Electromagnetic Induction Post Heating to Reduce NDE Delay Times of Welding In-Service Repairs
Bibliographic record
Abstract
Abstract When a pipeline requires a repair, a pressure-containing steel sleeve or an emergency repair fitting is often fillet welded to the in-service pipe to return the pipeline to normal service conditions. During welding, the flowing product rapidly quenches the fillet weld, promoting the formation of high hardness and low ductility microstructures in the heat-affected zone. The rapid cooling rates also limit the mobility of diffusible hydrogen introduced from the welding electrodes. The hydrogen can be trapped in the weld metal and heat-affected zone and concentrated in specific locations throughout the weld based on the welding deposition sequence. Fillet welds also contain inherent locations of geometric stress concentration at the weld toes and root locations. The elevated hydrogen concentration in the in-service weld, combined with the geometrical stress concentrations at the location of crack-susceptible microstructures, can increase the likelihood of forming a hydrogen-induced crack. Delayed non-destructive examination (NDE) is often employed to wait a sufficient time for any cracks to form so they can be detected. To reduce hydrogen concentration at the locations of stress concentration and NDE delay times, post-heating can be applied to the in-service weld. Elevating the temperature within the weld can enable hydrogen diffusion and reducing the cracking propensity. The rapid heat removal due to flowing product requires post-heating techniques with high energy outputs that will not overheat the steel surfaces. Electromagnetic induced current (induction heating) methods can produce sufficient thermal energy in the electrically conductive steel pipe and sleeve. Coupled numerical finite element analysis (FEA) models were utilized to simulate various induction cable arrangements and thermal convection coefficients, representative of various pipeline products. The analysis of the induction heating arrangements for the studied thermal convection coefficient was conducted to achieve a minimum temperature of 120 °C in the fillet weld root and toes to enable sufficient thermal driving force for hydrogen diffusion while ensuring the pipe and sleeve surface temperature does not exceed 200 °C. An optimal induction heating procedure was found to which could achieve the target temperatures within a reasonable heating time such that NDE delay times of in-service welds can be reduced by 5–6 times.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".