aqwa-analysis
Integrate with AQWA hydrodynamic software for RAO computation, damping analysis, and coefficient extraction. Use for AQWA file processing, RAO calculation, hydrodynamic coefficient extraction, and pre/post processing workflows.
Best use case
aqwa-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Integrate with AQWA hydrodynamic software for RAO computation, damping analysis, and coefficient extraction. Use for AQWA file processing, RAO calculation, hydrodynamic coefficient extraction, and pre/post processing workflows.
Teams using aqwa-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/aqwa-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How aqwa-analysis Compares
| Feature / Agent | aqwa-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?
Integrate with AQWA hydrodynamic software for RAO computation, damping analysis, and coefficient extraction. Use for AQWA file processing, RAO calculation, hydrodynamic coefficient extraction, and pre/post processing workflows.
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
# AQWA Analysis Skill
Integrate with ANSYS AQWA hydrodynamic software for RAO computation, added mass/damping extraction, and hydrodynamic coefficient management.
## Version Metadata
```yaml
version: 3.0.0
python_min_version: '3.10'
dependencies:
hydrodynamics: '>=1.0.0,<2.0.0'
compatibility:
tested_python:
- '3.10'
- '3.11'
- '3.12'
- '3.13'
os:
- Windows
- Linux
- macOS
```
## Changelog
### [3.0.0] - 2026-01-07
**Added:**
- Initial version metadata and dependency management
- Semantic versioning support
- Compatibility information for Python 3.10-3.13
**Changed:**
- Enhanced skill documentation structure
## When to Use
- AQWA hydrodynamic analysis post-processing
- RAO (Response Amplitude Operator) computation
- Hydrodynamic coefficient extraction
- AQWA file processing (LIS, DAT, MES)
- Added mass and damping matrix extraction
- Viscous damping determination
- Pre/post processing workflows
- Diffraction/radiation analysis (AQWA-LINE)
- Time domain motions (AQWA-DRIFT)
- Stability analysis (AQWA-LIBRIUM)
- Cable dynamics (AQWA-NAUT)
- Coupled analysis (AQWA-WAVE)
## Agent Capabilities
This skill integrates agent capabilities from `/agents/aqwa/`:
### Domain Expertise
- **Software**: ANSYS AQWA
- **Analysis Modules**:
- AQWA-LINE: Diffraction/Radiation analysis
- AQWA-DRIFT: Time domain motions
- AQWA-LIBRIUM: Stability analysis
- AQWA-NAUT: Cable dynamics
- AQWA-WAVE: Coupled analysis
### Core Capabilities
- First-order wave forces
- Second-order drift forces
- Multi-body interactions
- Mooring and riser systems
- Hydrodynamic coefficients
- RAO calculations
### Industry Standards
- DNV-RP-C205 (Environmental Conditions)
- API RP 2SK (Stationkeeping)
- ISO 19901-7 (Mooring Systems)
- IEC 61400-3 (Wind Turbines)
### Context Optimization
- Cross-references: OrcaFlex, ANSYS Mechanical
- Focused domain: AQWA hydrodynamics
- Max context size: 16000 tokens
## Prerequisites
- Python environment with `digitalmodel` package installed
- AQWA output files (LIS, DAT, or MES format)
- For running AQWA: ANSYS AQWA license
## Analysis Types
### 1. RAO Extraction
Extract RAOs from AQWA results.
```yaml
aqwa_analysis:
rao_extraction:
flag: true
input_file: "aqwa_results/vessel.LIS"
vessel_name: "FPSO"
wave_directions: [0, 45, 90, 135, 180]
output:
rao_file: "results/vessel_raos.csv"
plot_file: "results/rao_plots.html"
format: "amplitude_phase" # or real_imaginary
```
### 2. Hydrodynamic Coefficients
Extract added mass and damping matrices.
