A Finite Element Model of the External Corrosion of Buried Pipeline Steel under the Combined Influence of Heat Transfer, Cathodic Protection, and Oxygen Diffusion in Surrounding Soil
Bibliographic record
Abstract
Abstract This work presents a numerical model of the coupled interactions between temperature profile, electrolytic potential drop, and steady-state oxygen concentration gradient in soils surrounding buried pipelines. Three different soil types are considered (sand, clay, and peat), with porosity ratios varying between 0.4 and 0.8. Two volumetric wetness ratios are simulated for each soil type, representing moisture changes during successive soil drying-wetting cycles. The motivation behind this study is to model the interdependencies of heat transfer, cathodic protection, and oxygen diffusion on pipeline steel corrosion in various soil environments. A key benefit of the developed model is its rapid scalability, allowing the simulation of these interrelated phenomena for different geometries, dimensions, and boundary/initial conditions. The results of a select number of cases are presented in this paper. Based on the oxygen diffusion, cathodic protection, and iron oxidation behavior of an exposed 90° arc on the pipeline’s external surface facing a magnesium cathodic protection anode, it is found that drier sand and clay soil structures cause the most corrosion. The geometric location of the coating holiday relative to the ground surface and the cathodic protection anode has a particular influence on oxygen concentration and iron oxidation. Temperature fluctuations during seasonal weather cycles have observable effects on iron oxidation rates due to influences on heat transfer and oxygen diffusivity. An overall trend of decreased oxygen concentration and iron oxidation in wetter and warmer soils is detected and quantified.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".