Efficiency, safety, and reliability analysis of turbocharging in a large container vessel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Commercial shipping is currently dominated by mega container vessels. The shipping industry has seen a 10‐fold increase in the size of containers over the last four decades. These vessels are propelled by large marine diesel engines, hereafter referred to as the main engine. The performance of the main engine is determined by its subsystems. An important part of the main engine is the turbocharging system, which contributes to its safety, efficiency, and reliability. In this study, the effectiveness and reliability of the turbocharging system are evaluated. The Australian Maritime College has a Kongsberg Engine Simulator that can produce a variety of malfunctions on a running engine's turbocharging system. Analyzing the results obtained from the simulator determines the efficiency of the turbochargers. The study will provide recommendations for improving the safety of the turbocharging system for better performance to be achieved by the turbochargers, leading to an improvement in the main engine's performance. Last, the reliability of the turbocharging system is evaluated quantitatively using a fault tree analysis and reliability block diagrams. This will enable an optimum maintenance strategy to be established to ensure the safe operation of the vessel.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.004 | 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