Estimation of Flow-Accelerated Corrosion Rate in Nuclear Piping System
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 Flow-accelerated corrosion (FAC) is a life-limiting factor for the piping network of the primary heat transport system (PHTS) in CANDU® reactors. The pipe wall thinning caused by FAC is monitored by carrying out periodic in-service inspections (ISI) to ensure the fitness-for-service of the piping system. Accurate prediction of the lifetime of various components in the PHTS piping network requires estimation of FAC thinning rate. The traditional Bayesian inference techniques commonly employed for parameter estimation are computationally costly. This paper presents an inexpensive and intuitive simulation-based Bayesian approach to FAC rate estimation, called approximate Bayesian computation using Markov chain Monte Carlo (ABC-MCMC). ABC-MCMC is a likelihood-free Bayesian computation scheme that generates samples directly from an approximate posterior distribution by simulating data sets from a forward model. The efficiency of ABC-MCMC is demonstrated by presenting a comparison with a likelihood-based Bayesian computation scheme, Metropolis-Hastings (MH) algorithm, using a practical data-based example. Furthermore, an innovative step has been proposed for reducing the Markov chain burn-in time in the proposed scheme. To indicate the need of a Bayesian approach in quantifying the uncertainties related to the FAC model parameters, results from the linear regression method, a common industrial approach, are also presented in this study. The numerical results show a notable reduction in computational time, suggesting that ABC-MCMC is an efficient alternative to the traditional Bayesian inference methods, specifically for handling noisy degradation data.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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