An accelerometer-based approach to hull monitoring beyond the elastic regime
Bibliographic record
Abstract
With the efficient shipping route offered by the Northwest Passage, and its rapidly increasing availability to a wider range of ships in the coming years, the prevalence of ice covered waters to ships will be greatly increased. An option for hull-monitoring is explored which allows for damage detection beyond the elastic regime and deep into the plastic regime. This method involves using an accelerometer placed on the inside of the hull, and as an impact causing plastic damage is experienced, accelerometer readings are used to determine the delivered force to the hull. In this thesis a proof of concept for the suggested method is described and shown through a simplified example using finite element analysis. In this proposed method, the accelerometer allows for the structure to be examined from the point of view of the equation of motion. The proposition requires numerical integration from the acceleration data to find the displacement of the damaged area, and calibrations done in finite element analysis for both the stiffness and mass parts of the equation of motion to calculate the delivered force to the hull. The average error at which the proposed model determines force was found to be approximately 7%, which was calculated within the range of plastic flow behavior of the structure during an impact. Further development of this proposed method could have significant benefits such as increased safety for those at sea, better operational awareness of a ship’s capabilities, reduced dry-docking and inspection frequency, and the collection of realistic data from significant ice collisions at sea.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".