COMPUTIONAL FLUID DYNAMICS
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Fluid dynamics is the science of fluid motion.
Fluid flow is commonly studied in one of three ways:
Experimental fluid dynamics.
Theoretical fluid dynamics.
Numerically: computational fluid dynamics (CFD).
During this course we will focus on obtaining the knowledge required to be able to solve practical fluid flow problems using CFD.
Topics covered today include:
What is Computational Fluid Dynamics ?
How it works by an example.
CFD is a method to numerically calculate heat transfer and fluid flow. Currently, its main application is as an engineering method, to provide data that is complementary to theoretical and experimental data. This is mainly the domain of commercially available codes and in-house codes at large companies. CFD can also be used for purely scientific studies, e.g. into the fundamentals of turbulence. This is more common in academic institutions and government research laboratories. Codes are usually developed to specifically study a certain problem.
Getting Started with 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.
CFD analysis complements testing and experimentation.
Reduces the total effort required in the laboratory.
How it works?
CFD applies numerical methods (called discretization) to develop approximations of the governing equations of fluid mechanics in the fluid region of interest.
Governing differential equations: algebraic.
The collection of cells is called the grid.
The set of algebraic equations are solved numerically (on a computer) for the flow field variables at each node or cell.
System of equations are solved simultaneously to provide solution.
The solution is post-processed to extract quantities of interest (e.g. lift, drag, torque, heat transfer, separation, pressure loss, etc.).
Compute the solution
The discretized conservation equations are solved iteratively. A number of iterations are usually required to reach a converged solution.
Convergence is reached when:
Changes in solution variables from one iteration to the next are negligible.
Residuals provide a mechanism to help monitor this trend.
Overall property conservation is achieved.
The accuracy of a converged solution is dependent upon:
Appropriateness and accuracy of the physical models.
Grid resolution and independence.