A More Efficient Effective Area Method Algorithm for Corrosion Assessment (Faster RSTRENG)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Corrosion is one of the major threats to the safety and structural integrity of oil and gas transmission pipelines. The corrosion threat is usually managed by regular in-line inspection (ILI). The effective area method (RSTRENG) is the most popular corrosion assessment model to convert the measured corrosion size to predicted burst pressure. Given a detailed corrosion measurement profile, the effective area method involves an iterative process to find the minimum burst pressure. As stated in ASME B31G, “for a corroded profile defined by n measurements of depth of corrosion including the end points at nominally full wall thickness, n!/2(n − 2)! iterations are required to examine all possible combinations of local metal loss with respect to surrounding remaining material”, the widely used effective area algorithm has at least an order of n-square time complexity (O(n2)). As n increases, the computation time increases nonlinearly. This paper reviewed the traditional RSTRENG algorithm first, and demonstrated that it is not necessary to always loop through all the combinations and check the corresponding burst pressure one by one. Because some combinations with shallower and shorter corrosion size are certainly not the final critical combination corresponding to the minimum burst pressure. A more efficient algorithm (Faster RSTRENG) is proposed and presented in this paper, which can reduce the algorithm computation time significantly. While the impact of efficiency of the algorithm on the integrity assessment process is insignificant if solely conducting burst pressure calculation in a deterministic way, a highly efficient algorithm is desired when assessing a large volume of corrosion anomalies by the reliability-based (probabilistic) assessment method using Monte Carlo simulation technique where millions of simulations are required. In addition, TC Energy has developed a more accurate and precise corrosion assessment model, i.e., plausible profiles (Psqr) model, published in IPC 2018 and 2020, and reviewed by industry experts through PRCI. The use of Psqr model needs to generate hundreds of profiles for one corrosion anomaly and repeatedly uses the effective area algorithm. This needs extensive computing efforts. When Psqr model was built into the probabilistic assessment framework, the computing efforts were dramatically increased. Therefore, it is of great importance to have a more efficient effective area algorithm to facilitate the probabilistic assessment using Psqr model. A case study was used to demonstrate the efficacy of the developed algorithm by comparing the computation time using different effective area algorithms. Engineers who are already familiar with RSTRENG model, might jump to the Faster RSTRENG section and Annex A directly.
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.
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) | 0.001 | 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 it