aceengineer/digitalmodel-diffraction-rao

install_bokalift_2 OrcaWave Diffraction RAOs (parametric screening)

Analysis/computation results published for query + visualization via the Hugging Face datasets-server API. Generated by scripts/hf/save_results_to_hf.py.

Tables

config	rows	cols
hydrostatics	2	7
rao	600	10

Data-quality notes

Screening-grade, parametric hull (public particulars for vessel_db install_bokalift_2), de-identified — not class-approved; faithful-to-solver ≠ correct for design.

• Low-frequency artifact (flagged, not omitted): in rao, heave & sway at freq ≤ 0.35 rad/s (T ≥ 18 s) in non-beam headings are physically implausible (heave ≈ 7–9 m/m where beam seas correctly show ≈ 1.1). 40 rows carry reliability = "low_freq_artifact". Use freq ≥ 0.5 rad/s. Tracked: digitalmodel#1548.
• Withheld table — added_mass_damping: the source matrices declare units kg / kg·m² but magnitudes are consistent with te / te·m² (~1000× off, e.g. heave added-mass 58,447 vs ~1e8 kg expected). Withheld pending unit reconciliation. Tracked: digitalmodel#1550.
• Withheld — operation-envelope (Hs/Tp) limits: derived limits reach implausible values (~17 m Hs heavy-lift) and would mislead as operational limits. A class panel mesh is required first.
• Roll at resonance is high (23.5 deg/m at ζ=5 %, 19.2 at ζ=10 %) — a real, expected consequence of light damping, not an artifact.

Provenance: OrcaWave diffraction, de-identified from run lr_acma_3bd7c32b74db; vessel referenced only by vessel_db ID install_bokalift_2 (public particulars). Not a run-identity ledger (that is the separate aceengineer/*-runs contract).

Provenance

• Source repo: digitalmodel · algorithm: orcawave-diffraction@1537

• Schema version: 1.0.0

• Source snapshots (sha256):

• hydrostatics.parquet — sha256 3059b2cd485a3be5…

• rao.parquet — sha256 0644631ee77e0745…

Nulls are genuine missing values (non-finite floats were coerced to null). Load a table: pandas.read_parquet(hf_hub_download("aceengineer/digitalmodel-diffraction-rao", "<config>.parquet", repo_type="dataset")).