Application of Load Sequence to Control Crack Growth in Steel Pipelines Under Near Neutral pH SCC
Bibliographic record
Abstract
Pipelines are designed to operate below a maximum operating pressure in service. However, there are pressure fluctuations during operation. The presence of pressure fluctuations creates a drive for crack growth in steel pipes. In order to prevent catastrophic failure of pipelines, there is need for better understanding of the contribution of pressure fluctuations to crack growth rate in steel pipelines. Analysis of pressure fluctuation data in oil and gas pipelines shows that there are different types of fluctuations in a pipe due to friction loss with distance from the pump or compressor station. All these fluctuation types show a form of variable amplitude loading classified in this research as underload, mean load and overload. Studies of some structural systems shows that underload can cause acceleration of crack growth while retardation of crack growth is observed after an overload. This research aims to apply pressure fluctuations to manage integrity of steel pipelines through a novel approach of load sequence involving underload and overload in near neutral pH environment. Clear knowledge of the effect of load interaction involving load sequence of underload and overload is vital to control crack growth in steel pipelines under near neutral pH environment. The result of crack growth rate under different load sequence on X65 steel indicate that increase in overload ratio of 2, 3 and 4 caused an increase in crack growth rate of 1.68E−3, 1.89E−3 and 2.31E−3 mm/block respectively. These results are compared with results from other tests under variable amplitude without load sequence. Analyses were carried out on the morphology of the crack tip and the fracture surface after the test.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".