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Introduction to cfd

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Introduction to cfd

1. 1. INTRODUCTION TO CFD ARVIND DESHPANDE
2. 2. Introduction Computational Fluid Dynamics or CFD is the analysis of systems involving fluid flow, heat transfer and associated phenomena such as chemical reactions by means of computer based simulation. A tool for solving PDE’s 3 fundamental principles: Mass is conserved (Continuity equation); Newton’s second law (Navier-Stokes Eqn); Energy is conserved (Bernoulli’s Equation)3/7/2012 Arvind Deshpande (VJTI) 2
3. 3. Introduction Governing equations - PDE’s or integral equations Analytical and experimental approach (Old) “A theory is something nobody believes except the person proposing the theory and an experiment is something everybody believes except the person doing the experiment” --Albert Einstein3/7/2012 Arvind Deshpande (VJTI) 3
4. 4. Numerical Solutions (New) Computers can only do the following:  Add, Subtract, Multiply and Divide  Perform simple logical operations  Display colours on the screen What is Discretization?  Analytical Solution : Continuous  Numerical Solution : Discrete3/7/2012 Arvind Deshpande (VJTI) 4
5. 5. Introduction CFD - Science of determining a numerical solution to the governing equations of fluid flow whilst advancing the solution through space or time to obtain a numerical description of the complete flow field of interest. It is very important to know velocity, pressure and temperature fields in a large no. of applications involving fluids i.e liquids and gases. The performance of devices such as turbo machinery and heat exchangers is determined entirely by the pattern of fluid motion within them.3/7/2012 Arvind Deshpande (VJTI) 5
6. 6. Why CFD? Growth in complexity of unsolved engineering problems Need for quick solutions of moderate accuracy Absence of analytical solutions The prohibitive costs involved in performing even scaled laboratory experiments Efficient solution algorithms Developments in computers in terms of speed and storage Serial/parallel/web computing Sophisticated pre and post processing facilities3/7/2012 Arvind Deshpande (VJTI) 6
7. 7. Procedure1. Virtual model2. The flow region or calculation domain is divided into a large number of finite volumes or cells3. Partial differential equations are discretized using a wide range of techniques: finite difference, finite volume or finite element4. Algebraic equations gathered into matrices which are solved by an iterative procedure5. Numerical solution gives the values of the dependent variables at discrete locations6. Chemical reaction, Multiphase flow, mixing, phase change, mechanical movement3/7/2012 Arvind Deshpande (VJTI) 7
8. 8. 3/7/2012 Arvind Deshpande (VJTI) 8
9. 9. CFD - Third approach in fluid dynamics CFD today is equal partner with pure theory and pure experiment in the analysis and solution of fluid dynamic problems. It nicely and synergistically complements the other two approaches of pure theory and pure experiment, but it will never replace either of these approaches. CFD carry out numerical experiments. Numerical experiments carried out in parallel with physical experiments in the laboratory can sometimes be used to help interpret physical experiment.3/7/2012 Arvind Deshpande (VJTI) 9
10. 10. Advantages of CFD It complements experimental and theoretical fluid dynamics by providing an alternative cost effective means of simulating real flows. Insight Better visualization and enhanced understanding of designs. Foresight Testing many variations until you arrive at an optimal result before physical prototyping and testing. Practically unlimited level of detail of results at virtually no added expense. Efficiency Compression of design and development cycle.3/7/2012 Arvind Deshpande (VJTI) 10
11. 11. Advantages of CFD The simulation results in prediction of the flow fields and engineering parameters, which are very useful in the Design and Optimization of processes and equipments. Substantial reduction of lead times and costs of new designs Ability to study systems where controlled experiments are difficult or impossible to perform (e.g. very large systems) Ability to study systems under hazardous conditions at and beyond their normal performance limits (e.g. safety studies and accident scenarios) CFD is slowly becoming part and parcel of Computer Aided Engineering (CAE)3/7/2012 Arvind Deshpande (VJTI) 11
12. 12. Why do we use CFD ? Complements actual engineering testing Reduces engineering testing costs Provides comprehensive data not easily obtainable from experimental tests. Reduces the product-to-market time and costs Helps understand defects, problems and issues in product/process3/7/2012 Arvind Deshpande (VJTI) 12
13. 13. Benefits of CFD Understand Reduce System Cost Problems Improve Performance Reduce Design Time3/7/2012 Arvind Deshpande (VJTI) & Cost 13
