FLUID–STRUCTURE-INTERACTION ANALYSIS FOR WELDED PIPES WITH FLOW-ACCELERATED CORROSION WALL THINNING
Bibliographic record
Abstract
The flow-accelerated corrosion (FAC) entrance effect results in enhanced wall thinning immediately downstream of a weld if the weld connects an upstream FAC-resistant material with a downstream less resistant material. The weld regions, especially those with local repairs, are susceptible to cracking due to the high residual stresses induced by fabrication. The combined effects of the FAC entrance effect and high stresses at a weld might compromise the structural integrity of the piping and lead to a failure. Weld degradation by FAC entrance effect has been observed at nuclear and fossil power plants. This paper describes an application using fluid–structure-interaction (FSI) modelling to study the combined effects of FAC wall thinning, weld residual stresses, and in-service loads on welded structures. Simplified cases analyzed were based on CANDU outlet feeder conditions. The analysis includes the flow and mass transfer modelling of the FAC entrance effect using computational fluid dynamics (CFD) and nonlinear structural analyses of the welded structures with wall thinning and an assumed weld residual stress and strain distribution. The FSI analyses were performed using ANSYS Workbench, an integrated platform that enables the coupling of CFD and structural analysis solutions. The obtained results show that the combination of FAC, weld residual stresses, in-service loads (including the internal pressure) and (or) extreme loads could cause high stresses and affect the integrity of the welded pipes. The present work demonstrated that the FSI modelling can be used as an effective approach to assess the integrity of welded structures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".