mesh-generation

Mesh resolution planning estimates grid spacing (dx) and cell counts based on domain size and feature scales. Use the grid_sizing.py script with your domain length and desired resolution to compute appropriate discretization that resolves critical features with sufficient points across interfaces, ensuring simulation accuracy without over-refinement.

Aspect ratio, skewness, and orthogonality are critical quality metrics that affect solver stability and convergence. The mesh_quality.py script evaluates these metrics to identify problematic cells; high skewness or poor aspect ratios can cause numerical instability, so checking these before simulation prevents divergence and accuracy loss.

Adaptive mesh refinement balances accuracy and computational cost by refining regions where errors are largest. This workflow provides guidance by assessing current mesh quality and feature resolution, helping you identify under-resolved areas and determine optimal refinement locations to improve solution accuracy efficiently.

Yes, this workflow supports simple to complex geometries in 2D and 3D, including boundary layer handling. It evaluates feature size and aspect ratio across domains, allowing you to plan appropriate mesh spacing near walls and in bulk regions to capture both thin boundary layers and far-field behavior accurately.

This workflow requires Python 3.8+ with no external dependencies, making it lightweight and portable across systems. CLI scripts produce structured JSON or human-readable output, supporting rapid iteration on mesh parameters without installing specialized meshing libraries or solvers.

Resolution planning ensures features are resolved by calculating the number of grid points across a characteristic length scale. By specifying feature size and desired point count (typically 5+ points minimum), you verify that your chosen dx is fine enough to capture the physics accurately without unnecessary over-refinement.