SKILL·C79ED9

convergence-study

HeshamFS
Updated 1 month ago
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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

Recommended
Primary
npx skills add HeshamFS/materials-simulation-skills -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/HeshamFS/materials-simulation-skills
Git CloneAlternative
git clone https://github.com/HeshamFS/materials-simulation-skills.git ~/.claude/skills/convergence-study

Copy 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

InputDescriptionExample
Grid spacingsSequence of mesh sizes (coarse to fine)0.4,0.2,0.1,0.05
Timestep sizesSequence of dt values0.04,0.02,0.01
Solution valuesQoI at each refinement level1.16,1.04,1.01,1.0025
Expected orderFormal order of the numerical scheme2.0
Safety factorGCI safety factor (1.25 default)1.25

Script Outputs (JSON Fields)

ScriptKey Outputs
scripts/h_refinement.pyresults.observed_orders, results.mean_order, results.richardson_extrapolated_value, results.convergence_assessment
scripts/dt_refinement.pySame as h_refinement but for temporal convergence
scripts/richardson_extrapolation.pyresults.extrapolated_value, results.error_estimate, results.observed_order
scripts/gci_calculator.pyresults.observed_order, results.gci_fine, results.gci_coarse, results.asymptotic_ratio, results.in_asymptotic_range

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

ErrorCauseResolution
spacings and values must have the same lengthMismatched input arraysProvide equal-length lists
At least 2 refinement levels requiredToo few data pointsAdd more refinement levels
Exactly 3 refinement levels requiredGCI needs 3 levelsProvide fine/medium/coarse
Oscillatory convergence detectedNon-monotone convergenceCheck mesh quality or scheme

Interpretation Guidance

ScenarioMeaningAction
Observed order matches expectedSolution in asymptotic rangeReport GCI, extrapolate
Observed order < expectedPre-asymptotic or coding bugRefine further or debug
Negative observed orderSolution divergingCheck implementation
GCI asymptotic ratio near 1.0Grids in asymptotic rangeResults are reliable
GCI asymptotic ratio far from 1.0Not in asymptotic rangeRefine 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 (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

GitHub Repository

HeshamFS/materials-simulation-skills
Path: skills/core-numerical/convergence-study
0
agent-skillsagentscli-toolscomputational-sciencellmmaterials-science
FAQ

Frequently asked questions

What is the convergence-study skill?

convergence-study is a Claude Skill by HeshamFS. Skills package instructions and resources that Claude loads on demand, so Claude can perform convergence-study-related tasks without extra prompting.

How do I install convergence-study?

Use the install commands on this page: add convergence-study to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.

What category does convergence-study belong to?

convergence-study is in the Documentation category, tagged general.

Is convergence-study free to use?

Yes. convergence-study is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.

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