14. 14. HOW IT DIFFERS FROM STRESSANALYSIS? Stress analysis is generally check for safe working of the design, Very rarely the performance of the system depends on the stress levels The governing equations are linear  Ease of solution  Not much dependencies on the grid or mesh Need of auxiliary physics and models for CFD  Turbulence  Reactions  Multiple phases their transformations Confined domains Conservation of only energy, against conservation of mass, forces and energy CFD problems are, in general, more difficult to solve. Hence CFD was lagging behind structural mechanics.3/7/2012 Arvind Deshpande (VJTI) 14
15. 15. Applications of CFD Aerodynamics of aircraft : lift and drag Automotive : External flow over the body of a vehicle or internal flow through the engine, combustion, Engine cooling Turbo machinery: Turbines, pumps , compressors etc. Flow and heat transfer in thermal power plants and nuclear power reactors HVAC Manufacturing – Casting simulation, injection moulding of plastics Marine engineering: loads on off-shore structures Hydrodynamics of ships, submarines, torpedo etc.3/7/2012 Arvind Deshpande (VJTI) 15
16. 16. Applications of CFD Electrical and electronic engineering: cooling of equipment like transformers, Computers, microcircuits, Semiconductor processing, Optical fibre manufacturing Chemical process engineering: mixing and separation, chemical reactors, polymer molding Transport of slurries in process industries Environmental engineering: External and internal environment of buildings, wind loading, Investigating the effects of fire and smoke, distribution of pollutants and effluents in air or water, Hydrology and oceanography: flows in rivers, oceans Meteorology: weather prediction Enhanced oil recovery from rock formations Geophysical flows: atmospheric convection and ground water movement Biomedical engineering: Flow in arteries, blood vessels, heart, nasal cavity, Inhalers3/7/2012 Arvind Deshpande (VJTI) 16
17. 17. Pressure distribution on a pickup van withpathlines3/7/2012 Arvind Deshpande (VJTI) 17
18. 18. Streamlines on a Submarine with thesurface colored with Pressure3/7/2012 Arvind Deshpande (VJTI) 18
19. 19. Aerospace applications3/7/2012 Arvind Deshpande (VJTI) 19
20. 20. Aerospace applications3/7/2012 Arvind Deshpande (VJTI) 20
21. 21. Automotive applications Evaporating diesel fuel inside an autothermal reformer mixing chamber3/7/2012 Arvind Deshpande (VJTI) 21
22. 22. Temperature distribution in IC Engine3/7/2012 Arvind Deshpande (VJTI) 22
23. 23. Surface pressure distribution in anautomotive engine cooling jacket.3/7/2012 Arvind Deshpande (VJTI) 23
24. 24. Cooling of transformers3/7/2012 Arvind Deshpande (VJTI) 24
25. 25. Flow pathlines and temperature distribution in afan-cooled computer cabinet.3/7/2012 Arvind Deshpande (VJTI) 25
26. 26. FLOW IN LUNGS-Inhaling and exhaling of air3/7/2012 Arvind Deshpande (VJTI) 26
27. 27. Applications in Chemical Engg.3/7/2012 Arvind Deshpande (VJTI) 27
28. 28. Biomedical applications3/7/2012 Arvind Deshpande (VJTI) 28
29. 29. Flow through the turbine distributor runner draft tube rotating blades 29
30. 30. Computed flow in the runner 30
31. 31. Computed flow in the draft tube3/7/2012 Arvind Deshpande (VJTI) 31
32. 32. Some more applications3/7/2012 Arvind Deshpande (VJTI) 32
33. 33. Some more applications3/7/2012 Arvind Deshpande (VJTI) 33
34. 34. Some more applications Vortical structures generated by an Fluid flows around the spinnaker and aircraft landing gear main sail of a racing yacht design Temperatures on flame surface Pressure distribution modeled using LES and state-of the- on an F1 car art combustion models3/7/2012 Arvind Deshpande (VJTI) 34
35. 35. CFD USAGE & GROWTH 60 % 40 % Worldwide: 1 Billion USD 18 % 17 % 15 % 15 % India: Rs 50 Cr Projected Growth Rate Estimated annualexpenditure on CFD analysis Extrapolation of Published estimates3/7/2012 Arvind Deshpande (VJTI) 35
36. 36. National Scenario in CFD Educational / Research Institutes – IIT’s, IISc, BARC Industry – NAL, BHEL, SAIL, GTRE, Cummins, Mahindra, Birla group GE, TCS The number of companies adopting CFD is increasing in a major way in India each year CFD is the fastest growing sector of the CAD/CAM/CAE market with a projected 40-50% growth each year in CFD in India3/7/2012 Arvind Deshpande (VJTI) 36
37. 37. National Scenario in CFD The demand for CFD is spurred by:  Indian companies wanting to improve quality and compete globally CFD is predominantly used in Automotive Industry, Power Generation Industry and Chemical & Petrochemical Industry  MNC Engineering centers located in India and bringing their design/analysis work here and serving overseas clients Working on all aspects of design, analysis and performance improvement using CFD  Indian Science and Defence Labs enhancing their CFD research Defense labs like DRDO, NAL - Application of CFD to high-speed propulsion systems etc. Non defence labs - Focusing on materials and chemicals areas Students knowledgeable in CFD are being produced by only a handful of Institutes in India today The mismatch between the demand and availability of students is growing each year at a large rate3/7/2012 Arvind Deshpande (VJTI) 37
