FATIGUE CHARACTERIZATION OF RISERS AND PIPELINES UNDER REALISTIC VARIABLE AMPLITUDE LOADING AND THE INFLUENCE OF COMPRESSIVE STRESS CYCLES
Bibliographic record
Abstract
One of the most prominent factors affecting the performance and longevity of risers is vortex induced vibration (VIV), which can cause severe fatigue damage, especially in risers used in deep waters. The available approaches for analyzing VIV induced fatigue in risers mainly focus on the VIV aspect of the problem; indeed less attention has been paid on the effect of VIV on a riser’s fatigue life and in prediction of fatigue life using various models.\nThis dissertation first demonstrates how one can characterize fatigue of pipes and risers using an equivalent plate specimen as opposed to using a pipe specimen, thereby simplifying the task, yet obtaining good accuracy. Actual variable amplitude loadings (VAL) are used to study the fatigue crack growth in risers’ material with a focus on the various influencing parameters. Extensive experimental investigations are performed, followed by analytical and computational nonlinear finite element analyses. It is shown that the higher harmonics do cause significant fatigue damage, thus their influence should not be ignored. The influence of load interaction effects is also investigated, focusing on the fatigue crack growth retardation effects due to tension overloads, as well as the acceleration effects due to compression underloads. The crack closure concept is then used to explore into both the fatigue retardation and acceleration effects within a VAL scenario. An effective method for calculation of the stress intensity factor is proposed, which considers only the tensile portion of the stress range, while proposing another effective approach for accounting for the influence of compressive stress cycles.\nMoreover, a two-parameter approach is used in this dissertation, relating the fatigue crack growth rate (FCGR) to the crack tip opening displacement (CTOD). It is shown that the CTOD provides adequate information for calculating the FCGR under VAL, and it can be effectively used to account for the influence of the compressive stress cycles. The experimental investigation also considers the retardation effect resulting from the applied peak tensile overload cycles (TOLC) and the influence of various so-called “clipping” levels, demonstrating the significant influence of the TOLC on crack growth retardation in VAL.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".