Limitations

VANE states its assumptions explicitly so results are not over-interpreted.

Input format and rotor

  • Modern ``.lin`` only. The legacy OpenFAST linearization format is rejected.

  • Three-bladed rotors. The MBC3 transform and the blade collective/regressive/progressive taxonomy assume a three-bladed rotor. Two- or many-bladed rotors are out of scope for the multi-blade coordinate path; purely non-rotating models (no blade triplets) are averaged without the transform.

Averaged-model assumptions

  • The averaged non-rotating model approximates a linear time-periodic system by its azimuth average. It is exact for an isotropic rotor and degrades with anisotropy; the azimuth-spread uncertainty quantifies how much. A coherent sweep (distinct azimuths, constant rotor speed) is required and enforced.

  • Adequate azimuth coverage is the user’s responsibility; VANE rejects degenerate sweeps but does not synthesise missing azimuths.

Mode tracking

The cross-operating-point tracker extracts each Campbell line as the globally optimal single-path continuity through the sweep, then removes it and repeats. This is deterministic and avoids greedy adjacent-point errors, but it is a sequential extraction, not a simultaneous globally optimal multi-track assignment: in dense spectra or at near-degenerate crossings, an earlier high-scoring path can still claim a node a later path would have preferred. Tracks flagged is_ambiguous mark exactly these hotspots, and the per-track confidence (the weakest-link correlation) exposes how trustworthy a line is. Treat ambiguous, low-confidence tracks as candidates for manual review.

Resonance detection

Resonance detection reports line crossings between a mode curve and an nP excitation line, with the crossing rotor speed, frequency, and damping interpolated linearly, a damping-aware severity (a lightly damped crossing is more dangerous than a well-damped one), and optional operating-window filtering. The excitation family defaults to 1P/3P/6P/9P and can be set to any positive-integer harmonics. It does not yet provide closest-approach margin bands or uncertainty-propagated crossings.

AI components are experimental

The Gaussian-process classifier and the ensemble fusion are experimental. The default model is bootstrapped on synthetic labelled modes, not a measured, multi-turbine corpus, so its class probabilities are indicative rather than calibrated against measured data. Constructing the classifier — directly, or through the trainer, which builds one — emits an ExperimentalWarning; the ensemble fuses the predictions of an already-constructed classifier. Do not rely on the learned label alone for engineering decisions; the rule-based physical label and the deterministic taxonomy are the primary identification path. The PyTorch-based sequence/graph trackers are not part of the supported core. See the model card (Model card — default mode classifier) for the default model’s training data, intended use, and metrics.

Scope

VANE post-processes linearization output; it does not run OpenFAST, set up models, or compute spatial (geometry-resolved) mode shapes. Visualisation renders modal participation and complex phasors, not deflected turbine geometry.