38. 38. Methodology in CFD Pre Processor  Pre processor  Geometry generation  Geometry cleanup  Meshing  Solver Solver  Problem specification  Additional models  Numerical computation  Post Processor  Line and Contour data Post Processor  Average Values  Report Generation3/7/2012 Arvind Deshpande (VJTI) 38
39. 39. 1. Pre-processor Definition of the geometry of the region of interest: the computational domain Creating regions of fluid flow, solid regions and surface boundary names Grid generation – the sub-division of the domain into a number of smaller, non-overlapping sub-domains: a grid (or mesh) of cells (or control volumes or elements) Accuracy of a solution, calculation time and cost in terms of necessary computer hardware are dependent on the fineness of the grid. Over 50% of time spent in industry on a CFD project is devoted to the definition of domain geometry and grid generation. Selection of the physical and chemical phenomena that need to be modeled. Definition of fluid properties. Specification of appropriate boundary conditions at cells which coincide with or touch the domain boundary3/7/2012 Arvind Deshpande (VJTI) 39
40. 40. 2. Solver• CFD is the art of replacing the differential equation governing the Fluid Flow, with a set of algebraic equations (the process is called discretization), which in turn can be solved with the aid of a digital computer to get an approximate solution.3/7/2012 Arvind Deshpande (VJTI) 40
41. 41. Finite difference method Domain including the boundary of the physical problem is covered by a grid or mesh At each of the interior grid point the original Differential Equations are replaced by equivalent finite difference approximations Truncated Taylor series expansions are often used to generate finite difference approximations of derivatives of  in terms of point samples of  at each grid point and its immediate neighbours Most popular during the early days of CFD FDM has the most formal foundation because, its inherent straightforwardness and simplicity.3/7/2012 Arvind Deshpande (VJTI) 41
42. 42. Finite Element Method The solution domain is discretized into number of small sub regions (i.e. Finite Elements). Select an approximating function known as interpolation polynomial to represent the variation of the dependent variable over the elements. The piecewise approximating functions for  are substituted into the equation it will not hold exactly and a residual is defined to measure the errors. The integration of the governing differential equation (often PDEs) with suitable weighting Function, over each elements to produce a set of algebraic equations-one equation for each element. The set of algebraic equations are then solved to get the approximate solution of the problem. Structural Design, Vibration Analysis, Fluid Dynamics, Heat Transfer and Magnetohydrodynamics3/7/2012 Arvind Deshpande (VJTI) 42
43. 43. Finite volume method FLUENT, PHOENICS, and STAR-CD Integration of the governing equations of fluid flow over all the (finite) control volumes of the solution domain. This is equivalent to applying a basic conservation law (e.g. for mass or momentum) to each control volume. Discretisation involves the substitution of a variety of finite – difference – type approximations for the terms in the integrated equation representing flow process such as convection, diffusion and sources. This converts the integral equations into a system of algebraic equations. Solution of the algebraic equations by an iterative method.3/7/2012 Arvind Deshpande (VJTI) 43
44. 44. Rate of change of  in the Net flux of  due to control volume with respect to = convection into the + time control volume Net flux of  due to diffusion into the + control volume Net rate of creation of  inside the control volume3/7/2012 Arvind Deshpande (VJTI) 44
45. 45. 3.Post-processorVersatile data visualization tools. Domain geometry and grid display Vector plots showing the direction and magnitude of the flow. Line and shaded contour plots 2D and 3D surface plots Particle tracking View manipulation (translation, rotation, scaling etc.) Visualization of the variation of scalar variables (variables which have only magnitude, not direction, such as temperature, pressure and speed) through the domain. Quantitative numerical calculations. Charts showing graphical plots of variables Hardcopy output Animation for dynamic result display Data export facilities for further manipulation external to the code3/7/2012 Arvind Deshpande (VJTI) 45
46. 46. 3/7/2012 Arvind Deshpande (VJTI) 46
