orcaflex-jumper-analysis
Rigid and flexible jumper modelling in OrcaFlex covering installation analysis, in-place analysis, VIV screening, and fatigue assessment.
Best use case
orcaflex-jumper-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Rigid and flexible jumper modelling in OrcaFlex covering installation analysis, in-place analysis, VIV screening, and fatigue assessment.
Teams using orcaflex-jumper-analysis 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/jumper-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How orcaflex-jumper-analysis Compares
| Feature / Agent | orcaflex-jumper-analysis | 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?
Rigid and flexible jumper modelling in OrcaFlex covering installation analysis, in-place analysis, VIV screening, and fatigue assessment.
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
# OrcaFlex Jumper Analysis Skill ## Description Rigid and flexible jumper modelling in OrcaFlex — covers installation analysis (crane lift, lowering, landing), in-place analysis (VIV, fatigue, extreme response), and parametric studies across environmental headings and sea states. ## When to Use - Jumper installation analysis (SZ/DZ, AHC on/off) - Rigid jumper stress analysis and VIV screening - Flexible jumper fatigue assessment - Multi-section pipe modelling with buoyancy modules - Three-point lift rigging design - Parametric heading/sea-state studies ## Key Concepts ### Multi-Section Pipe - Jumpers typically have 15-25 OrcaFlex line sections with alternating line types - Line types: bare coated pipe, buoyancy modules, strake sections, insulation - Each section has distinct OD, wall thickness, mass, and bending stiffness - Section lengths vary: short connector sections (1-2m) to long pipe runs (50-100m) ### M-Shape Buoyancy Layout - Mid-span buoyancy modules create characteristic M-shape in water column - Module dimensions: typically 10m blocks, density ~0.694 te/m3 - Buoyancy placement defined by arc-length ranges along jumper - Net buoyancy per module determines equilibrium shape ### Rigid End Connectors - Modelled as separate short OrcaFlex lines with very high bending stiffness - Typical: OCS 200-V connectors, OD ~1.8m, length ~0.5-1.0m - `EndBxBendingStiffness: 1.0e+307` (effectively rigid) - Connected to main jumper via end connections ### Installation Rigging Chain The full lift system from vessel to jumper, modelled as linked OrcaFlex objects: 1. **Vessel** — Installation vessel with RAOs 2. **Crane Pedestal** — 6DBuoy at vessel crane location 3. **Crane Boom** — Constraint object (boom geometry) 4. **Crane Wire** — Winch object (main hoist) 5. **Sling** — Line from winch to masterlink 6. **Masterlink** — 3DBuoy (central connection point) 7. **Slings** — Lines from masterlink to spreader bar ends 8. **Spreader Bar** — 6DBuoy (~120ft / 36.6m) 9. **Lift Slings + Turnbuckles** — Lines from spreader bar to clamp points 10. **Clamps** — Attached to jumper at pickup arc lengths ### Three-Point Lift - Spreader bar with asymmetric pickup at 3 arc-length positions - Pickup points determined by jumper COG and weight distribution - Typical: 5 clamps (10-inch jumper clamps, ~0.26 te each) at sling points - COG calculation considers all KIT weights (~46 te total for 4 KITs) ### AHC System (Active Heave Compensation) - Modelled via Winch + ExternalFunction (DLL) - Two analysis variants: AHC-on (DZ) and AHC-off (SZ) - ExternalFunction64.dll provides real-time heave compensation - AHC reduces dynamic tension variation during lowering ### Dual-Zone Analysis | Zone | Depth | Wave Theory | Typical Hs | Focus | |------|-------|-------------|------------|-------| | **SZ** (Splash Zone) | Surface | JONSWAP | 1.5m | Sling loads, vessel motion | | **DZ** (Deep Zone) | Near seabed | Dean Stream | 2.0m | Landing loads, clearance | ### Coatings & Insulation | Type | Density (te/m3) | Thickness (mm) | Purpose | |------|----------------|----------------|---------| | Insulation | 0.979 | 76.2 | Thermal | | Buoyancy | 0.694 | 343 | Net uplift | | Strake | 1.128 | 5 | VIV suppression | ### Two-Step Statics - **Step 1**: User-specified starting positions (rigging geometry) - **Step 2**: Full statics solve (catenary + equilibrium) - Critical for installation models where initial geometry is non-trivial ### Parametric Studies - Environmental headings: 0, 30, 60, 90, 120, 150, 165, 180 degrees - Sea states: Hs = 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.5m - Seeds: 20+ random seeds per (heading, Hs) combination - Total runs per jumper: 200-1000+ simulations ## Model Library ### Available Models | Model | Location | Description | |-------|----------|-------------| | Manifold-to-PLET | `docs/modules/orcaflex/jumper/manifold_to_plet/` | Full installation rigging | | PLET-to-PLEM | `docs/modules/orcaflex/jumper/plet_to_plem/` | Shorter jumper variant | | SUT/MM | `docs/modules/orcaflex/jumper/sut_mm/` | SZ/DZ/resonance variants | ### File Structure ``` docs/modules/orcaflex/jumper/<model>/ ├── monolithic/ # Sanitized original OrcaFlex YAML │ ├── DZ_AHCoff.yml │ ├── SZ.yml │ └── ... └── spec.yml # Extracted spec for modular builder ``` ## Commands ### Generate from spec ```bash uv run python -m digitalmodel.solvers.orcaflex.modular_generator --spec docs/modules/orcaflex/jumper/manifold_to_plet/spec.yml ``` ### Validate round-trip ```bash uv run python scripts/semantic_validate.py \ --mono docs/modules/orcaflex/jumper/manifold_to_plet/monolithic/SZ.yml \ --modular output/generated_model.yml ``` ### Run benchmark ```bash uv run python scripts/benchmark_model_library.py --library-only --three-way --skip-mesh ``` ## Implementation Notes - Jumper models use the `generic` field in `ProjectInputSpec` (not dedicated jumper schema) - The `MonolithicExtractor` handles arbitrary OrcaFlex object types including 3DBuoys, 6DBuoys, Constraints, Winches - Vessel RAO data round-trips through the extractor (verify RAO table sizes) - ExternalFunction DLL references will cause benchmark failures — add to skip list - For parametric studies, use the campaign generator pattern with seed/heading/Hs variations ## Related Skills - `/orcaflex-extreme-analysis` — General installation modelling - `/orcaflex-model-generator` — Modular YAML generation from spec - `/orcaflex-modeling` — Core OrcaFlex modelling patterns - `/orcaflex-specialist` — Advanced OrcaFlex specialist workflows
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