```yaml
aqwa_analysis:
coefficients:
flag: true
input_file: "aqwa_results/vessel.LIS"
frequencies: "all" # or specific list [0.1, 0.2, 0.3]
output:
added_mass_file: "results/added_mass.csv"
damping_file: "results/damping.csv"
matrices_file: "results/hydro_matrices.json"
```
### 3. AQWA File Processing
Parse and process AQWA output files.
```yaml
aqwa_analysis:
file_processing:
flag: true
files:
- path: "aqwa_results/vessel.LIS"
type: "lis"
- path: "aqwa_results/vessel.DAT"
type: "dat"
extract:
- "raos"
- "added_mass"
- "damping"
- "wave_forces"
- "drift_forces"
output_directory: "results/aqwa_processed/"
```
### 4. Viscous Damping
Determine viscous damping from decay tests or empirical methods.
```yaml
aqwa_analysis:
viscous_damping:
flag: true
method: "empirical" # or decay_test
vessel:
length: 300.0
beam: 50.0
draft: 20.0
motions: ["roll", "pitch", "heave"]
empirical_factors:
roll_percentage: 5.0 # % of critical
pitch_percentage: 3.0
heave_percentage: 2.0
output:
damping_file: "results/viscous_damping.json"
```
## Python API
### RAO Extraction
```python
from digitalmodel.modules.aqwa.aqwa_raos import AqwaRAOs
# Initialize RAO extractor
raos = AqwaRAOs()
# Load AQWA results
raos.load("aqwa_results/vessel.LIS")
# Get RAO for specific motion and direction
surge_rao = raos.get_rao(
motion="surge",
wave_direction=180.0 # degrees (head seas)
)
# Get all RAOs as DataFrame
rao_df = raos.to_dataframe()
# Columns: frequency, direction, surge_amp, surge_phase, sway_amp, ...
# Plot RAOs
raos.plot_rao(
motions=["heave", "pitch", "roll"],
directions=[0, 90, 180],
output_file="results/rao_comparison.html"
)
# Export to OrcaFlex format
raos.export_orcaflex("vessel_raos.yml")
```
### Hydrodynamic Coefficient Extraction
```python
from digitalmodel.modules.aqwa.aqwa_reader import AqwaReader
from digitalmodel.modules.aqwa.aqwa_analysis import AqwaAnalysis
# Initialize reader
reader = AqwaReader()
# Load AQWA output
data = reader.read("aqwa_results/vessel.LIS")
# Get added mass matrix at specific frequency
frequency = 0.1 # rad/s
added_mass = data.get_added_mass(frequency)
# Returns 6x6 numpy array
# Get damping matrix
damping = data.get_damping(frequency)
# Returns 6x6 numpy array
# Get frequency-dependent matrices
frequencies = data.get_frequencies()
for freq in frequencies:
A = data.get_added_mass(freq)
B = data.get_damping(freq)
print(f"ω = {freq:.3f}: A33 = {A[2,2]:.1f}, B33 = {B[2,2]:.1f}")
```
### AQWA Analysis Router
```python
from digitalmodel.modules.aqwa.aqwa_analysis import AqwaAnalysis
# Initialize analysis
aqwa = AqwaAnalysis()
# Configure analysis
cfg = {
"aqwa": {
"input_file": "aqwa_results/vessel.LIS",
"extract": ["raos", "added_mass", "damping", "drift_forces"],
"output_directory": "results/"
}
}
# Run extraction
results = aqwa.run(cfg)
# Access results
raos = results["raos"]
added_mass = results["added_mass"]
damping = results["damping"]
drift = results["drift_forces"]
```
### Pre-Processing
```python
from digitalmodel.modules.aqwa.aqwa_preprocess import AqwaPreProcess
# Initialize pre-processor
preprocess = AqwaPreProcess()
# Generate AQWA input from vessel geometry
preprocess.generate_input(
vessel_geometry="geometry/hull.stl",
water_depth=1000.0,
wave_frequencies=[0.05, 0.1, 0.15, 0.2, 0.3, 0.5, 0.8, 1.0],
wave_directions=[0, 45, 90, 135, 180],
output_file="aqwa_input/vessel.dat"
)
```
### Post-Processing
```python
