hydrodynamic-analysis-application-3-added-mass-convergence-check
Sub-skill of hydrodynamic-analysis: Application 3: Added Mass Convergence Check.
Best use case
hydrodynamic-analysis-application-3-added-mass-convergence-check is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of hydrodynamic-analysis: Application 3: Added Mass Convergence Check.
Teams using hydrodynamic-analysis-application-3-added-mass-convergence-check should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/application-3-added-mass-convergence-check/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How hydrodynamic-analysis-application-3-added-mass-convergence-check Compares
| Feature / Agent | hydrodynamic-analysis-application-3-added-mass-convergence-check | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of hydrodynamic-analysis: Application 3: Added Mass Convergence Check.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Application 3: Added Mass Convergence Check
## Application 3: Added Mass Convergence Check
```python
def check_added_mass_convergence(
panel_counts: list,
added_mass_results: list
) -> dict:
"""
Check convergence of added mass with panel count.
Args:
panel_counts: List of panel counts
added_mass_results: List of 6x6 added mass matrices
Returns:
Convergence assessment
"""
import plotly.graph_objects as go
# Check heave added mass convergence
A33_values = [A[2, 2] for A in added_mass_results]
# Calculate relative change
relative_changes = [
abs(A33_values[i] - A33_values[i-1]) / A33_values[i-1] * 100
for i in range(1, len(A33_values))
]
# Plot convergence
fig = go.Figure()
fig.add_trace(go.Scatter(
x=panel_counts,
y=A33_values,
name='A33 (Heave Added Mass)',
mode='lines+markers'
))
fig.update_layout(
title='Added Mass Convergence Study',
xaxis_title='Panel Count',
yaxis_title='A33 (tonnes)',
hovermode='x unified'
))
fig.write_html('reports/added_mass_convergence.html')
# Convergence criteria: < 1% change
converged = relative_changes[-1] < 1.0 if relative_changes else False
return {
'converged': converged,
'final_value': A33_values[-1],
'relative_change_percent': relative_changes[-1] if relative_changes else 0,
'recommended_panels': panel_counts[-1] if converged else 'Increase further'
}
# Example
panel_counts = [1000, 2000, 5000, 10000, 15000]
A_results = [
np.diag([15000, 15000, 45000, 1e6, 1e6, 5e5]),
np.diag([15000, 15000, 48000, 1e6, 1e6, 5e5]),
np.diag([15000, 15000, 49500, 1e6, 1e6, 5e5]),
np.diag([15000, 15000, 50000, 1e6, 1e6, 5e5]),
np.diag([15000, 15000, 50100, 1e6, 1e6, 5e5])
]
convergence = check_added_mass_convergence(panel_counts, A_results)
print(f"Converged: {convergence['converged']}")
print(f"Recommended panels: {convergence['recommended_panels']}")
```Related Skills
mnt-analysis-cleanup
Survey, classify, and clean up `/mnt/local-analysis/` (or any sibling-to-workspace-hub directory holding orphan worktrees, codex-burn artifacts, agent log accumulations, and outer-clone duplicates) without losing useful code/work. Surfaces a tiered approval menu rather than baking decisions; defers all destructive ops until user confirms.
repo-architecture-analysis
Scan a Python repo's package structure, count classes/functions, classify module maturity (PRODUCTION/DEVELOPMENT/SKELETON/GAP), and generate architecture reports with Mermaid diagrams. Use when asked to analyze codebase structure, find untested packages, or assess module maturity.
viv-analysis
Assess vortex-induced vibration (VIV) for risers and tubular members with natural frequency and safety factor calculations. Use for VIV susceptibility analysis, natural frequency calculation, vortex shedding assessment, and tubular member fatigue from VIV.
structural-analysis
Structural analysis for marine and offshore structures per DNV/API/ISO codes. Use when performing ULS/ALS limit state checks, column buckling, beam deflection, tubular joint capacity (DNV-RP-C203), or stiffened panel analysis. Covers section properties, combined loading, and ALS dented pipe assessment.
signal-analysis
Perform signal processing, rainflow cycle counting, and spectral analysis for fatigue and time series data. Use for analyzing stress time histories, computing FFT/PSD, extracting fatigue cycles (ASTM E1049-85), and batch processing OrcaFlex signals.
orcawave-qtf-analysis
Second-order wave force QTF computation in OrcaWave. Use when computing mean drift forces, difference-frequency or sum-frequency QTFs, slow-drift response, or applying Newman approximation for offshore structures.
orcaflex-modal-analysis
Perform modal and frequency analysis on OrcaFlex models to extract natural frequencies, mode shapes, and identify dominant DOF responses. Use for VIV assessment, resonance identification, and structural dynamics characterization.
orcaflex-jumper-analysis
Rigid and flexible jumper modelling in OrcaFlex covering installation analysis, in-place analysis, VIV screening, and fatigue assessment.
orcaflex-installation-analysis
Create and analyze OrcaFlex models for offshore installation sequences including subsea structure lowering, pipeline installation, and crane operations. Generate models at multiple water depths and orientations for installation feasibility studies.
orcaflex-extreme-analysis
Extract extreme response values with linked statistics from OrcaFlex simulations. Use for design load identification, max/min extraction with associated values, and extreme event characterization.
hydrodynamics
Manage hydrodynamic coefficients, wave spectra, and environmental loading for vessel response analysis. Use for 6×6 matrix management, wave spectrum modeling, OCIMF loading calculations, and RAO interpolation.
diffraction-analysis
Master skill for hydrodynamic diffraction analysis - AQWA, OrcaWave, and BEMRosetta integration