Dynamic Modeling and Simulation of a Spar Floating Offshore Wind Turbine With Consideration of the Rotor Speed Variations
Bibliographic record
Abstract
This paper presents a rigid multibody dynamic model to simulate the dynamic response of a spar floating offshore wind turbine (FOWT). The system consists of a spar floating platform, the moorings, the wind turbine tower, nacelle, and the rotor. The spar platform is modeled as a six degrees-of-freedom (6DOFs) rigid body subject to buoyancy, hydrodynamic and moorings loads. The wind turbine tower supports rigid nacelle and rotor at the tip. The rigid rotor is modeled as a disk spinning around its axis and subject to the aerodynamic load. The generator torque control law is incorporated into the system dynamics to capture the rotor spinning speed response when the turbine is operating below the rated wind speed. The equations of motions are derived using Lagrange's equation in terms of the platform quasi-coordinates and rotor spin speed. The external loads due to hydrostatics, hydrodynamics, and aerodynamics are formulated and incorporated into the equations of motion. The dynamic simulations of the spar FOWT are performed for three load cases to examine the system eigen frequencies, free decay response, and response to a combined wave and wind load. The results obtained from the present model are validated against their counterparts obtained from other simulation tools, namely, FAST, HAWC2, and Bladed, with excellent agreement. Finally, the influence of the rotor gyroscopic moment on the system dynamics is investigated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".