A Procedure to Evaluate a B31.1 Welding End Transition Joint to Include the Fatigue Effects of Thermal Gradients for Design and Plant Life Extension
Bibliographic record
Abstract
Fatigue evaluation in B31.1 is currently done based on Equation 1 & 2 [B31.1-2007] which considers only displacement load ranges. Yet, fatigue damage is also occurring due to pressure cycling and thermal gradients. To exacerbate this, power plant design pressures and temperatures are rising, new materials are being introduced, pipes and attached components are become increasingly thick, and owners are requiring the power plants to heat-up and cool-down at faster rates. Also, power plant owners are more and more interested in extending the life of power plants beyond their original design life. Although the knowledge of thermal gradients has been available for many years, no attempt has been made to incorporate this into the B31.1 Code. This paper takes the first step in addressing this pressing need in today’s power plant environment. Granted there are several configurations where the effects of thermal gradients could be assessed. As the first step, this paper provides a procedure to evaluate the fatigue effects of thermal gradients in the prevalent Welding End Transition Joint (ASME B31.1 Fig 127.4.2) based on thermal gradients calculated as per [PVP2009-77147]. The disadvantage of this approach is that the conservatism in the calculation thermal gradients inherent in ASME Section III Sub-section NB-3600-2007 is inherent in these calculations as well, and may produce unacceptable results. If the results turn out to be unacceptable, it is a warning that something else needs to be done in the way of either monitoring or modifying or further evaluation. The advantage of this methodology is that it maintains the traditional B31.1 approach to fatigue by controlling SE with the same limit of SA except that there is now an additional term, f ‘, to account for the fatigue effects due to thermal gradients. In addition, it eliminates the need for a computer program to calculate this additional term, a computer program that is not normally accessible to the B31.1 designer anyway. Considering the fatigue effects of thermal gradients in this way will further help to preserve the integrity of the piping pressure boundary and consequently, the safety of personnel in today’s power plants and into the future.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".