Mitigating Failures in Ferritic-Austenitic Dissimilar Metal Joints in Petrochemical Industry
Bibliographic record
Abstract
There are several failure mechanisms that might affect ferritic-austenitic dissimilar metal welds (DMWs) in petrochemical plants and refineries. Examples are cracking due to creep, stress corrosion cracking (SCC), sulphide SSC, thermal fatigue, brittle fracture, pitting corrosion, and hydrogen embrittlement. Of these, creep, SCC, and hydrogen embrittlement are perhaps of greater interest. Industry has many lessons learned; however, still experiences high consequence failures. This work describes the most common failure mechanisms in dissimilar ferritic-austenitic welds and summarizes a guidance to prepare welding procedures and reduce the likelihood of failures. This guidance is based on a literature review and industry experience. The metallurgical characteristics of the damage observed in both service and laboratory test samples indicate that creep rupture is the dominant failure mode for Dissimilar Metal Welds (DMW) in some high temperature service conditions. However, it has also been observed that temperature cycling contributes significantly to damage and can cause failure even when primary stress levels are relatively low. Therefore, a creep-fatigue assessment procedure is required as part of a remaining life calculation. API 579-1/ASME FFS-1 2007 Fitness-For-Service standard includes a compendium of consensus methods for reliable assessment of the structural integrity of equipment containing identified flaws or damage. Part 10 of API 579-1 includes a method for protection against failure from creep-fatigue. In the assessment of DMW, a creep-fatigue interaction equation is provided to evaluate damage caused by thermal mismatch, sustained primary stresses, and cyclic secondary loads [Ref.1]. Failures due to hydrogen embrittlement cracking (HEC) mechanisms are not uncommon and are also described in this paper [Ref. 2]. Finally, a case history of a DMW failure in a steam methane furnace, which is common in the petrochemical industry, is described and shown as an example of a failure mitigation approach.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".