Convergence Study

Convergence Study

Goal

Provide script-driven convergence analysis for verifying that numerical solutions converge at the expected rate as the mesh or timestep is refined.

Requirements

• Python 3.10+
• No third-party packages — scripts use only the Python standard library (math).

Inputs to Gather

Script Outputs (JSON Fields)

Workflow

1. Run grid/timestep refinement study with at least 3 levels
2. Compute observed convergence order with h_refinement.py or dt_refinement.py
3. Compare observed order to expected order of the scheme
4. Estimate discretization error via Richardson extrapolation
5. Report GCI for formal solution verification using gci_calculator.py
6. Document convergence results and any anomalies

Decision Guidance

Do you have 3+ refinement levels?
+-- YES --> Run h_refinement.py or dt_refinement.py
|           +-- Observed order matches expected? --> Solution verified
|           +-- Order too low? --> Check: pre-asymptotic, coding error, insufficient resolution
|           +-- Order too high? --> Check: superconvergence or cancellation effects
+-- NO (only 2 levels) --> Use richardson_extrapolation.py with assumed order
                           (less reliable without order verification)

CLI Examples

# Spatial convergence with 4 grid levels
python3 scripts/h_refinement.py --spacings 0.4,0.2,0.1,0.05 --values 1.16,1.04,1.01,1.0025 --expected-order 2.0 --json

# Temporal convergence with 3 timestep levels
python3 scripts/dt_refinement.py --timesteps 0.04,0.02,0.01 --values 2.12,2.03,2.0075 --expected-order 2.0 --json

# Richardson extrapolation with assumed 2nd-order
python3 scripts/richardson_extrapolation.py --spacings 0.02,0.01 --values 1.0032,1.0008 --order 2.0 --json

# GCI for 3-mesh verification
python3 scripts/gci_calculator.py --spacings 0.04,0.02,0.01 --values 1.0128,1.0032,1.0008 --json

Error Handling

Interpretation Guidance

Asymptotic-ratio caveat (constant refinement ratios). When the refinement ratios are equal (r21 == r32, the common case), the asymptotic ratio AR = GCI_coarse / (r^p * GCI_fine) reduces algebraically to f1/f2. It then only measures the relative gap between the two finest QoI values — not whether the data follow the assumed power law — so an AR near 1.0 can give false reassurance even when the observed order is far from expected. The gci_calculator.py JSON emits a notes entry flagging this. For a real asymptotic-range determination:

1. Compare the observed order p to the scheme's theoretical/expected order — a match is the meaningful evidence of being in the asymptotic range.
2. For stronger verification, use 4+ systematically refined grids and check that the observed order is consistent across successive grid triplets (h_refinement.py reports one order per triplet plus mean_order).

Verification checklist

• Used >= 3 systematically refined grids/timesteps so h_refinement.py / dt_refinement.py can report a results.mean_order; recorded the per-triplet results.observed_orders (a single-pair Richardson run does not verify order).
• Recorded results.mean_order and confirmed results.convergence_assessment reads PASS (observed order within 10% of the scheme's expected order); a FAIL or unknown means the result is not yet verified.
• Confirmed results.in_asymptotic_range is true and that no notes entry reports pre-asymptotic (>50% order variation), negative/non-positive order, or zero error differences before quoting any GCI or extrapolated value.
• Checked refinement ratios r21/r32 from gci_calculator.py are >= 1.3 (round-off noise floor) and that no Oscillatory convergence detected error was raised.
• Recorded results.gci_fine (with the safety factor used: 1.25 for >= 3 grids with verified order, 3.0 for 2 grids with assumed order) as the reported discretization uncertainty, plus results.extrapolated_value as the best estimate.
• For constant refinement ratios, did NOT treat asymptotic_ratio near 1.0 as proof of asymptotic range (it degenerates to f1/f2); confirmed the asymptotic range via observed-order-vs-expected and read the gci_calculator.py notes caveat.

Common pitfalls & rationalizations

Security

Input Validation

• All numeric parameters (spacings, timesteps, values, expected-order, order) are validated as finite positive numbers
• Comma-separated value lists are length-matched (spacings and values must have equal length) and capped at 10,000 entries
• GCI calculator enforces exactly 3 refinement levels; Richardson extrapolation requires at least 2
• Safety factor is validated as a finite number not less than 1.0 (Roache uses Fs in {1.25, 3.0})

File Access

• Scripts read no external files; all inputs are provided via CLI arguments
• Scripts write only to stdout (JSON output); no files are created unless the agent explicitly uses the Write tool

Tool Restrictions

• Bash: Used to execute the four Python analysis scripts (h_refinement.py, dt_refinement.py, richardson_extrapolation.py, gci_calculator.py) with explicit argument lists
• Read: Used to inspect script source and reference documentation

Safety Measures

• No eval(), exec(), or dynamic code generation
• All subprocess calls use explicit argument lists (no shell=True)
• Scripts use only Python standard library (math module); no pickle loading or deserialization of untrusted data
• Minimal tool surface (Bash and Read only) limits the agent's ability to modify the filesystem

References

• references/convergence_theory.md - Formal convergence order, log-log analysis, asymptotic range
• references/gci_guidelines.md - Roache's GCI method, ASME V&V 20, safety factors