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Record W4402154356 · doi:10.31399/asm.cp.itsc2024p0049

Techno-Economic Assessment of Utilization of Cold Spraying Process for Fabrication of Resistive Heating Elements for Temperature Protection of Steel Pipes

2024· article· en· W4402154356 on OpenAlexaff
M. Rad, Peter Menghesha, Maria Ophelia Jarligo, André McDonald

Bibliographic record

VenueThermal spray · 2024
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFabricationHeating elementMaterials scienceCoatingElectric heatingGas dynamic cold sprayTinProcess engineeringProcess (computing)Composite materialMetallurgyEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract Design, manufacturing, and utilization of efficient heating systems for pipelines and closed-pressure equipment are necessary for cold regions to compensate for heat loss and prevent damages that are caused by freezing of the enclosed liquid. Given large-scale financial losses that stem from failure and bursting of the pipes, the development of novel, efficient, and affordable heaters, which can lead to improved efficiency, cost savings, and environmental benefits across various industries and applications, is of crucial importance. Heating systems have already been produced via different high-temperature thermal spraying techniques to achieve higher efficiency compared to conventional heating cables. In this study, tin, as the heating element, was deposited by using the cold spray process onto alumina coating that was fabricated by flame spraying (FS) to provide electrical insulation. Techno-economic assessment of fabrication and utilization of the coating-based heaters was conducted. It was found that cold-sprayed heater coatings exhibit improved performance compared to other thermally sprayed heater coatings and conventional heater cables. Further, their fabrication and utilization were more economically feasible. The results suggest that the new generations of coating-based heating systems may be competitive with conventional heat tracers that are widely used in industry.

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.

How this classification was reachedexpand

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 score0.010
Threshold uncertainty score0.525

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 head0.029
GPT teacher head0.319
Teacher spread0.290 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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