from digitalmodel.modules.aqwa.aqwa_postprocess import AqwaPostProcess
# Initialize post-processor
postprocess = AqwaPostProcess()
# Load results
postprocess.load("aqwa_results/vessel.LIS")
# Generate comprehensive report
postprocess.generate_report(
output_file="results/aqwa_report.html",
include=[
"summary",
"rao_plots",
"coefficient_tables",
"drift_force_plots"
]
)
# Validate results
validation = postprocess.validate()
if validation["warnings"]:
for warning in validation["warnings"]:
print(f"Warning: {warning}")
```
### Result Validation
```python
from digitalmodel.modules.aqwa.aqwa_validator import AqwaValidator
# Initialize validator
validator = AqwaValidator()
# Load results
validator.load("aqwa_results/vessel.LIS")
# Run validation checks
results = validator.validate()
# Check for common issues
if not results["symmetry_check"]:
print("Warning: RAOs not symmetric for symmetric vessel")
if not results["low_frequency_check"]:
print("Warning: Low frequency added mass may be inaccurate")
if not results["radiation_check"]:
print("Warning: Radiation damping check failed")
# Kramers-Kronig causality check
kk_result = validator.kramers_kronig_check()
if not kk_result["passed"]:
print(f"Causality violation at frequencies: {kk_result['violations']}")
```
## Key Classes
| Class | Purpose |
|-------|---------|
| `AqwaAnalysis` | Main analysis router |
| `AqwaRAOs` | RAO computation and export |
| `AqwaReader` | File parsing (LIS, DAT, MES) |
| `AqwaPreProcess` | Input file generation |
| `AqwaPostProcess` | Results post-processing |
| `AqwaValidator` | Result validation |
## File Format Support
### LIS Files (Listing Output)
Primary output file containing:
- RAOs (amplitude and phase)
- Added mass matrices
- Damping matrices
- Wave excitation forces
- Drift forces
### DAT Files (Data Input)
Input file containing:
- Hull geometry
- Mass properties
- Analysis settings
- Wave conditions
### MES Files (Mesh)
Mesh definition:
- Panel geometry
- Node coordinates
- Panel connectivity
## Configuration Examples
### Complete AQWA Workflow
```yaml
basename: aqwa_analysis
aqwa_analysis:
# Step 1: Process AQWA output
file_processing:
flag: true
input_file: "aqwa_results/fpso.LIS"
output_directory: "results/"
# Step 2: Extract RAOs
rao_extraction:
flag: true
wave_directions: [0, 30, 60, 90, 120, 150, 180]
output:
rao_file: "results/fpso_raos.csv"
orcaflex_file: "results/fpso_raos.yml"
plots: "results/rao_plots.html"
# Step 3: Extract coefficients
coefficients:
flag: true
output:
added_mass: "results/added_mass.csv"
damping: "results/damping.csv"
# Step 4: Add viscous damping
viscous_damping:
flag: true
method: "percentage_critical"
values:
roll: 5.0
pitch: 3.0
heave: 2.0
# Step 5: Validate results
validation:
flag: true
checks:
- symmetry
- low_frequency
- kramers_kronig
output:
report: "results/validation_report.json"
```
## Output Formats
### RAO CSV Format
```csv
frequency_rad_s,direction_deg,surge_amp,surge_phase,sway_amp,sway_phase,heave_amp,heave_phase,roll_amp,roll_phase,pitch_amp,pitch_phase,yaw_amp,yaw_phase
0.100,0.0,0.985,178.2,0.000,0.0,1.023,-2.5,0.000,0.0,0.156,175.8,0.000,0.0
0.100,90.0,0.000,0.0,0.978,175.4,1.015,-3.2,2.345,-8.5,0.000,0.0,0.012,92.1
```
### Coefficient Matrices JSON
```json
{
"frequencies_rad_s": [0.1, 0.2, 0.3, 0.5, 0.8],
"added_mass": {
"0.1": [[1.2e6, 0, 0, 0, 1.5e7, 0],
[0, 1.3e6, 0, -1.2e7, 0, 0],
...],
"0.2": [...]
