convergence-study
About
This skill performs automated convergence analysis for numerical simulations, computing observed orders of accuracy and estimating discretization errors using Richardson extrapolation and Grid Convergence Index (GCI) calculations. It helps developers verify mesh adequacy, check asymptotic convergence, and prepare formal verification reports per ASME V&V 20 standards. Use it whenever you need to assess solution accuracy or determine if your grid resolution is sufficient.
Quick Install
Claude Code
Recommendednpx skills add HeshamFS/materials-simulation-skills -a claude-code/plugin add https://github.com/HeshamFS/materials-simulation-skillsgit clone https://github.com/HeshamFS/materials-simulation-skills.git ~/.claude/skills/convergence-studyCopy and paste this command in Claude Code to install this skill
Documentation
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 3.8+
- NumPy (not required; scripts use only math stdlib)
Inputs to Gather
| Input | Description | Example |
|---|---|---|
| Grid spacings | Sequence of mesh sizes (coarse to fine) | 0.4,0.2,0.1,0.05 |
| Timestep sizes | Sequence of dt values | 0.04,0.02,0.01 |
| Solution values | QoI at each refinement level | 1.16,1.04,1.01,1.0025 |
| Expected order | Formal order of the numerical scheme | 2.0 |
| Safety factor | GCI safety factor (1.25 default) | 1.25 |
Script Outputs (JSON Fields)
| Script | Key Outputs |
|---|---|
scripts/h_refinement.py | results.observed_orders, results.mean_order, results.richardson_extrapolated_value, results.convergence_assessment |
scripts/dt_refinement.py | Same as h_refinement but for temporal convergence |
scripts/richardson_extrapolation.py | results.extrapolated_value, results.error_estimate, results.observed_order |
scripts/gci_calculator.py | results.observed_order, results.gci_fine, results.gci_coarse, results.asymptotic_ratio, results.in_asymptotic_range |
Workflow
- Run grid/timestep refinement study with at least 3 levels
- Compute observed convergence order with
h_refinement.pyordt_refinement.py - Compare observed order to expected order of the scheme
- Estimate discretization error via Richardson extrapolation
- Report GCI for formal solution verification using
gci_calculator.py - 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
| Error | Cause | Resolution |
|---|---|---|
spacings and values must have the same length | Mismatched input arrays | Provide equal-length lists |
At least 2 refinement levels required | Too few data points | Add more refinement levels |
Exactly 3 refinement levels required | GCI needs 3 levels | Provide fine/medium/coarse |
Oscillatory convergence detected | Non-monotone convergence | Check mesh quality or scheme |
Interpretation Guidance
| Scenario | Meaning | Action |
|---|---|---|
| Observed order matches expected | Solution in asymptotic range | Report GCI, extrapolate |
| Observed order < expected | Pre-asymptotic or coding bug | Refine further or debug |
| Negative observed order | Solution diverging | Check implementation |
| GCI asymptotic ratio near 1.0 | Grids in asymptotic range | Results are reliable |
| GCI asymptotic ratio far from 1.0 | Not in asymptotic range | Refine further |
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 greater than 1.0
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 (
mathmodule); 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 rangereferences/gci_guidelines.md- Roache's GCI method, ASME V&V 20, safety factors
GitHub Repository
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