Preliminary Stress Corrosion Cracking Modeling Study of a Dissimilar Material Weld of Alloy (Inconel) 182 with Stainless Steel 316 in Pressurized Water Nuclear Reactor
Bibliographic record
Abstract
Dissimilar welds (DW) are normally used in many components junctions in structural project of PWR (Pressurized Water Reactors) in Nuclear Plants. One had been departed of a DW of a nozzle located at a Reactor Pressure Vessel (RPV) of a PWR reactor, that joins the structural vessel material with an A316 stainless steel safe end. This weld is basically done with Inconel/Alloy 182 with a weld buttering of Inconel/Alloy 82. It had been prepared some axial cylindrical specimens retired from the Alloy 182/A316 weld end to be tested in the slow strain rate test machine located at CDTN laboratory. Based in these stress corrosion susceptibility results, it was done a preliminary semi-empiric modeling application to study the failure initiation time evolution of these specimens. The used model is composed by a deterministic part, and a probabilistic part according to the Weibull distribution. It had been constructed a specific Microsoft Excel worksheet to do the model application of input data. The obtained results had been discussed according with literature and also the model application limits.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".