},
"damping": {
"0.1": [[2.5e5, 0, 0, 0, 3.2e6, 0],
...],
"0.2": [...]
}
}
```
## Best Practices
1. **Frequency range** - Ensure frequencies cover wave spectrum of interest
2. **Direction resolution** - Use 30° or finer for asymmetric vessels
3. **Panel density** - Verify mesh convergence for accurate results
4. **Low frequency** - Check added mass at low frequencies for stability
5. **Viscous damping** - Always add viscous damping for roll motion
## Common Issues
### Mesh Quality
```python
# Check mesh quality before running
from digitalmodel.modules.aqwa.mesh_check import AqwaMeshCheck
mesh = AqwaMeshCheck()
mesh.load("geometry/hull.mes")
quality = mesh.check_quality()
if quality["min_aspect_ratio"] < 0.1:
print("Warning: Poor aspect ratio panels detected")
if quality["intersecting_panels"] > 0:
print(f"Error: {quality['intersecting_panels']} intersecting panels")
```
### Result Validation
```python
# Always validate extracted coefficients
validator = AqwaValidator()
validator.load("results/vessel.LIS")
# Check physical consistency
if not validator.check_positive_definite_damping():
print("Warning: Damping matrix not positive definite")
if not validator.check_symmetric_added_mass():
print("Warning: Added mass matrix not symmetric")
```
## MCP Tool Integration
### Swarm Coordination
```javascript
// Initialize hydrodynamic analysis swarm
mcp__claude-flow__swarm_init { topology: "mesh", maxAgents: 4 }
// Spawn specialized agents
mcp__claude-flow__agent_spawn { type: "analyst", name: "aqwa-processor" }
mcp__claude-flow__agent_spawn { type: "code-analyzer", name: "coefficient-extractor" }
```
### Memory Coordination
```javascript
// Store extracted RAOs
mcp__claude-flow__memory_usage {
action: "store",
key: "aqwa/raos/vessel",
namespace: "hydrodynamics",
value: JSON.stringify({
vessel: "FPSO",
directions: [0, 45, 90, 135, 180],
frequencies: 50,
timestamp: Date.now()
})
}
// Store coefficient matrices
mcp__claude-flow__memory_usage {
action: "store",
key: "aqwa/coefficients/added_mass",
namespace: "hydrodynamics",
value: JSON.stringify({
frequency_count: 25,
matrix_size: "6x6",
validated: true
})
}
```
### Phased Processing Workflow
The agent uses a phased approach for AQWA processing:
1. **Discovery**: Identify AQWA output files
2. **Quality**: Validate file integrity
3. **Extraction**: Extract RAOs and coefficients
4. **Synthesis**: Combine multi-body results
5. **Validation**: Check physical consistency
6. **Integration**: Export to OrcaFlex format
### 5. Benchmark Comparison vs OrcaWave
Compare AQWA results with OrcaWave for validation focusing on peak/significant values.
