Computational Fluid Dynamics
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 sri.2988 Active In SP Posts: 15 Joined: Feb 2010 23-02-2010, 11:55 PM send me this topic pdf and ppt
 seminar surveyer Active In SP Posts: 3,541 Joined: Sep 2010 23-09-2010, 03:59 PM 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. Computers are used to perform the millions of calculations required to simulate the interaction of liquids and gases with surfaces defined by boundary conditions. Even with high-speed supercomputers only approximate solutions can be achieved in many cases. Ongoing research, however, may yield software that improves the accuracy and speed of complex simulation scenarios such as transonic or turbulent flows. Initial validation of such software is often performed using a wind tunnel with the final validation coming in flight tests. The most fundamental consideration in CFD is how one treats a continuous fluid in a discretized fashion on a computer. One method is to discretize the spatial domain into small cells to form a volume mesh or grid, and then apply a suitable algorithm to solve the equations of motion (Euler equations for inviscid, and Navier–Stokes equations for viscous flow). In addition, such a mesh can be either irregular (for instance consisting of triangles in 2D, or pyramidal solids in 3D) or regular; the distinguishing characteristic of the former is that each cell must be stored separately in memory. Where shocks or discontinuities are present, high resolution schemes such as Total Variation Diminishing (TVD), Flux Corrected Transport (FCT), Essentially NonOscillatory (ENO), or MUSCL schemes are needed to avoid spurious oscillations (Gibbs phenomenon) in the solution. If one chooses not to proceed with a mesh-based method, a number of alternatives exist, notably : Smoothed particle hydrodynamics (SPH), a Lagrangian method of solving fluid problems, Spectral methods, a technique where the equations are project and implimentationed onto basis functions like the spherical harmonics and Chebyshev polynomials, Lattice Boltzmann methods (LBM), which simulate an equivalent mesoscopic system on a Cartesian grid, instead of solving the macroscopic system (or the real microscopic physics). It is possible to directly solve the Navier–Stokes equations for laminar flows and for turbulent flows when all of the relevant length scales can be resolved by the grid (a Direct numerical simulation). In general however, the range of length scales appropriate to the problem is larger than even today's massively parallel computers can model. In these cases, turbulent flow simulations require the introduction of a turbulence model. Large eddy simulations (LES) and the Reynolds-averaged Navier–Stokes equations (RANS) formulation, with the k-ε model or the Reynolds stress model, are two techniques for dealing with these scales. In many instances, other equations are solved simultaneously with the Navier–Stokes equations. These other equations can include those describing species concentration (mass transfer), chemical reactions, heat transfer, etc. More advanced codes allow the simulation of more complex cases involving multi-phase flows (e.g. liquid/gas, solid/gas, liquid/solid), non-Newtonian fluids (such as blood), or chemically reacting flows (such as combustion). Reference: en.wikipediawiki/Computational_fluid_dynamics
 seminar paper Active In SP Posts: 6,455 Joined: Feb 2012 10-02-2012, 02:04 PM Computational Fluid Dynamics   cfd thombare always.ppt (Size: 1.4 MB / Downloads: 31) What is CFD? Computational Fluid Dynamics (CFD) is the science of predicting fluid flow, heat transfer, mass transfer, chemical reactions, and related phenomena by solving the mathematical equations which govern these processes using a numerical process. The result of CFD analyses is relevant engineering data used in: conceptual studies of new designs detailed product development troubleshooting redesign CFD analysis complements testing and experimentation. Reduces the total effort required in the laboratory. CFD - How It Works Analysis begins with a mathematical model of a physical problem. Conservation of matter, momentum, and energy must be satisfied throughout the region of interest. Fluid properties are modeled empirically. Simplifying assumptions are made in order to make the problem tractable (e.g., steady-state, incompressible, inviscid, two-dimensional). Provide appropriate initial and/or boundary conditions for the problem. An Example: Water flow over a tube bank Goal compute average pressure drop, heat transfer per tube row Assumptions flow is two-dimensional, laminar, incompressible flow approaching tube bank is steady with a known velocity body forces due to gravity are negligible flow is translationally periodic (i.e. geometry repeats itself) Advantages of CFD Low Cost Using physical experiments and tests to get essential engineering data for design can be expensive. Computational simulations are relatively inexpensive, and costs are likely to decrease as computers become more powerful. Speed CFD simulations can be executed in a short period of time. Quick turnaround means engineering data can be introduced early in the design process Ability to Simulate Real Conditions CFD provides the ability to theoretically simulate any physical condition
 seminar paper Active In SP Posts: 6,455 Joined: Feb 2012 10-02-2012, 02:24 PM Computational Fluid Dynamics   Computational fluid Dynamics by tThombare A S.ppt (Size: 1.4 MB / Downloads: 28) What is CFD? Computational Fluid Dynamics (CFD) is the science of predicting fluid flow, heat transfer, mass transfer, chemical reactions, and related phenomena by solving the mathematical equations which govern these processes using a numerical process. The result of CFD analyses is relevant engineering data used in: conceptual studies of new designs detailed product development troubleshooting redesign CFD analysis complements testing and experimentation. Reduces the total effort required in the laboratory. CFD - How It Works Analysis begins with a mathematical model of a physical problem. Conservation of matter, momentum, and energy must be satisfied throughout the region of interest. Fluid properties are modeled empirically. Simplifying assumptions are made in order to make the problem tractable (e.g., steady-state, incompressible, inviscid, two-dimensional). Provide appropriate initial and/or boundary conditions for the problem. Mesh Generation Geometry created or imported into preprocessor for meshing. Mesh is generated for the fluid region (and/or solid region for conduction). A fine structured mesh is placed around cylinders to help resolve boundary layer flow. Unstructured mesh is used for remaining fluid areas. Identify interfaces to which boundary conditions will be applied. cylindrical walls inlet and outlets symmetry and periodic faces
