
aqwa-analysis
by vamseeachanta
Offshore asset lifecycle: ASCII data → strength, fatigue, FFS, hydrodynamics
SKILL.md
name: aqwa-analysis description: 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. version: 3.0.0 updated: 2025-01-02 category: offshore-engineering triggers:
- AQWA analysis
- RAO extraction
- added mass calculation
- damping coefficient
- wave diffraction
- radiation analysis
- AQWA-LINE
- AQWA-DRIFT
- AQWA-LIBRIUM
- AQWA-NAUT
- .LIS files
- .DAT files
- .MES files
AQWA Analysis Skill
Integrate with ANSYS AQWA hydrodynamic software for RAO computation, added mass/damping extraction, and hydrodynamic coefficient management.
Version Metadata
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
digitalmodelpackage 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.
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.
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.
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.
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
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
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
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
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
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
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
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
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
{
"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
- Frequency range - Ensure frequencies cover wave spectrum of interest
- Direction resolution - Use 30° or finer for asymmetric vessels
- Panel density - Verify mesh convergence for accurate results
- Low frequency - Check added mass at low frequencies for stability
- Viscous damping - Always add viscous damping for roll motion
Common Issues
Mesh Quality
# 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
# 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
// 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
// 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:
- Discovery: Identify AQWA output files
- Quality: Validate file integrity
- Extraction: Extract RAOs and coefficients
- Synthesis: Combine multi-body results
- Validation: Check physical consistency
- Integration: Export to OrcaFlex format
5. Benchmark Comparison vs OrcaWave
Compare AQWA results with OrcaWave for validation focusing on peak/significant values.
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:
# 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
.LISfiles - 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:
# 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:
# 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
- hydrodynamics - Coefficient management
- orcaflex-modeling - Apply RAOs in OrcaFlex
- orcawave-analysis - Benchmark validation
- mooring-design - 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 management
Score
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