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Record W4409485768 · doi:10.5006/c2023-19280

Innovative Mitigation Strategies to Address AC Corrosion at Higher Frequencies

2023· article· en· W4409485768 on OpenAlexaff
Wolfgang Fieltsch, J.A. Farquharson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsImperial Oil (Canada)Stantec (Canada)Cochrane
Fundersnot available
KeywordsCorrosionComputer scienceEnvironmental scienceMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Abstract A 2018 field investigation on an NPS 6, approximately 4 km long, liquid pipeline identified a rectifier as the likely source of the elevated DC and AC current densities, which resulted in AC corrosion anomalies detected during in-line inspections (ILI). Initial findings indicated that the 120 Hz rectifier ripple may have contributed to the accelerated AC corrosion at this location. A literature review identified a gap in the industry with no existing standards that address AC corrosion at these higher frequencies, and very little research on the topic. This resulted in additional field and laboratory investigations to further quantify the risks of higher frequency harmonics on cathodically protected pipelines. Due to the complexity of the system, innovative mitigation approaches were required to mitigate the AC corrosion risk and minimize DC interference risks, while at the same time ensuring adequate CP is maintained along the pipeline. A comprehensive mitigation and monitoring system was installed and commissioned. This paper focuses on the design and implementation of the mitigation system, along with a full assessment of the system commissioning and monitoring data. Lessons learned during the mitigation and commissioning process are intended to provide guidance to operators encountering similar conditions on their pipeline systems.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.279
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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