47. 47. Problem solving with CFD Convergence – The property of a numerical method to produce a solution which approaches the exact solution as the grid spacing, is reduced to zero. Consistency - The property of a numerical method to produce system of algebraic equations solution which are equivalent to original governing equations as the grid spacing, is reduced to zero. Stability - associated with damping of errors as the numerical method proceeds. If a technique is not stable, even round off errors in the initial data can cause wild oscillations or divergence.3/7/2012 Arvind Deshpande (VJTI) 47
48. 48. Problem solving with CFD Conservativeness – Local conservation of fluid property for each control volume. It also ensures global conservation of fluid property for the entire domain. Boundedness – In a linear problem, without sources the solution is bounded by the maximum and minimum boundary values of the flow variables. Similar to stability. Transportiveness – Numerical schemes must account for the directionality of influencing in terms of the relative strength of diffusion to convection.3/7/2012 Arvind Deshpande (VJTI) 48
49. 49. Problem solving with CFD Convergence of iterative process – Residuals (measure of overall conservation of the flow properties) are very small. Good initial grid design relies largely on an insight into the expected properties of the flow. Background in the fluid dynamics of the problem and experience of meshing similar problems helps. Grid independence study - A procedure of successive refinement of initially coarse grid until certain key results do not change.3/7/2012 Arvind Deshpande (VJTI) 49
50. 50. Problem solving with CFD CFD is no substitute for experimental work, but a very powerful problem solving tool. Comparison with experimental test work High end – Velocity measurements by hot wire or laser Doppler anemometer Static pressure or temperature measurements with static pitot tube traverse can also be useful. Comparison with previous experience Comparison with analytical solutions of similar but simpler flows. Comparison with closely related problems reported in the literature e.g ASME Main outcome of any CFD exercise is improved understanding of the behaviour of the system. Main ingredients for success in CFD are experience and a thorough understanding of the physics of the fluid flows and fundamentals of the numerical algorithms.3/7/2012 Arvind Deshpande (VJTI) 50
51. 51. CFD – A Big Picture CFD (computational fluid dynamics) is not a CFD software. Commercial software are purely a set of tools which can be used to solve the fluid mechanics problem numerically on a computer. Commercial CFD codes may be extremely powerful, but their operation still requires a high level of skill and understanding from the operator to obtain meaningful results in complex situations. Users of CFD must know fundamentals of fluid dynamics, heat transfer, turbulence, chemical reactions and numerical solution algorithms. They must have adequate knowledge of the physics of the problem. In CFD, the user is responsible for correctly choosing the tools. He must note that that CFD solution for a problem gets generated due the sequential usage of chosen tools from the collection of tools available in the software. The user of CFD must get familiarized with all possible tools before he starts using them. Best solutions are possible if correct tools are chosen in the correct sequence. The quality of the results depends on the background of the user, quality of the tools and the capability of the computer.3/7/2012 Arvind Deshpande (VJTI) 51
52. 52. Identification and formulation of flowproblem User must decide the physical and chemical phenomenon that needed to be considered e.g. 2-D or 3-D Incompressible or compressible Laminar or turbulent Single phase or 2 phase Steady or unsteady To make right choices require good modeling skills Assumptions are required to reduce the complexity to a manageable level while preserving the important features of the problem. Appropriateness of the simplifications introduced partly governs quality of information generated by CFD Engineers need CFD codes that produce physically realistic results with good accuracy in simulations with finite grid.3/7/2012 Arvind Deshpande (VJTI) 52
53. 53. Verification and Validation Verification and validation increase our confidence in the simulation No computer software can be proved to have no errors. We can state that software is wrong if evidence to this effect can be collected Verification is solving the chosen equations right Numerical techniques for verification involves finding out sources of error in spatial & temporal discretisation, iterative convergence, and rounding off errors Checking out if time steps adequate for all situations Validation is Solving the right equation Is the simulation matching with experimental data Experimental data helps validation of similar simulations Scientific literature3/7/2012 Arvind Deshpande (VJTI) 53
