Multiple Tmds On Motion Reduction Of Floating Offshore Wind Turbines
Bibliographic record
Abstract
This study explores the damping effects of the Tuned Mass Damper (TMD) on a floating offshore wind turbine (FOWT).The environmental conditions refer to the IEC-61400-3 Design Load Case.The DLC 1.2 and DLC 6.2 were used in the study.Turbulent wind field simulation was performed by TurbSim, and the load of wind waves on structures was generated by FAST which was developed by NREL.The TMD is located at the proper places of the supported tower to see the motion reduction effects.The best mass ratio and damping ratio were tested.Various TMDs have different motion reduction effect of FOWT when it is under wind, wave and seismic load separately.The TMD motion reduction effect of FOWT under coupled wind and wave loads were also studied.In a DLC 6.2 environmental loads, the offshore wind turbines stopped operating due to extreme environmental conditions.The average extreme wind speed in the 10-minute 50-year regression period was 54.16 m / s, and the significant wave height and period in the 50-year regression period were 8.24 m and 12.01 s. Figure 25 shows the fore-aft response of FOWT without TMD, with one TMD and three TMDs.It can be seen that the motion reduction is more significant that that of FOWT under DLC 1.2 loads.Simliar results can be seen in Figure 26.As before, there are no clear improvement when the number of TMD is increase to 3. The best motion reduction occurred at the second beating response.In the first beating, the motion response of FOWT with 1 TMD is even worse than that of FOWT without TMD, whereas the problem may be solved to some extent when 3-TMDs is installed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".