Agent skill

aqwa-output

AQWA output formats (RAO CSV, coefficient JSON), LIS parsing conventions, result validation, benchmark comparison vs OrcaWave, and validation criteria.

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SKILL.md

AQWA Output Skill

Output formats, LIS parsing conventions, result validation, and benchmark comparison for ANSYS AQWA. See aqwa for Python API.

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], ...] },
  "damping": { "0.1": [[2.5e5, 0, 0, 0, 3.2e6, 0], ...] }
}

LIS Parsing Conventions

  • ADDED MASS header has double space — use whitespace normalization for matching
  • WAVE FREQUENCY line appears ~23 lines before DAMPING section — search backward 30 lines
  • Matrix rows in added mass/damping sections are separated by blank lines
  • First RAO section = displacement RAOs; subsequent sections = velocity/acceleration (skip these)

Post-Processing & Validation

python
from digitalmodel.aqwa.aqwa_postprocess import AqwaPostProcess
from digitalmodel.aqwa.aqwa_validator import AqwaValidator

postprocess = AqwaPostProcess()
postprocess.load("aqwa_results/vessel.LIS")
postprocess.generate_report(
    output_file="results/aqwa_report.html",
    include=["summary", "rao_plots", "coefficient_tables", "drift_force_plots"]
)

validator = AqwaValidator()
validator.load("aqwa_results/vessel.LIS")
results = validator.validate()
# Check: symmetry_check, low_frequency_check, radiation_check

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")

kk_result = validator.kramers_kronig_check()

Benchmark Comparison vs OrcaWave

yaml
aqwa_analysis:
  benchmark_comparison:
    flag: true
    aqwa_results: "aqwa_results/vessel.LIS"
    orcawave_results: "orcawave_results/vessel.sim"
    tolerance: 0.05
    peak_threshold: 0.10
    output:
      comparison_report: "results/aqwa_orcawave_comparison.html"
      peak_analysis: "results/peak_values_comparison.html"

Standalone Comparison Scripts

bash
# Peak-focused comparison
cd docs/modules/orcawave/L01_aqwa_benchmark
python run_comparison_peaks.py

# Heading-by-heading comparison
python run_proper_comparison.py

Module-Level Integration

python
from digitalmodel.diffraction.aqwa_converter import AQWAConverter

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)
# Use with DiffractionComparator for full benchmark analysis

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

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

  • aqwa — Hub skill with Python API
  • aqwa/input — Input formats and configurations
  • aqwa/reference — Solver stages and OPTIONS keywords
  • orcawave/analysis — OrcaWave benchmark validation

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