Computational Fluid Dynamics (CFD) is the simulation of fluids engineering systems using modeling (mathematical physical problem formulation) and numerical methods (discretization methods, solvers, numerical parameters, and grid generations, etc.). The process of the computation is shown as figure below:

Firstly, we have a fluid problem. To solve this problem, we should know the physical properties of fluid by using Fluid Mechanics. Then we can use mathematical equations to describe these physical properties. This is Navier-Stokes Equation and it is the governing equation of CFD. As the Navier-Stokes Equation is analytical, human can understand it and solve them on a piece of paper. But if we

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Then, we can write programs to solve them. The typical languages are Fortran and C. Normally the programs are run on workstations or supercomputers. At the end, we can get our simulation results. We can compare and analyze the simulation results with experiments and the real problem. If the results are not sufficient to solve the problem, we have to repeat the process until find satisfied solution. This is the process of CFD.

Importance of Computational Fluid Dynamics :

There are three methods in study of Fluid which are theory analysis, experiment and simulation (CFD). As a new method, CFD has many advantages compared to experiments. The advantages of CFD over the experiments’ is tabulated below : Simulation (CFD) Experiment

Cost Cheap

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Heat transfer for electronics packaging applications.

And many more

Examples of CFD application : Smoke plume from an oil fire in Baghdad CFD simulation by FLUENT

Limitations of CFD :

Physical models.

CFD solutions rely on physical models of real world processes such as turbulence, compressibility, chemistry, multiphase flow, and etc.

The CFD solutions can only be as accurate as the physical models on which they are based.

Numerical errors.

Solving equations on a computer invariably introduces numerical errors.

Round-off error: due to finite word size available on the computer.

Truncation error which is due to approximations in the numerical models. Truncation errors will go to zero as the grid is refined. Mesh refinement is one way to deal with truncation error.

Boundary conditions.

For physical models, the accuracy of the CFD solution is only as good as the initial or boundary conditions provided to the numerical model.

Example: flow in a duct with sudden expansion. If flow is supplied to domain by a pipe, you should use a fully developed profile for velocity rather than assume uniform