Computational Fluid Dynamics
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23-02-2010, 11:55 PM


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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
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18-02-2011, 09:25 AM

Computational Fluid Dynamics Solution
Powerful Computational Fluid Dynamics Software for Optimization of Product Development and Processes
Understanding the motion of liquids and gases is crucial in many branches of engineering. Until recently, studies of fluids in motion were confined to the laboratory.
But with the rapid growth in computer processing power, software applications now bring numerical analysis and solutions of flow problems to the desktop. In addition, the use of common interfaces and workflow processes makes fluid dynamics accessible to designers as well as analysts. Computational fluid dynamics (CFD) has become an integral part of the engineering design and analysis environment at many companies that need to predict the performance of new designs or processes before they are manufactured or implemented. For more than 20 years, companies around the world have trusted technology to contribute to their successes. Fluid dynamics is used in industries including aerospace, automotive, chemical processing, power generation, heating, ventilation, air conditioning, biomedical, oil and gas, marine, and many others. From ventilation comfort in large buildings to the tiniest scale in micro-pumps and nanotechnology, a wide range of applications can be addressed due to the scalable nature of fluid dynamics. ANSYS has considerable
expertise in using simulation-driven design to improve performance for pumps, fans, turbines, compressors and other rotating machinery, and the company has incorporated this into all elements of ANSYS CFX software, making it a leader in this demanding field. Specialized models for combustion, reacting flows and radiation, among others, help provide the insight into equipment and processes required to increase production, improve product longevity and decrease waste.
One Environment
The ANSYS® Workbench™ environment provides a single setting for simulation from start to finish, enabling users to perform more product development tasks faster.
ANSYS Workbench delivers the basis for a full computer-aided engineering (CAE) solution from ANSYS, providing access to a wide variety of simulation technologies. All settings are persistent and are connected back to the parametric computeraided design (CAD) model, from analysis-specific modifications made to the geometry through the application of physics, solver control parameters, graphic objects created during post-processing and quantitative expressions evaluating performance.
Industry Solutions
11.0 RELEASE

ANSYS CFX in ANSYS Workbench Delivers Fast Design Iteration
and Parametric Studies The parameter manager of the ANSYS Workbench environment enables setup of a series of simulations to study the operating range of a product or to investigate and compare
several alternative designs. The parameterized geometry and physics description, combined with the automatic calculation of performance metrics, allows the operating range of the product or process to be established quickly. The geometry, meshing, physics specification, solution and report generation sequence is run automatically over
the range and sampling frequency of the parameters defined. Designers and analysts now are able to minimize physical prototyping and deliver better, more innovative products faster than ever. Design of Experiments as well as deterministic and robust design optimization techniques now can be applied to CFD using DesignXplorer™ technology.
Geometry
ANSYS® DesignModeler™ software is a geometry tool specifically designed for the creation and modification of geometry for analysis. Using an advanced system of interfaces, ANSYS DesignModeler software gives a direct, bi-directional link to geometry models created in a wide variety of existing CAD packages. It is an easy-to-use, fully parametric CAD tool. As the geometry portal for all ANSYS products, ANSYS DesignModeler software provides a single geometry source for a complete range of
engineering simulation tools. ANSYS DesignModeler helps to create the detailed geometry required for engineering simulation, minimizes geometry rework and simplifies interdisciplinary analyses.
Meshing
Providing accurate CFD results requires superior meshing technology. The meshing application within the ANSYS Workbench environment provides access to swept, hexdominant, tetrahedral and prism meshing technologies in a single location that can beapplied on a part-by-part basis. ANSYS® ICEM CFD™ meshing tools also are available
and include mesh editing capabilities as well as structured hexahedral meshing.
CFD Pre-Processing
The ANSYS CFX physics pre-processor is a modern, consistent and intuitive interface forthe definition of the complex physics sometimes required for CFD analysis. In addition,this tool reads one or more meshes from a variety of sources and provides the user withoptions for assigning domains.
CFD Solver
The heart of ANSYS CFX within the ANSYS Workbench interface is the coupledalgebraic multigrid solver. In brief, it achieves reliable and fast convergence by solvingthe equations well. The solver is fully scalable — achieving linear increase in CPU timewith problem size — is easy to set up in both serial and parallel run modes, and isrepresentative of true physics. The solver manager provides feedback on convergenceprogress and allows dynamic display of many criteria. When necessary, parameters can
be adjusted without stopping the solver so convergence can be accelerated. The ANSYSCFX solver runs in high accuracy mode by default, achieving accurate flow predictions robustly and reliably.


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10-02-2012, 02:04 PM

Computational Fluid Dynamics


.ppt   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



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10-02-2012, 02:24 PM

Computational Fluid Dynamics


.ppt   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


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10-08-2012, 12:37 PM

Computational Fluid Dynamics


.pdf   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.



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