Development of Predictive Models of Flow Induced and Localized Corrosion
Bibliographic record
Abstract
Corrosion is a serious industrial concern.According to a cost of corrosion study released in 2002, the direct cost of corrosion is approximately $276 billion dollars in the United States -approximately 3.1% of their Gross Domestic Product * .Key influences on the severity of corrosion include: metal and electrolyte composition, temperature, turbulent flow, and location of attack.In this work, mechanistic models of localized and flow influenced corrosion were constructed and these influences on corrosion were simulated.A rigourous description of mass transport is paramount for accurate corrosion modelling.A new moderately dilute mass transport model was developed.A customized hybrid differencing scheme was used to discretize the model.The scheme calculated an appropriate upwind parameter based upon the Peclet number.Charge density effects were modelled using an algebraic charge density correction.Activity coefficients were calculated using Pitzer's equations.This transport model was computationally efficient and yielded accurate simulation results relative to experimental data.Use of the hybrid differencing scheme with the mass transport equation resulted in simulation results which were up to 87% more accurate (relative to experimental data) than other conventional differencing schemes.In addition, when the charge density correction was used during the solution of the electromigration-diffusion equation, rather than solving the charge density term separately, a sixfold increase in the simulation time to real time was seen (for equal time steps in both simulation strategies).Furthermore, the charge density correction is algebraic, and thus, can be * Reference: http://www.nace.org/nace/content/publicaffairs/cocorrindex.aspiii applied at larger time steps that would cause the solution of the charge density term to not converge.The validated mass transport model was then applied to simulate crevice corrosion initiation of passive alloys.The cathodic reactions assumed to occur were crevice-external oxygen reduction and crevice-internal hydrogen ion reduction.Dissolution of each metal in the alloy occurred at anodic sites.The predicted transient and spatial pH profile for type 304 stainless steel was in good agreement with the independent experimental data of others.Furthermore, the pH predictions of the new model for 304 stainless steel more closely matched experimental results than previous models.The mass transport model was also applied to model flow influenced CO 2 corrosion.The CO 2 corrosion model accounted for iron dissolution, H + , H 2 CO 3 , and water reduction, and FeCO 3 film formation.The model accurately predicted experimental transient corrosion rate data.Finally, a comprehensive model of crevice corrosion under the influence of flow was developed.The mass transport model was modified to account for convection.Electrode potential and current density in solution was calculated using a rigourous electrode-coupling algorithm.It was predicted that as the crevice gap to depth ratio increased, the extent of fluid penetration also increased, thereby causing crevice washout.However, for crevices with small crevice gaps, external flow increased the cathodic limiting current while fluid penetration did not occur, thereby increasing the propensity for crevice corrosion.and my life.Thank you so much.I am so
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".