Grid Convergence Index (GCI) for STAR-CCM+

Start from the engineering decision, not the menu option

Use systematic mesh refinement and GCI when three reasonably related grids can establish observed order and numerical uncertainty. In STAR-CCM+, the relevant setting only becomes meaningful when it is tied to a measurable output, a physical scale and a stated modelling assumption.

This guide deliberately avoids a release-specific click sequence. Interface names and solver options can change between releases, while the engineering checks remain stable. Use the Siemens documentation for your installed release to confirm exact menu names after the physical decision is clear.

Inputs and definitions to verify first

Before changing solver controls, confirm the quantities that actually define this problem. The most important checks for this topic are:

• refinement ratio: confirm the value, definition, units and spatial location before using it to justify the setup.
• same modelling assumptions: confirm the value, definition, units and spatial location before using it to justify the setup.
• quantity of interest: confirm the value, definition, units and spatial location before using it to justify the setup.
• iterative error below discretization change: confirm the value, definition, units and spatial location before using it to justify the setup.

Recommended STAR-CCM+ workflow

1. Define the engineering output.

   Write down the quantity that will determine success before changing Grid Convergence Index (GCI) for STAR-CCM+. This prevents a software setting from becoming the objective itself.

2. Verify the physical inputs.

   Check refinement ratio and same modelling assumptions first. Then confirm quantity of interest and iterative error below discretization change are consistent with the real operating condition.

3. Create a documented baseline.

   Run one traceable baseline with the model, mesh, boundary conditions and reference values recorded before tuning secondary options.

4. Monitor solution evidence.

   Track observed order, extrapolated value and GCI/asymptotic ratio. A stable residual history alone is not enough if the engineering evidence is still drifting.

5. Run one targeted sensitivity.

   Change the parameter that most directly controls the uncertainty and confirm that the engineering conclusion does not depend on one arbitrary setting.

What evidence should support the final setup?

The setup is credible when the engineering outputs are stable for the right reason—not merely because the solver stopped changing quickly. Build the evidence around:

• observed order: compare the baseline with at least one targeted sensitivity or independent physical expectation.
• extrapolated value: compare the baseline with at least one targeted sensitivity or independent physical expectation.
• GCI/asymptotic ratio: compare the baseline with at least one targeted sensitivity or independent physical expectation.

Where possible, compare these signals with a hand calculation, correlation, test value, conservation balance or a deliberately simplified CFD case. Independent checks are especially useful before increasing model complexity.

Common failure modes

• changing mesh strategy between levels.
• using only two grids and calling it GCI.
• letting iterative noise dominate grid differences.

If one of these appears, return to the physical definition before tuning relaxation, discretization or convergence controls. Numerical tuning should not compensate for an inconsistent model.

Turn the guide into an engineering check

Related STAR-CCM+ guides