Measurement and Modeling of the Motions of a High-Speed Catamaran in Waves
Bibliographic record
Abstract
Wetdeck slamming can be defined as a large vertical acceleration event that occurs when ship motions cause an impact between the cross deck and the ocean’s surface. The use of Computational Fluid Dynamics (CFD) and other simulation tools to accurately predict wetdeck slamming loads and ship motions has become the objective of a number of efforts (Hess, et al, 2007; Lin, et al, 2007; Faller et al, 2008; for example). The Sea Fighter, FSF-1, is a high-speed research vessel developed by the U.S. Office of Naval Research (ONR). Christened in 2005, she is an aluminum catamaran propelled by four steerable water jets capable of speeds up to 50 knots. In 2006, Sea Fighter underwent a series of rough water trials to assess its operational profile in high sea states (Fu, et. al., 2007). Along with this assessment, ONR sponsored an effort to obtain full-scale qualitative and quantitative wave slamming and ship motion data. One of these rough water trials took place 18–20 April 2006 as the ship transited from Esquimalt, British Columbia, Canada to San Diego, California, USA. During this trial, the significant wave height ranged from 1.5 to 2.7 m and the ship speed ranged from 20 to 40 knots. This paper describes the results of the effort to characterize the Sea Fighter’s motion in waves. To provide suitable full-scale validation data, the incoming ambient waves had to be characterized. A Light Detecting and Ranging, (LiDAR) system, an array of ultrasonic distance sensors, and several video cameras were used to characterize the incoming wave field. In addition, three fiber optic gyro motion units were deployed to record ship motions. Additionally, a GPS unit was utilized to measure ship speed, pitch, roll, and heading. Several slam and near slam events are discussed over the range of ship’s speed, heading, and sea states tested. Similarities and differences between these events are also noted. Additionally, this data was used to develop a simulation of the Sea Fighter’s motion in waves similar to previous work done utilizing model test data (Hess, et al, 2007; Faller et al, 2008).
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".