Influence of Surface Conductivity Contrasts on the Currents and Fields Induced in Buried Pipelines by Sources of Variable Frequency
Bibliographic record
Abstract
Abstract Various sources of fluctuating electromagnetic fields produce electric fields and currents in buried pipelines, which interfere with the pipeline infrastructure. Among these sources are power lines with a known single frequency (50 or 60 Hertz, Hz) and the natural geomagnetic field with variations in the frequency range from millihertz (mHz) to few Hz, which produce telluric currents in the pipelines. In this paper the analytical approach to the problem of the induction by external sources of variable frequency in an infinitely long multi-layered cylinder, representing the pipeline, is extended to include the conductivity contrast between the soil, in which pipeline is buried, and the air or sea water above it. The surface conductivity contrast influences the electromagnetic fields and currents depending on frequencies and pipeline electromagnetic characteristics. The mathematical model presented has been applied to the cases of underwater pipelines or pipelines embedded in soils with different conductivities. Modeling results show attenuation of the electric field induced in the pipeline in the case of significantly larger conductivity of upper media for AC and telluric frequencies. The developed analytical method can also be used for evaluation of ready-made software packages that deal with electromagnetic interference especially for low frequencies.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".