```yaml
aqwa_analysis:
benchmark_comparison:
flag: true
aqwa_results: "aqwa_results/vessel.LIS"
orcawave_results: "orcawave_results/vessel.sim" # Optional
tolerance: 0.05 # 5% tolerance for peaks
peak_threshold: 0.10 # 10% of peak magnitude
output:
comparison_report: "results/aqwa_orcawave_comparison.html"
peak_analysis: "results/peak_values_comparison.html"
```
### Peak-Focused Validation
**Using the Standalone Comparison Script:**
```bash
# Run peak-focused comparison (AQWA vs OrcaWave)
cd docs/modules/orcawave/L01_aqwa_benchmark
python run_comparison_peaks.py
# Output:
# - comparison_results/peak_comparison_YYYYMMDD_HHMMSS.html
# - Peak RAO values for all 6 DOFs
# - Statistical analysis focused on significant values (≥10% of peak)
```
**Script Features:**
- Extracts AQWA RAOs from `.LIS` files
- Identifies peak values for each DOF (heave, pitch, roll, surge, sway, yaw)
- Compares peaks with OrcaWave results (when available)
- Applies 5% tolerance to significant values only
- Generates interactive HTML report with visualization
**For Custom Heading-by-Heading Analysis:**
```bash
# Run comprehensive heading-by-heading comparison
cd docs/modules/orcawave/L01_aqwa_benchmark
python run_proper_comparison.py
# Output:
# - comparison_results/final_comparison_YYYYMMDD_HHMMSS.html
# - Heading-by-heading RAO comparison for all 6 DOFs
# - Statistical analysis with mean/max differences
# - Interactive plots with period-based comparison
```
**For Module-Level Integration:**
```python
# Use the diffraction comparison framework module
from digitalmodel.modules.diffraction.comparison_framework import DiffractionComparator
from digitalmodel.modules.diffraction.aqwa_converter import AQWAConverter
# Extract AQWA data
converter = AQWAConverter(
analysis_folder="docs/modules/orcawave/L01_aqwa_benchmark",
vessel_name="SHIP_RAOS"
)
aqwa_results = converter.convert_to_unified_schema(water_depth=30.0)
# Create diffraction comparator (requires both AQWA and OrcaWave results)
# comparator = DiffractionComparator(
# aqwa_results=aqwa_results,
# orcawave_results=orcawave_results, # Both required
# tolerance=0.05 # 5% tolerance
# )
#
# # Compare RAOs
# rao_comparison = comparator.compare_raos()
#
# # Generate comprehensive report
# report = comparator.generate_report()
# print(f"RAO comparison complete")
# Note: For actual benchmark analysis, use the standalone scripts above
```
## Benchmark Validation Criteria
**Engineering Standard Practice:**
- 5% tolerance applies to **peak and significant values only**
- "Significant" = RAO magnitude ≥ 10% of peak value
- Pass requires 90% of significant points within 5% tolerance
- Low-amplitude responses (<10% of peak) excluded from validation
- Focus on resonance regions and operationally important periods
**Typical Peak Values by DOF:**
- **Heave**: 0.9-1.1 m/m (near natural period)
- **Pitch**: 0.4-0.6 deg/m (near natural period)
- **Roll**: 2-5 deg/m (beam seas, low frequency)
- **Surge**: 0.8-1.0 m/m (following seas)
- **Sway**: 0.8-1.0 m/m (beam seas)
- **Yaw**: Small (<0.1 deg/m for symmetric vessels)
## Related Skills
- [diffraction-analysis](../diffraction-analysis/SKILL.md) - **Master skill** for all diffraction workflows
- [bemrosetta](../bemrosetta/SKILL.md) - AQWA → OrcaFlex conversion with QTF and mesh support
- [hydrodynamics](../hydrodynamics/SKILL.md) - Coefficient management
- [orcaflex-modeling](../orcaflex-modeling/SKILL.md) - Apply RAOs in OrcaFlex
- [orcawave-analysis](../orcawave-analysis/SKILL.md) - Benchmark validation
- [mooring-design](../mooring-design/SKILL.md) - Vessel motion input
## References
- ANSYS AQWA User Manual
- DNV-RP-C205: Environmental Conditions and Environmental Loads
- Newman, J.N.: Marine Hydrodynamics
- Agent Configuration: `agents/aqwa/agent.yaml`
---
## Version History
- **3.1.0** (2026-01-05): Added peak-focused benchmark comparison framework, 5% tolerance validation for significant values, automated AQWA vs OrcaWave comparison scripts
- **3.0.0** (2025-01-02): Merged agent capabilities from agents/aqwa/, added MCP integration, phased processing workflow, AQWA module descriptions
- **2.0.0** (2024-11-15): Added validation and preprocessing modules
- **1.0.0** (2024-10-01): Initial release with RAO extraction and coefficient managementRelated Skills
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