54. 54. What basics do you need to do develop asuccessful student of CFD ?  Develop a thorough understanding of the fundamentals of Fluid Mechanics, Heat Transfer and CFD  Get exposure to the physics and solution algorithms  Develop good programming skills3/7/2012 Arvind Deshpande (VJTI) 54
55. 55. WHAT IS IMPORTANT? CFD Numerical Methods Mathematics Fluid Mechanics, Heat Transfer3/7/2012 Arvind Deshpande (VJTI) 55
56. 56. WHAT IS IMPORTANT? Focus of the technology  Fundamentals  Domain knowledge  Numerical modeling and its limitations  Long time investment Software tools will follow  Learning the tool just acquiring the skills  Tools will facilitate the solution process  Keep on changing  Can be learnt is short span3/7/2012 Arvind Deshpande (VJTI) 56
57. 57. Career Opportunities in CFD – AnOverview CFD offers career opportunities in different areas based on the specific interest and skill set of the students  Code development  Development of various modules of CFD software  Can be for general purpose software or for codes for specific application  Application of CFD software  For solving industrial problems in diverse areas  Testing & Validation of CFD codes  Usually for QA of multipurpose commercial software  Documentation for CFD codes  Writing technical documents like user guides for commercial CFD codes In industry, opportunities in CFD application are relatively more than those in development, testing and documentation3/7/2012 Arvind Deshpande (VJTI) 57
58. 58. Conclusions• CFD is a powerful tool to solve complex flows in engineering systems. However:• Extreme care should be taken while:  Generating geometry and grids,  Choosing flow model,  Boundary conditions  Material properties  Convergence criteria (grid independence) Unless proper inputs are given and solution is checked, the solution we get may not be the real solution!!-It will be GIGO 3/7/2012 Arvind Deshpande (VJTI) 58
59. 59. Syllabus1 Introduction: Definition and overview of CFD, need, Advantages of CFD, 2 Applications of CFD, CFD methodology, Convergence, consistency, stability, iterative convergence, grid independence, Verification and validation2 Governing equations of mass, momentum and energy : Derivation, 6 Discussion of physical meanings and Presentation of forms particularly suitable to CFD, Boundary Conditions – Dirichlet, Neumann, Robbins, initial conditions, mathematical behavior of partial differential equations – Elliptic, parabolic & hyperbolic equations, impact on CFD3 Discretisation methods – Introduction to Finite Difference Method, Finite Volume 6 Method, Finite Element Method Finite Difference method – Introduction to finite differences, difference equation, Solution of discretised equations, Tri Diagonal Matrix Algorithm, explicit and implicit approach, Errors and analysis of stability, Von-Neumann stability method, CFL condition4 Grid Generation: Structured and Unstructured Grids, General transformations of 4 the equations, body fitted coordinate systems, Algebraic and Elliptic Methods, O- type, C- type and H-type structured grid generation multi block structured grids, adaptive grids
60. 60. Syllabus5 Finite volume method for diffusion problems (Conduction): Steady state one 6 dimensional and two dimensional heat conduction with or without heat generation, dealing with Dirichlet, Neumann, and Robins type boundary conditions, Multi-solid heat conduction, Non-linear Heat Conduction, Unsteady heat conduction- Explicit, Crank-Nicolson , Implicit schemes6 Finite volume method for advection-diffusion problems (Convection- 6 conduction): Steady One-dimensional and Two Dimensional Convection- Diffusion, Advection schemes-Central, first order upwind, hybrid, power law, Second order upwind, QUICK etc., Properties of advection schemes – Conservativeness, boundedness, transportiveness, False diffusion, unsteady advection - diffusion7 Solution algorithms for pressure velocity coupling in steady flows: 6 Staggered grids, SIMPLE, SIMPLER, SIMPLEC, PISO algorithms, unsteady flows8 Turbulence modeling : Turbulence, its effect on governing equations, turbulence 4 models – k-ε , RSM, ASM, LES etc.9 Post processing – xy plots, contour plots, vector plots, streamline plots etc. 2
61. 61. Reference1) An Introduction to Computational Fluid Dynamics, The Finite Volume Method H K Versteeg and W Malalasekera, Pearson Education, 2008.2) Numerical Heat Transfer and Fluid Flow – S V Patankar, Taylor & Francis, 1980. A standard text on the details of numerical method3) Computational Fluid Dynamics, The basics with applications John.D.Anderson, JR.,Mcgraw-Hill International edition, 19954) Computational Fluid Flow and Heat Transfer K.Muralidhar and T.Sundararajan, Narosa, 20075) Computational methods for fluid dynamics Ferziger and Peric, Springer, 20046) Introduction to Computational Fluid Dynamics A.W. Date, Cambridge, 2005. Web Sites www.cfd-online.com3/7/2012 Arvind Deshpande (VJTI) 61
62. 62. Thank You Hope You Enjoyed the Tour ofColorful / Computational Fluid Dynamics!3/7/2012 Arvind Deshpande (VJTI) 62