Recuperación de palas de un aerogenerador en la ría de Viveiro
Bibliographic record
Abstract
RESUMEN:La presente Memoria corresponde a la operación llevada a cabo por el buque de salvamento marítimo Sar Gavia y buque Lateromar. Dicha operación se realizó en aguas de la ría de Viveiro (Lugo-Galicia) en ella se lograron reflotar y llevar a tierra palas de aerogenerador que había perdido el buque mercante BBC Ontario en un día de condiciones adversas. El estudio detalla las operaciones llevadas a cabo por ambos buques con medios de posicionamiento pasivo diferentes y explora otras posibles maniobras que se podrían llevar a cabo utilizando un buque de mayor porte y/o dotado de sistema de posicionamiento dinámico. Dado que la cubierta del B/S Sar Gavia tiene aproximadamente 11 metros lineales de cubierta y su grúa tiene una capacidad máxima de 12 toneladas, todo ello imposibilitaría el poder cargarlas a bordo, y el Lateromar de menor porte que el Sar Gavia se encuentra más limitado. \nAdemás se abordan las nuevas tecnologías que existen para realizar sondeos y encontrar objetos en el fondo del mar. \nDentro de dicho estudio se hace especial hincapié en los métodos de remolque, reflotamiento de objetos, tipos de sonares y buques especiales que se puedan utilizar en este tipo de operaciones y la tecnología de posicionamiento dinámico de buques. \nFinalmente se extraen las conclusiones de este estudio que pueden ser de utilidad en una operación similar que pueda darse en futuros trabajos. \nABSTRACT:This report corresponds to the operation carried out by the maritime rescue vessel Sar Gavia and the Lateromar vessel. This operation was performed in waters of the Viveiro estuary (Lugo-Galicia), where the wind turbine blades that the merchant ship BBC Ontario had losted in a day of adverse conditions , they were refloated and brought to land. The study details of the operations carried out by both ships with different passive positioning means and explores other possible maneuvers that could be executed using a bigger vessel and / or equipped with a dynamic positioning system. Since the deck of the B / S Sar Gavia has approximately 11 linear meters and its crane has a maximum capacity of 12 tons, all this would make it impossible to load them on board, and the Lateromar with a smaller size than the Sar Gavia is more limited. \nIn addition, the new technologies that exist to conduct surveys and find objects on the sea floor are addressed. \nWithin this study, special emphasis is placed on the methods of towing, refloating objects, sonar types and special vessels that can be used in this type of operations and the dynamic positioning technology of ships. \nFinally, the conclusions of this study that may be useful in a similar operation that may occur in future work are extracted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".