Accelerating heavy lift and transport design and analysis for the Polar Icebreaker
Bibliographic record
Abstract
Abstract This paper presents the development and review of shipyard heavy lift and transport design and analysis at Seaspan Vancouver Shipyard, located in the Canadian Pacific. With larger projects that require higher demands in movement in the yards, several challenges arose, such as design, process, and physical constraints. The state of design and analysis of rigging and structural analysis is discussed and compared to industry methods. Seaspan has updated its design and analysis process to create safe and efficient production operations. Case studies of common analysis techniques and challenges are also discussed. INTRODUCTION Seaspan is currently building the heaviest ship ever constructed in Canada, the CCGS Arpatuuq (also known as the Polar Icebreaker). Since the inception of the National Shipbuilding Strategy (NSS) in 2011, Seaspan has been instrumental in revitalizing Canadian shipbuilding expertise, successfully launching five ships. Notably, three of these are first-of-class vessels, and each successive ship has grown substantial in size and complexity. Building the Protecteur-Class vessel (JSS) to Arpatuuq will result in approximately a 75% increase in weight, pushing facility, planning, and engineering process limits. Figure 1 illustrates the increase in both maximum displacement and lightship weight. One of the complex challenges that comes with building a larger first-of-class vessel is in the block handling operations. This includes overhead crane lifts and turns, transportation by Self-Propelled Modular Transporter (SPMT), and by barge between works sites and within the yard facilities. While Seaspan is a world-class shipyard with state-of-the- art facilities, its location within a major metropolitan area creates challenges with space limitations. The limited space necessitates a high volume of block movement, and this presents a unique challenge for engineers, as making efficient plans is critical to maintaining the schedule and therefore the build budget. To reduce the cost of ship construction to the customer, a heavy engineering focus is on simplifying deliverables without sacrificing safety, accelerating the engineering analysis, and reducing movement and production effort. This paper aims to explore the methods Seaspan has used to overcome the challenges and streamline the design and analysis of lifts and transports by incorporating global best practices in the maritime industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".