Validating Impressed Current Cathodic Protection Numerical Modelling Results Using Physical Scale Modelling Data
Bibliographic record
Abstract
Abstract Physical scale modelling (PSM) is an experimental technology that has been used to evaluate and design shipboard impressed current cathodic protection (ICCP) systems. PSM is also a preferred tool used for validating numerical modelling results owing to the well-controlled conditions in PSM experiments. However, one issue in using PSM for the validation of numerical modelling results is the lack of information on the actual polarization behavior of the cathodes on a model hull during PSM experiments. Consequently, the polarization curve data used as boundary conditions in numerical modeling trials is usually different from the polarization behavior of the cathodes in PSM experiments. This difference can result in a discrepancy between the numerical modelling and the PSM results that is difficult to separate from other numerical errors. A discrete area current control (DACC) technique was developed in a previous ICCP PSM study to simulate the polarization behavior of a propeller material under various conditions. The present study extended the DACC technique to simulating the polarization curve behavior of multiple discrete cathodes on a model hull. The application of the DACC technique also made it possible for both the PSM and numerical modelling trials to use the same sets of polarization curve data as inputs or as boundaries in the validation studies. This paper demonstrates the use of the DACC technique to simulate the polarization behaviors of a propeller material and three paint damage patches. The PSM results obtained under different polarization behaviors of propeller and hull materials are also used to validate the numerical modelling results obtained under the same polarization conditions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".