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Record W2141710790 · doi:10.2514/6.2010-7837

Centrifuge Adhesion Test to Evaluate Icephobic Coatings

2010· article· en· W2141710790 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsCentrifugeAdhesionPaint adhesion testingMaterials scienceComputer scienceComposite materialPhysics

Abstract

fetched live from OpenAlex

Ice accumulation is a human safety risk and a cause of important material damages in maritime, ground and air transportation, on ground and maritime energy exploitation and in telecommunication and electric networks. Many ice protection systems have been developed to prevent ice formation, but they are expensive to use due to their energy consumption. Passive systems, such as icephobic coatings applied on exposed surfaces, appear to be an interesting solution to prevent ice accumulation by reducing its adherence; but coating icephobicity is difficult to quantify. To measure icephobicity, the Anti-icing Material International Laboratory (AMIL) developed the Centrifuge Adhesion Test (CAT) in 2006. The test involves icing the extremity of small beams under freezing drizzle in a climatic chamber. Following icing, the beams are balanced in a centrifuge and rotated at an accelerating speed until the ice detaches and the corresponding rotation speed is measured. The adhesion shear stress is calculated from the centrifugal force evaluated at the detachment rotation speed, and the ice contact surface. The results are then reported as an Adhesion Reduction Factor (ARF) which is the ratio of the adhesion shear stress of the beams with a candidate coating with respect to uncoated beams. The higher the ARF, the more icephobic the coating; values below one indicate an increase in adhesion. For coatings with high icephobicity, the CAT apparatus sensitivity proved to be insufficient to evaluate the ARF. To remedy this, in 2009, AMIL modified the CAT apparatus to increase its sensitivity. The new CAT, called CAT-NG, can measure ARF in a range of 0.6 to 1 000 inexpensively and timely. To ensure reliable ARF factor with confidence level of 95%, the beam area exposed to icing is polished after 5 tests, or every month, to reduce the ice erosion effect on aluminum surface. Also, the ice specimen mass should be 5.5 g ± 5% with a length of 34 mm ± 5%. For glaze ice tested at -10°C on 6061-T6 aluminum with AMIL Standard Surface roughness of 0.7 μm, the detachment speed is 7 800 RPM ± 7% when the beam is rotated at speeds increasing linearly at rate of 300 RPM/s corresponding to a shear stress of 0.51 MPa ± 7%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.011

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.

Opus teacher head0.021
GPT teacher head0.278
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it