(Invited) Atmospheric Corrosion Monitoring in Cold Arctic Climate Using Modular Corrosion Test Racks
Bibliographic record
Abstract
Atmospheric corrosion is a complex process, which involves chemical, electrochemical, and physical changes to the metal exposed. Atmospheric corrosion occurs when a metal surface is under a thin layer of moisture, but not completely immersed, and the metal surface corrodes while exposed to environmental factors. The arctic and sub-arctic region identified by the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) has an average temperature of -18oC or less during winter. It is commonly assumed that there is very little to no corrosion in cold environments. However, previous studies in the Antarctic and Arctic regions have shown significant corrosion damage when exposed to cold conditions. Studies in the sub-arctic region of Canada, Norway, and Russia show extensive atmospheric corrosion rates (when compared to Antarctica) due to human developments and the resulting increase in mining and metallurgical industries. Experimental and theoretical work has shown that the electrochemical process proceeds at temperatures as low as -25oC to -20oC . Moreover, very little corrosion data are available for metal alloys exposed to cold arctic and sub-arctic conditions. The two important factors that affect atmospheric corrosion rates are aerosol chlorides (or salt-laden snow from the marine environment or deicing salts on roads) and time of wetness (TOW) along with other climatic parameters such as rainfall, temperature, humidity. Factors that drive the atmospheric corrosion in cold climates are winds that can bring in salt-laden snow from the marine environment and the use of de-icing salts can also contribute to high levels of chlorides. The eutectic point or the freezing point of de-icing salts can be lowered to -50oC, melting the ice/snow layer on top of metal samples. This phenomenon keeps metal samples moist for much longer periods, thus increasing the TOW. In the presence of chlorides and moisture, extensive atmospheric corrosion damages can be observed on metal samples. Another contributing factor to high corrosion rates is low rainfall, which in turn cannot periodically wash off the deposited chlorides and SO2 on top of the samples. In addition, ever-increasing ambient temperatures due to climate change in recent years affect the snow presence on top of the metal samples. The temperature of the samples is not too high to evaporate the snow/ice deposited, but high enough to cause melt and sustain moisture for longer periods of time. This leads to the formation of varying thickness of wet ice/snow layers on the metal surface. Long hours of sunlight in the summer also increase the surface temperature of metal samples beyond the ambient temperatures, causing dew formation and condensation, which in turn results in higher TOW. The atmospheric corrosion damage in cold environments is close to the main human activity, which is concentrated near the coastal areas. The substantial human growth and climate change in the arctic and sub-arctic region pushes for a renewed better understanding of the atmospheric corrosion mechanisms that can lead to good choice of materials selection and better design practices for infrastructure and other applications. The combination of urbanization and proximity to marine environments make arctic and sub-arctic regions in North America, particularly Alaska, an important natural laboratory to study atmospheric corrosion in cold regions. Design and establishment of modular corrosion test racks equipped with weather stations will be discussed. The angle of exposure can be changed to 0, 30 and 45-degrees to the horizontal (see the attached figure). This will allow us to test the metals alloys exposed to different angles for the same exposure period and study the effect of snow/ice retention on top of the metal surface. 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".