Preprints
https://doi.org/10.5194/wes-2026-204
https://doi.org/10.5194/wes-2026-204
06 Nov 2026
 | 06 Nov 2026
Status: this preprint is currently under review for the journal WES.

An Engineering-Level Vortex Induced Vibration Methodology for Parked-Load Assessment

Kevin R. Moore, Alex Frye, Nathaniel de Velder, Ian D. Brownstein, Benjamin Strom, and Brent J. Summerville

Abstract. Vortex-induced vibration (VIV) can increase cyclic loading of stationary and slowly moving bluff bodies. Assessing parked wind-turbine loads across winds and blade orientations requires design sweeps that are costly with blade-resolved fluid–structure simulations. We couple QBlade's native aeroelastic response to fixed-body lift and drag spectra through a calibrated, finite-amplitude response law. Sectional data set the coefficients for an H-rotor vertical-axis wind turbine (H-VAWT); separate blade studies train the same law and test a response map on an excluded tip geometry. A turbulent-inflow H-VAWT example compares native QBlade, Spectral VorLap, and Hybrid VorLap under prescribed slow idling. Hybrid VorLap increases the conditional edgewise damage-equivalent load by a factor of 2.63, but flapwise bending still controls the combined longitudinal stress. Comparison with approximate normal operation shows that fatigue ranking depends on wind exposure and the assumed high-cycle material law. Extreme-wind longitudinal stress changes little at the monitored sections. The method supports selection of candidate VIV load cases for detailed analysis, but requires application-specific spectral and response data for quantitative use.

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Kevin R. Moore, Alex Frye, Nathaniel de Velder, Ian D. Brownstein, Benjamin Strom, and Brent J. Summerville

Status: open (until 04 Dec 2026)

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Kevin R. Moore, Alex Frye, Nathaniel de Velder, Ian D. Brownstein, Benjamin Strom, and Brent J. Summerville
Kevin R. Moore, Alex Frye, Nathaniel de Velder, Ian D. Brownstein, Benjamin Strom, and Brent J. Summerville

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Short summary
Detailed simulations of wind-driven blade vibrations often require supercomputers. We developed a workstation-compatible alternative using precomputed airflow data and a calibrated model of blade motion. In a parked wind turbine study, it predicted 2.63 times higher repeated bending loads in one direction which could contribute to premature failure from fatigue. This approach can help engineers to identify potentially damaging wind conditions without repeating expensive simulations.
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