MétaCan
Menu
Back to cohort
Record W2803924139 · doi:10.31399/asm.cp.itsc2018p0635

Development of a Thermal-Sprayed Coating System to Mitigate Ice Accumulation and Freezing Damage in Carbon Steel Pipes

2018· article· en· W2803924139 on OpenAlex
André McDonald

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

VenueThermal spray · 2018
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceCoatingComposite materialHeating elementScanning electron microscopeLayer (electronics)Substrate (aquarium)Carbon steelMetallurgyThermalCorrosion

Abstract

fetched live from OpenAlex

Abstract A multi-layered thermal-sprayed coating system, developed as a resistive heating system, was deposited on a carbon steel pipe. The feasibility of using a 50Cr-50Ni coating as a heating element on top of a conductive substrate was studied. Alumina was deposited to serve as an electrically insulating layer between the metal coating and the substrate to restrict the flow of electrons from the metal alloy heating element to the steel substrate. Continuity, homogeneity, and adhesion of the coating were qualitatively analyzed by studying scanning electron microscope images. The performance of the heating system was determined by measuring the ice temperature and the times required to heat and melt the solid ice that was formed within the pipe. It was found that the coating system was able to generate the heat required to melt the ice in the pipe, thus avoiding the detrimental effects on the pipe of internal liquid freezing. This suggests that the proposed novel resistive heating system can be used on an industrial scale to mitigate or avoid the detrimental effects of ice accumulation in steel and other metallic pipes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
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 score0.108
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.235
Teacher spread0.219 · 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