 seminar ideas Super Moderator Posts: 10,003 Joined: Apr 2012 10-08-2012, 12:37 PM Computational Fluid Dynamics   CFD Introduction.pdf (Size: 315.58 KB / Downloads: 28) Introduction: This chapter is intended as an introductory guide for Computational Fluid Dynamics CFD. Due to its introductory nature, only the basic principals of CFD are introduced here. For more detailed description, readers are referred to other textbooks, which are devoted to this topic.1,2,3,4,5 CFD provides numerical approximation to the equations that govern fluid motion. Application of the CFD to analyze a fluid problem requires the following steps. First, the mathematical equations describing the fluid flow are written. These are usually a set of partial differential equations. These equations are then discretized to produce a numerical analogue of the equations. The domain is then divided into small grids or elements. Finally, the initial conditions and the boundary conditions of the specific problem are used to solve these equations. The solution method can be direct or iterative. In addition, certain control parameters are used to control the convergence, stability, and accuracy of the method. All CFD codes contain three main elements: (1) A pre-processor, which is used to input the problem geometry, generate the grid, define the flow parameter and the boundary conditions to the code. (2) A flow solver, which is used to solve the governing equations of the flow subject to the conditions provided. There are four different methods used as a flow solver: (i) finite difference method; (ii) finite element method, (iii) finite volume method, and (iv) spectral method. (3) A post-processor, which is used to massage the data and show the results in graphical and easy to read format. In this chapter we are mainly concerned with the flow solver part of CFD. This chapter is divided into five sections. In section two of this chapter we review the general governing equations of the flow. In section three we discuss three standard numerical solutions to the partial differential equations describing the flow. In section four we introduce the methods for solving the discrete equations, however, this section is mainly on the finite difference method. And in section five we discuss various grid generation methods and mesh structures. Special problems arising due to the numerical approximation of the flow equations are also discussed and methods to resolve them are introduced in the following sections. Boundary Conditions The governing equation of fluid motion may result in a solution when the boundary conditions and the initial conditions are specified. The form of the boundary conditions that is required by any partial differential equation depends on the equation itself and the way that it has been discretized. Common boundary conditions are classified either in terms of the numerical values that have to be set or in terms of the physical type of the boundary condition. For steady state problems there are three types of spatial boundary conditions that can be specified: Techniques for Numerical Discretization In order to solve the governing equations of the fluid motion, first their numerical analogue must be generated. This is done by a process referred to as discretization. In the discretization process, each term within the partial differential equation describing the flow is written in such a manner that the computer can be programmed to calculate. There are various techniques for numerical discretization. Here we will introduce three of the most commonly used techniques, namely: (1) the finite difference method, (2) the finite element method and (3) the finite volume method. Spectral methods are also used in CFD, which will be briefly discussed. Spectral Methods Another method of generating a numerical analog of a differential equation is by using Fourier series or series of Chebyshev polynomials to approximate the unknown functions. Such methods are called the Spectral method. Fourier series or series of Chebyshev polynomials are valid throughout the entire computational domain. This is the main difference between the spectral method and the FDM and FEM, in which the approximations are local. Once the unknowns are replaced with the truncated series, certain constraints are used to generate algebraic equations for the coefficients of the Fourier or Chebyshev series. Either weighted residual technique or a technique based on forcing the approximate function to coincide with the exact solution at several grid points is used as the constraint. For a detailed discussion of this technique refer to Gottlieb and Orzag.13 Comparison of the Discretization Techniques The main differences between the above three techniques include the followings. The finite difference method and the finite volume method both produce the numerical equations at a given point based on the values at neighboring points, whereas the finite element method produces equations for each element independently of all the other elements. It is only when the finite element equations are collected together and assembled into the global matrices that the interaction between elements is taken into account. Both FDM and FVM can apply the fixed-value boundary conditions by inserting the values into the solution, but must modify the equations to take account of any derivative boundary conditions. However, the finite element method takes care of derivative boundary conditions when the element equations are formed and then the fixed values of variables must be applied to the global matrices.