Design of Pressure Hulls Using Nonlinear Finite Element Analysis
Bibliographic record
Abstract
Through the use of nonlinear finite element analysis (NLFEA), submarine pressure hull designs could potentially be based on calculated limit states that include the full geometric complexity of the structure, and real-world effects such as build imperfections. In addition, NLFEA could provide a rational means of assessing the effects of in-service damage on structural performance. Analysis of pressure hulls using 3D NLFEA is not currently supported in design codes, primarily because the uncertainty regarding the accuracy of the method has not been quantified. Defence Research and Development Canada (DRDC), the R&D branch of the Canadian Navy, is undertaking work to develop a partial safety factor for 3D NLFEA of pressure hulls, by comparison of numerically calculated collapse pressures to experimental results. Data from experiments previously conducted at various institutions will be augmented by a pressure hull testing program currently being undertaken by a joint project of DRDC and the Ministry of Defence of the Netherlands. The development of NLFEA modeling and analysis guidelines, as well as a revised design/analysis procedure, will be discussed, as well as a history of the DRDC submarine structures research program.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".