Computational fluid dynamics for engineers lecture 4 commercial codes
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Computational Fluid Dynamics for Engineers Lecture 4: Commercial Codes PowerPoint PPT Presentation


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Computational Fluid Dynamics for Engineers Lecture 4: Commercial Codes. Why commercial CFD codes. 100+ man-years of CFD development Integrated grid generation, solver and post-processing Standardized tools (tested and validated) for companies Ability to handle complex geometries

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Computational Fluid Dynamics for Engineers Lecture 4: Commercial Codes

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Computational fluid dynamics for engineers lecture 4 commercial codes

Computational Fluid Dynamics for EngineersLecture 4: Commercial Codes


Why commercial cfd codes

Why commercial CFD codes

  • 100+ man-years of CFD development

  • Integrated grid generation, solver and post-processing

  • Standardized tools (tested and validated) for companies

  • Ability to handle complex geometries

  • Easy-to-use user interfaces

  • Technical support

  • Ability to import information from other CAD tools and interface with other analysis tools

  • Faster virtual prototyping for quicker design evaluation

  • Design to cost, performance and quality

  • Flexible user subroutines that allow creative problem solving


Physics modeled

Physics modeled

  • Incompressible or compressible flows

  • Internal or external flows

  • Laminar or turbulent flows

  • Moving boundary flows

  • Newtonian, non-Newtonian with variable properties

  • Combustion, chemical kinetics, plasma chemistry

  • Multiphase flows


Limitations

Limitations

  • Learning curve related to user interfaces

  • Makes users regard it as a black box

  • Solutions look realistic enough to be believable

  • Move toward reducing the number of solution variables or algorithms removed from the user

  • Defaults for algorithm, convergence etc.

  • Codes – too robust or too stable ? (always guaranteed a solution – even with incorrect boundary conditions or problem set-up)


Suggestions

Suggestions

  • Always know what to expect from the solution

  • Compare with analytical solutions for ‘simplified’ scenarios

  • Need for good experimental data and validation

  • Always question the results

  • Use the most accurate (even if time consuming) algorithms

  • Work with tighter tolerances, finer mesh

  • Verify if solutions are mesh-converged

  • Review cell reynolds number, mesh skewness (compute CFL limit)

  • Do not believe the absolute numbers !

  • Let simulations suggest trends and simplifications for analytical solutions


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