Our Journey in Simulation of Corrosion: From Initial Premises to Digitally-Twinned Vehicles and Civilian Infrastructures
Bibliographic record
Abstract
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 247th 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
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".