Air Outlet → Surrounding → Pressure Outlet 3 Walls and Interiors 22
  23. 23. Figure 3.13: Specifying Boundry Types Figure 3.14: Boundry Conditions 23
  24. 24. Figure 3.15: Continuum Boundry 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. 24
  25. 25. 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. Figure 3.16: Mesh Volumes 25
  26. 26. Figure 3.17: Mesh Figure 3.18: Size Function 1 26
  27. 27. Figure 3.19: Size Function Function 2-3 Figure 3.20: Size Function 4-5Figure 3.21: Size Function 6 27
  28. 28. 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 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 28
  29. 29. Figure 3.22: Grid Check Figure 3.23: Grid Size 29
  30. 30. Figure 3.24: Solver Figure3.25: Memory Usage 30
  31. 31. Figure 3.26.a: Materials Figure 3.26.b:Materials 31
  32. 32. Figure 3.26.c: Materials Figure 3.27: Thermal Expansion Coefficient Boundry Conditions: Figure 3.28: Airas Fluid Material 32
  33. 33. Pressure Outlet Figure 3.29.a: Pressure Outlet Figure 3.29.b: Pressure Outlet 33
  34. 34. Figure 3.29.c:Pressure Outlet Figure 3.29.d: Pressure Outlet 34
  35. 35. Figure 3.30.a: Velocity Inlet Figure 3.30.b: Velocity Inlet 35
  36. 36. Figure 3.31.a: Wall Properties Figure 3.31.b: Wall Properties 36
  37. 37. 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 Inlet3 37
  38. 38. Figure 3.33.a: Velocity Inlet 4 Figure 3.33.b: Velocity Inlet 4 38
  39. 39. Pressure Outlets Figure3.34.a:Pressure Outlet 1 Figure3.34.b: Pressure Outlet 1 39
  40. 40. Figure3.35.a: Pressure Outlet 3 Figure3.35.b: Pressure Outlet 3 40
  41. 41. 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 41
  42. 42. Figure 4.3:Velocity Distribution Figure 4.4: Pressure Distribution 42
  43. 43. Figure 4.5: Wall Temperature Distribution (Outer) Figure 4.6: Wall Temperature Distribution (Inner) 43
  44. 44. 4.2. Case 2 Figure 4.7: Residuals Figure 4.8: Temperature Distribution , 44
  45. 45. Figure 4.9: Velocity Distribution Figure 4.10: Dynamic Pressure Distribution 45
  46. 46. Figure 4.11: Wall Temperature Distribution (Outer) Figure 4.12: Wall Temperature Distribution (Inner) 46
  47. 47. 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 47
  48. 48. 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. 48
  49. 49. 6. REFERENCES [1] Peeyush Agarwal. 2009 “Simulation of Heat Transfer Phenomenon in Furnace Using FLUENT-GAMBIT” Page 23-25, National Institute of TechnologyRourkela [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. 49

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