Performance Characteristics of Surface Protection Coating Systems for Offshore Structures Under Arctic Conditions
Bibliographic record
Abstract
Abstract Eight organic coating systems have been investigated according to their performance under Arctic offshore conditions. Three performance groups were considered: corrosion protection performance, performance under mechanical loads, and de-icing performance. The investigations involved the following tests: accelerated corrosion protection/ageing testing, anti-icing tests, tests for coating and ice adhesion, impact resistance tests, hardness measurements, and wettability tests. The test conditions were adapted to Arctic offshore conditions, which mainly covered low temperatures down to -60°C, dry-wet cycles, and UV radiation. Testing facilities for icing/de-icing performance tests have been developed. Standard offshore tests for corrosion protection assessment were partly modified. The coating systems investigated were organic coating systems which differed in generic coating material, number of layers, dry film thickness and application method. The first part of the paper reports about experimental design, the test methodologies, and test facilities used. The second part discusses the results of the investigations. Based on an assessment scheme, a ranking of coating systems is recommended regarding their protection performance under Arctic offshore conditions. The investigations are part of a nationally funded Cluster Project " POLAR?? (Production, Operation and Living in Arctic Regions). 1.0 Introduction Oil and gas exploration in Arctic regions is a future scenario for energy delivery. Due to changes in climate, inaccessible areas become accessible for exploration, drilling and production devices. In due of this development, a number of challenges will be faced, mainly caused by the harsh environment. The environment is characterized by violent wind, high waves, very low air temperatures, and ice, rime and snow. The latter issues, in particular, will have effects on the performance of surface protection systems. It is known that steel corrosion will not be accelerated in low-temperature sea water (Melchers, 2002), although the water may show increased oxygen contents. It is not an increase in corrosivity, but rather the question how surface protective coatings will respond to the harsh environment, that will determine the performance of organic coatings. This would specifically include the following items:–corrosion protection capacity;–response to mechanical loads;–icing and de-icing behavior. In terms of corrosion protection capacity, the low air temperature may be a special challenge to the coatings. Temperatures as low as -60 °C can be expected in Arctic regions. Standard testing scenarios for offshore coatings (ISO 20340, NORSOK M-501) request air temperatures up to −20 °C only, and it is not known how organic coatings may perform at lower temperatures. The response to mechanical loads can be related to low temperatures, too. It can be assumed that the response of organic materials may change from plastic response to elastic, or elastic-plastic, response (Hainsworth and Kilgallon, 2008), and to higher rigidity modules at low temperatures (Murase and Nanishi, 1985). This would, among others, induce a susceptibility to cracking (Bjoergum et al., 2011). Mechanical near-surface parameters, like scratch hardness, are also affected at low temperatures (Hainsworth and Kilgallon, 2008). These modification aspects are also considerable for the entire system " substrate-coating??, which may react in a modified way if exposed to very low temperatures. A parameter that would describe this response is the adhesion between substrate and coating. Icing and de-icing are crucial processes in terms of efficiency and safety of offshore structures (ISO 12949, 2001; ISO 19906, 2010; Ryerson, 2010). ISO 12949 (2001) distinguished between two types of atmospheric ice: glace and rime, whereby the formation of either ice type depends mainly on air temperature and wind speed. Glace is caused by freezing rain; it features a high density (900 kg/m3) and rather strong adhesion/cohesion strength numbers. Rime typically forms due to in-cloud icing; it has a moderate density, and it is usually vane-shaped. On offshore platforms, either type of ice may be found. Examples are shown in Fig. 1. Although active icing prevention strategies, such as heating, are powerful, they cannot be used elsewhere on a platform. Passive icing prevention in terms of anti-icing coating surfaces is a very attractive alternative. The same is true for anti-icing. Active methods, such as vibrations, heating or mechanical scraping, could be replaced or supported through coating surfaces that provide a weak adhesion to adhering ice (Antonini et al., 2011).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".