Development of Predictive Models of Flow Induced and Localized Corrosion
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».