Floating wind turbines: a geophysics-derived method to make anchoring calculations more reliable

The pre-registration of explicit, falsifiable predictions with predefined GO/NO-GO/PIVOT criteria is exemplary. This structure forces a clear decision pathway and mitigates confirmation bias, a rare and commendable practice in computational mechanics.

The formulation is remarkably clear and operationalised: the success criteria (probability coverage [0.94, 0.99], bias reduction ≥ 25%) are quantified and testable, which is rare and valuable in the UQ literature. This pre-specification imposes an epistemic rigour that calibration studies often lack.

The explicit separation of aleatory (bootstrap) and epistemic (initial ensemble) uncertainty is methodologically principled and represents a genuine improvement over monolithic UQ approaches that conflate the two.

The explicit separation of aleatory (bootstrap) and epistemic (geostatistical ensemble) uncertainty directly addresses a critical pain point for floating offshore wind (FOW) developers: the current industry practice of using single best-fit calibration with safety factors leads either to over-engineered mooring systems (cost overruns of 10–15% on CAPEX) or to under-predicted fatigue loads (warranty and insurance risks). The target of a 25% bias reduction on the 99th percentile mooring load translates into a tangible reduction in mooring line cross-section or chain grade, which is a direct cost driver.

Methodological originality: the transfer of a hybrid geophysical UQ framework (block bootstrap + geostatistical initialisation + homotopy) to the calibration of reduced-order models for floating wind is a genuine novelty, not covered by conventional calls on digital twins.