Hull Structural Performance Monitoring System for Ships Operating
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Bibliographic record
Abstract
Abstract Research on measuring the ice impact pressure on icebreaker hulls began inthe late 1970's, and its focus was to determine the magnitude of the impactpressures and to obtain long-term statistics of the impacts. Increasedcomputing power in the 1980's allowed the recording of time-histories onmultiple sensors that led to the development of the pressure-contact arearelationship. The aim of these systems, however, was to understand theice impact process and to provide guidance to design engineers. Thispaper presents a new hull structure monitoring system that can benefit both theship designers and operators for ships operating in ice-covered waters. With this system, the ice load monitoring system can measure and process theice impact loads immediately after each impact in near real-time. Theimpact measurements are used to estimate the resulting stresses on the hullstructures which are then compared to the allowable stresses. This systemcan provide meaningful near real-time feedback to the ship's crew of thestresses due to ice impact compared to the allowable stress. Thisinformation can assist the ship's crew in making informed decisions for safeand efficient operations in ice. The main focus of this paper is on themethodology for assessing the hull structural responses under ice impact andthe presentation of this information to the ship's crew. Introduction The state-of-the-art ice load monitoring and alarm system (ILMS) has beeninstalled on the Arctic shuttle tanker Timofey Guzhenko. The workassociated with the development, design, installation of the system, datacollection and analysis is a joint venture between ABS, ConocoPhillips, andSamsung Heavy Industries (SHI) with vessel owner cooperation [1]. This systemis designed to measure and record ice pressures and loads as well as computeice-induced responses of the hull structure at highly loaded locations on thehull. Stresses resulting from the ice loads are compared with allowablestresses in near real-time and the margin of safety in the structure isdisplayed on a bridge monitor. The system was commissioned in April 2009with the intent of long-term unmanned operation.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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 it