The Effect of Ovality and Thickness Variations on Stress Analysis of Tube Bends Under Internal Pressure
Bibliographic record
Abstract
Corrosion fatigue damage has resulted in the catastrophic failure of several riser and supply tubes in fossil boilers operating under sub-critical conditions. Most of the damage has been found on the neutral axis of tube bends and the damage mechanism was identified as corrosion fatigue. Significant stress concentrations will always be found associated with high tube ovality and significant thickness variations. The purpose of this study is to demonstrate the effect tube bend geometry on the stress distributions developed due to the internal pressure. The work was performed to consider how the stresses present in tube bends vary with ovality and thickness variations (thinning/thickening). Ovality and thickness variations in tube bends, which are generally introduced during the bending process, were modeled into the three-dimensional finite element models (FEM). The finite element models considered five different degrees of ovalization of the cross-section at the center of the bend. For each model the maximum principal stress values and distribution of stresses within tube bends were evaluated. The finite element analysis (FEA) predictions were compared with the actual locations of corrosion damage on the tube bend and reasonably predicted where cracks should be expected to initiate and propagate, and where cracks should not be anticipated.
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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 | 0.000 | 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) | 0.001 | 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".