Both OpenFOAM and SU2 are popular open-source CFD (Computational Fluid Dynamics) codes with active communities and extensive capabilities. While both have their strengths, it ultimately depends on your specific requirements and preferences.

OpenFOAM:

- Community support: OpenFOAM has a large and active user community, making it easier to find resources, tutorials, and assistance.

- Ease of learning: OpenFOAM has a steeper learning curve compared to some other CFD codes due to its complex structure and customization options. However, there are resources available to help newcomers get started.

- Capabilities: OpenFOAM is a powerful and versatile CFD code that supports a wide range of models and solvers for various fluid flow problems.

- Computational speed: OpenFOAM's computational speed depends on the specific solver and the problem being simulated. It can be computationally efficient for certain types of simulations, but it's always recommended to assess performance on a case-by-case basis.

- GPU support: OpenFOAM has limited native support for GPU acceleration, and it may require additional customization or the use of specialized solvers to take advantage of GPU capabilities.

- Integration with Matlab/Python: OpenFOAM can be coupled with external tools like Matlab or Python through various interfaces and libraries. This allows you to create scripts for automation, optimization, and design processes.

SU2:

- Community support: SU2 also has an active user community, and while it may not be as large as OpenFOAM's, it still offers resources and support for users.

- Ease of learning: SU2 is designed to be more user-friendly and has a simpler structure compared to OpenFOAM, making it easier to learn and use.

- Capabilities: SU2 supports a range of flow physics and offers a variety of algorithms for solving CFD problems. It has capabilities for both steady and unsteady simulations.

- Computational speed: SU2 is known for its computational efficiency, and it has been designed to leverage high-performance computing resources effectively.

- GPU support: SU2 has built-in support for GPU acceleration, allowing you to take advantage of GPU capabilities for faster simulations.

- Integration with Matlab/Python: SU2 provides interfaces to connect with external tools like Matlab and Python, enabling you to develop scripts for automation and optimization tasks.

Other CFD codes you may consider:

- OpenCFOAM: A fork of OpenFOAM that aims to provide an enhanced user experience and improved usability while retaining the core features of OpenFOAM.

- FEniCS: A powerful finite element framework that can handle a variety of physics problems, including fluid dynamics.

- Gmsh: While not a full-fledged CFD code, Gmsh is a powerful mesh generation tool that can be coupled with other solvers to perform CFD simulations.

When it comes to cloud computing, both OpenFOAM and SU2 can be deployed on cloud platforms. However, the specifics of GPU support and cloud computing options may vary depending on the platform and your specific setup. It's recommended to check the documentation and resources provided by the respective codes and cloud providers for more information on compatibility and performance.

In summary, both OpenFOAM and SU2 have their merits, and the choice depends on your specific requirements, familiarity, and preferences. Experimenting with both and assessing their suitability for your particular needs may help you make an informed decision.

Thanks for your amazing response, Gopal Sharma !

Both OpenFOAM and SU2 are popular open-source CFD codes with active communities and extensive capabilities. However, the choice between them depends on your specific requirements and preferences. Here's some information to help you make a decision:

In addition to OpenFOAM and SU2, there are other open-source CFD codes you can consider, such as:

Remember to thoroughly evaluate the features, documentation, and support for each code to determine which one best suits your specific needs and goals.

Personally, I started with OpenFOAM. Even though, it looks quite intimidating at the beginning, it will get easier, as there are a lot of guides and tutorials available online. Also, the code in itself is quite useful to understand a lot of theories in numerical methods.

Additionally, if you are interested in investigating other aspects such as fluid-structure interactions, aeroacoustics, combustion modelling, and other areas, you can find multiple coupling to external source codes to deal with each aspect.

Following are the advantages in my opinion:

- you can get an excellent understanding of the numerical schemes and methods when you work with OpenFOAM. You have access to the source code, which makes it easy for you to navigate and look through how the calculations and schemes work.

- to understand the basic concepts in CFD such as turbulence modelling, projection methods, etc.

- once you start working on your custom solvers, transportModels, thermophysical models, and custom libraries, you will also have a grasp on many aspects of code handling, mpi-handling, etc.

The list continues. :)

Achuth Nair Balachandran Nair It is certainly something I did not think of: the possibility of better understanding of numerical schemes and access to source code for modification. Since I develop some Machine Learning algorithms and stuff, this is quite useful to me, since I could make intrusive coding. Many thanks for commenting.

Francisco-Javier Granados-Ortiz glad that it was helpful. If you are interested in machine learning module or library in OpenFOAM, it might be worth checking ITHACA-FV. It is developed by Gianluigi Rozza's team, and they have done a lot of work regarding this topic.