Application of Finite Element Analysis in Subsea Pipeline Integrity Assessment
Bibliographic record
Abstract
Abstract Subsea pipelines carrying high temperature and high pressure hydrocarbon fluids are prone to failure due to global buckling. The design of such pipeline should essentially consider the effect of seabed profile (flat or uneven), types of soil on the mudline (sand or clay) and pipeline route layout. To check pipeline integrity a two step approach has been applied. In the first step, buckling susceptibility is estimated based on empirical formulation provided in research papers and code. In the next step, the buckle prone pipeline has been studied through a rigorous approach, where Finite Element Analysis tool is utilized to simulate the actual in-place scenario i.e. considering the pipeline as installed on the uneven sea bed and the effect of pipe soil interaction. It is observed that the assumptions applied in empirical formulation result in over conservative design. Such designs have a cascading impact on the material requirement, offshore construction time and overall project cost. The FEA approach is effectively utilised to have an optimized boundary condition i.e. by allowing actual seabed modelling based on bathymetry data at close intervals, pipeline layout and a refined pipe soil contact modelling. This paper focuses on the application of research work and code guideline for determination of susceptibility of high pressure high temperature submarine pipeline to lateral buckling and application of Finite Element Analysis (FEA) methods to assess the integrity of the pipeline system for the maximum operating condition. FE Approach can be effectively utilized to simulate the actual pipeline behaviour on seabed. It also provides the necessary design inputs for designing pipeline buckle mitigation solutions.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".