Concept design optimization for OSVs operating on the Flemish Pass basin
Bibliographic record
Abstract
Significant oil discoveries in the Flemish Pass Basin off the coast of Newfoundland and Labrador have given rise to interest in studying the capabilities of offshore support vessels (OSVs) to provide logistics support to an operating offshore oil installation. The Flemish Pass Basin represents a departure from current operational environments, characterized by longer distances, deeper waters, and a harsher metocean climate, which raises concerns as to the suitability of existing vessel design configurations. As a result, this study has been performed into the optimization of the design of offshore support vessels, to develop a high-level optimized concept design which can support oil and gas development on the Flemish Pass Basin. First, a high-level review of existing approaches to optimizing the design of OSVs and their logistics was examined. These approaches, though powerful for their individual optimization goals, failed to tie all the optimization requirements and logistics together into a holistic understanding of the most efficient design of hull and fleet to meet the operational requirements. The works presented in this paper show the process used to tie these approaches together into a complete optimization algorithm, to develop a fit for purpose fleet of OSVs. To support this algorithm, a series of computer simulations were performed to develop sets of equations to describe the seakeeping, resistance, and stability performance of OSVs. These simulations were performed on 4 principal hull designs: axe bow, bulbous bow, vertical bow, and X bow, which are representative of many state-of-the-art vessels currently operating. Using the derived equations, a computerized algorithm was developed which takes account of sea state probabilities and operational requirements to relate the vessel performance, downtime, scheduling, and design to minimize fleet annual cost. The algorithm automatically rejects any hull or fleet mix designs which cannot achieve the required delivery performance, and any which due to their excessive speed, weights, or lack of stability could not be operated. A result of running this algorithm showed an optimized OSV design consisting of a fleet of 2 vessels based on the vertical bow hull form, with 100 m length, 25 m beam, and a 4 m draft. In general, the results showed a strong cost efficiency of using large, low displacement hulls and fewer voyages. The sensitivity of the results was studied, and it was found that the algorithm output can significantly vary with little change on the input. Further, the algorithm has a margin of error which can impact which vessel is ultimately recommended, which indicates a need for more detailed studies beyond the concept design phase, to ensure that the optimum design has been selected.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".