Finite Element Modeling of Buried Longitudinally Welded Large-Diameter Oil Pipelines Subject to Fatigue
Bibliographic record
Abstract
The design and construction of large-diameter buried pipelines primarily for crude oil transportation is governed in Canada by CSA Z662, ASME B31.4, and ASME BPVC Section VIII. Although these codes provide general guidelines on pipeline design, many aspects of modelling the pipeline are not given in detail, and the results can vary significantly based on how these details are modelled. Engineers often adopt a very conservative approach and this results in pipelines that are over-designed and therefore unnecessarily costly. Following the design code, this thesis provides a detailed fatigue analysis (FA) of a large-diameter buried liquid pipeline and incorporates the effects of the stress concentrations associated with manufacturing defects and tolerances. A stress analysis of the pipe is first performed using the finite element method (FEM), and results obtained are used in conjunction with both elastic and elastic-plastic FA life assessment models to predict fatigue damage (FD). The results of a FEM and FA performed on four standard pipeline OD’s show that a 20% increase in the outside diameter (OD) to wall thickness (WT) ratio can be achieved when plasticity is considered. This is equivalent to one to two increments of standard WT or the percent reduction of a pipeline construction cost. In the analyses process, where the code leaves significant room for interpretation, this thesis provides clarity on appropriate procedures to follow. Examples include how to accurately model the weld profile, and the misalignments due to the manufacturing process. Furthermore, a simple calculation tool is developed that can be used to approximate hot-spot elastic stresses.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.001 |
| 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".