Our Journey in Simulation of Corrosion: From Initial Premises to Digitally-Twinned Vehicles and Civilian Infrastructures
Notice bibliographique
Résumé
Finite-element analysis (FEA) numerical simulation is an indispensable tool for mechanical engineers, yet its application in corrosion engineering remains in its developmental stages. Over the past decade, the Corrosion Team at the NRC Automotive and Surface Transportation Research Center (AST) has been at the forefront of advancing this field. By integrating advanced machine learning algorithms, the team has developed and calibrated FEA corrosion models that utilize extensive data collected from instrumented vehicles. These vehicles are equipped with connected galvanic specimens, weight-loss devices, and sensors for air and surface temperature, relative humidity, and time-of-wetness. Recently, the team has added functionality to adapt model outputs based on the geographical location of serviced components, offering context-specific predictions and solutions. Efforts have been made to scale these models to the microscale, allowing the prediction and mitigation of failure mechanisms originating at this level, which often escalate into engineering failures. This work, ongoing and informed by publications [1,2], aims to address critical gaps in corrosion prediction. These advancements have resulted in adoption of the software by automotive suppliers and OEMs, significantly reducing the need for experimental validation tests and accelerating the development of cost-effective, corrosion-resistant designs. Building on this success, NRC-AST has partnered with the NRC-Construction Corrosion Team to tackle the challenge of predicting the corrosion behavior of bridge structural assemblies made of weathering steel and various mechanical fasteners. Using FEA and historical weather data from across Canada, the team has extended the scope of their models to address the unique needs of Canadian civilian infrastructures exposed to de-icing salts. As of this 247 th ECS meeting in Montreal, this new application is publicly available for download from the NRC website: nrc.canada.ca/en/research-development/products-services/software-applications. This talk will showcase NRC’s remarkable journey, from our first connected vehicle to digitally twinned FEA models and vehicles, and finally to the successful technological transfer to the Canadian civilian infrastructure domain. References: [1] Hu Zhou, Danny Chhin, Alban Morel, Danick Gallant, and Janine Mauzeroll (2022) Potentiodynamic polarization curves of AA7075 at high scan rates interpreted using the high field model. npj Material Degradation 6 , 20 (doi: 10.1038/s41529-022-00227-3). [2] Hu Zhou, Danny Chhin, Yuanjiao Li, Danick Gallant, Alban Morel, and Janine Mauzeroll (2024) Quantitative interpretation of potentiodynamic polarization curves obtained at high scan rates in scanning electrochemical cell microscopy. Analytical Chemistry 96 (38) 15108-15116 (doi: 10.1021/acs.analchem.4c01476). Figure 1
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».