What Every Design Engineer Needs to Know about Computational Fluid Dynamics

Written By: Andreas Korff
  • 3/12/2018

[Editor: As you may have heard, Creo 5.0 includes the new Creo Flow Analysis extension, a complete computational fluid dynamics (CFD) solution. For many design engineers, this may be the first time they’ll have access to a tool that can accurately visualize how liquids and gases flow around a 3D CAD model. As such, we asked Kamran Fouladi, an engineering professor who’s dedicated his career to CFD, to provide a quick overview of CFD for everyday designers and engineers—especially those who may not be simulation experts.]

The field of CFD has grown by leaps and bounds and is no longer the realm of the few. In 2008, I created a CFD group on LinkedIn, there were 6 of us. Today that same LinkedIn group boasts more than 44,000 members.

The tools have evolved significantly over the years too. Not long ago, companies evaluated models using “in-house” packages made of disjointed programs with scant documentation and hard-to-find training support. As you can imagine, it was not easy to use—engineers needed expert knowledge of mathematics and computational methodologies to develop, modify, and apply these codes for their own use.

Fortunately, organizations today can now take advantage of commercial and even open source tools that they can embed into their design and R&D processes.

What does that mean for the design engineers in these companies? They may still need expert analysts to simulate their models, but they may also be able to analyze models themselves. In fact, I regularly meet these design engineers and structural analysts in my training classes.

Here’s a good starting place for those new to the discipline of CFD:

So, What Is CFD?

In simplest terms, CFD is the computer-based simulation of flow motion problems. More specifically, it provides approximate solutions to fluid flow problems using computers and numerical algorithms.

Note that simulation doesn’t completely remove the need for physical testing, but it does complement it. In fact, CFD offers significant advantages to a design and analysis process. That’s because engineers can use it to find detailed and comprehensive information about the flow field and to visualize it without any intrusion.  In a design process, CFD is a cost-saving alternative to trial and error practices and allows designers to explore "what if" scenarios.

Image: CFD simulation of a model in Creo.

The Three Stages of a CFD Simulation

There are three major stages in a CFD simulation process:

  • Pre-processing requires a flow domain, which includes the geometry, to be established. The model must be simplified by excluding any geometrical features that have no significant effects on the flow field. However, it should be noted that major geometrical alterations would adversely impact the accuracy of the simulation.

    The discretization of the flow domain (mesh generation) also takes place in this Pre-processing stage. Traditionally, this is the most labor-intensive and time-consuming part of simulation as analysts must strive for the optimal mesh.

    In a CFD simulation, the accuracy of the solution strongly depends on the number of grid points. Basically, more grid points are needed in regions of high gradient. On the other hand, the larger the grid size, the greater the computational cost (time and memory).

  • Execution includes solver setup and number crunching. In solver setup, the analyst defines the solver setting by selecting the appropriate physical and numerical model, including material properties, domain properties, boundary conditions, initial conditions, numerical schemes, and convergence criteria.
  • Post-processing of the results allows for visualization of the flow field, extraction of the desired flow properties, as well as verification and validation of the simulation model. Documentation is also an important part of the post-processing stage.

Best Practices

CFD employs an iterative process to achieve a converged solution, but a converged solution does not always equate to an accurate solution. This is because errors and uncertainties are unavoidable in a simulation. For example, errors can arise due to stopping the run before the converged solution has been achieved (Convergence Error) or when a less than adequate mesh is used (Discretization Error).

Similarly, uncertainties can occur due to lack of definite choices during the solver setup. For instance, there is no universally accepted turbulence model that works for all flows and all regimes. Moreover, the accuracy and effectiveness of models vary depending on the specific application.

Fortunately, there are some best practices, in form of guidelines and strategies that could help designers and analysts produce more accurate simulations.

For example, a designer can use one of these best practices for building an appropriate CFD simulation model. The idea of this best practice is that simulation in the early design stages pays big dividends when not all details have been worked out.

One cannot leave out important geometrical details, but an early simulation does not require exhaustive details. Furthermore, this early work will yield reasonable approaches in dealing with thermal-fluid issues.

For complex problems, it is important that designers build a concept model early, solve the simple things first, and then go on to more complex ones. More specifically, they should find actionable bite-sized places to apply CFD and build on the success of these simulations.


CFD is an essential simulation tool but fraught with many pitfalls for new users. That said, some best practices can help these newcomers manage errors and uncertainties and produce credible analyses.

With advances in simulation over the past 10-20 years and the uptake in its use, CFD is primed to become a bona fide design tool. I’ll discuss the role of CFD in design and its application in various stages of design in upcoming posts.


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About the Author

Andreas Korff

Kamran Fouladi, Ph.D., P.E. is an educator, researcher, and specialist in Computational Fluid Dynamics (CFD) with more than 28 years of engineering and teaching experience. Since 2000, Kamran has provided CFD consulting to projects of national importance - DOD’s Helicopter Advanced Protection System, NASA Orion Launch Abort Vehicle, NASA Crew Exploration Vehicle, NASA Orbital Space Plane, and NASA supersonic transport. He has also been involved in developing and conducting CFD training courses for professional engineers for more than 10 years. Kamran is currently an Assistant Professor at Widener University.