How do you assess the grid convergence and mesh quality of your CFD solution?

1 What is grid convergence?

Grid convergence is the process of refining the computational mesh or grid until the CFD solution becomes independent of the grid size and distribution. This means that the solution does not change significantly when you increase the number of grid cells or change the grid spacing. Grid convergence is important because it ensures that your CFD solution is not affected by artificial numerical errors or artifacts caused by the discretization of the governing equations.

2 How to perform grid convergence?

To perform grid convergence, you need to run your CFD model with different grid sizes and compare the solution variables of interest, such as pressure, velocity, temperature, or drag coefficient. You can use different methods to quantify the grid convergence, such as the grid convergence index (GCI), the Richardson extrapolation, or the order of accuracy. These methods estimate the discretization error and the asymptotic behavior of the solution as the grid is refined. You can also plot the solution variables versus the grid size or the inverse of the grid size and check if they converge to a constant value or a linear trend.

5 How to improve mesh quality?

To improve mesh quality, you need to modify the mesh parameters and settings according to your problem and CFD software. This can be done by selecting the right mesh types and schemes, such as structured, unstructured, hybrid, or adaptive meshes. Mesh refinement or coarsening should be used in areas of high or low gradients, curvature, or complexity. Additionally, the mesh size, spacing, growth rate, or transition ratio should be adjusted to control the resolution and smoothness of the mesh. Mesh smoothing or optimization techniques can also be used to reduce the distortion or irregularity of the mesh cells. Lastly, boundary layers should be resolved with adequate y+ values or wall functions.

6 How to balance grid convergence and mesh quality?

Grid convergence and mesh quality are interrelated and often conflicting goals in CFD. On one hand, you want to have a fine and uniform mesh that captures flow features and reduces discretization error. On the other hand, you want a coarse and optimized mesh that saves computational time and resources and avoids numerical instability. To balance grid convergence and mesh quality, start with a coarse mesh and run a preliminary CFD simulation to identify regions of interest and potential problems. Refine the mesh gradually and locally in those regions until you achieve grid convergence for your solution variables. Check the mesh quality statistics and criteria, modifying the mesh parameters and settings if necessary. Repeat the process until you have a converged and high-quality mesh that meets your accuracy and efficiency requirements.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?