OpenFOAM Turbulence Model

It provides a rich variety of turbulence models: RANS (k-ε, k-ω SST), LES (Smagorinsky, WALE, dynamicK), DES/DDES, etc. They can be easily switched via turbulenceProperties.

Expressing this with equations, it looks like this.

k-ω SST model:

The numerical solution methods for OpenFOAM turbulence models are based on the Finite Volume Method (FVM) or the Finite Element Method (FEM). Being open-source, its greatest advantage is the ability to verify and modify algorithm details at the source code level. Discretization schemes and convergence criteria logic, which are black boxes in commercial solvers, can be directly verified, making it particularly suitable for academic research and method development. Continuous improvement and bug fixes by the community ensure its quality.

Explains the theoretical foundation of numerical solution methods implemented by open-source CAE tools.

The principle of minimum potential energy, fundamental to structural analysis:

The displacement field $\mathbf{u}$ that makes $\Pi$ stationary is the equilibrium solution. CalculiX and Code_Aster implement the Galerkin method based on this variational principle.

The FVM adopted by OpenFOAM is based on integral conservation laws for control volumes:

Discrete equations are obtained by applying this integral form to each control volume and numerically evaluating the fluxes on the faces.

Because the source code is open, algorithm verification by third parties is possible. On the other hand, there is no vendor support like with commercial tools, so information sharing within user communities and forums is important.

• OSS tool results should always be verified against known benchmark problems.
• Be aware of version incompatibilities (especially differences between OpenFOAM forks).
• It is recommended to confirm OSS accuracy by comparing results with commercial tools.
• When documentation is lacking, direct reference to the source code may be necessary.

Understanding the dimensionless parameters governing the physical phenomenon being analyzed is fundamental to appropriate model selection and parameter setting.

• Peclet Number Pe: Relative importance of convection and diffusion. Pe >> 1 indicates convection dominance (stabilization methods required).
• Reynolds Number Re: Ratio of inertial forces to viscous forces. Fundamental parameter for fluid problems.
• Biot Number Bi: Ratio of internal conduction to surface convection. For Bi < 0.1, the lumped capacitance method is applicable.
• Courant Number CFL: Indicator of numerical stability. Explicit methods require CFL ≤ 1.

Dimensional analysis based on Buckingham's Π theorem is effective for order-of-magnitude estimation of analysis results. Using characteristic length $L$, characteristic velocity $U$, and characteristic time $T = L/U$, estimate the order of each physical quantity beforehand to confirm the validity of the analysis results.

Choosing appropriate boundary conditions is directly linked to solution uniqueness and physical validity. Insufficient boundary conditions lead to an ill-posed problem, while excessive ones cause contradictions.

Yeah, you're doing great! Actually getting your hands dirty is the best way to learn. If you don't understand something, feel free to ask anytime.

Explains key points of numerical solution methods and implementation for OpenFOAM turbulence models.

Building from source code uses CMake or dedicated build systems (like OpenFOAM's wmake). Proper version management of dependency libraries (MPI, PETSc, BLAS/LAPACK, etc.) is important. Linux environment is recommended, but using WSL2 or Docker containers makes it possible to set up on Windows as well.