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Record W2127412122 · doi:10.1142/s1793962314500020

Analytical modeling of oxide thickness variation of metals under high temperature solid-particle erosion

2013· article· en· W2127412122 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

VenueAdvances in Complex Systems · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsNational Research Council CanadaCarleton UniversityUniversity of Ottawa
Fundersnot available
KeywordsOxideAlloyMaterials scienceErosionMetallurgyParticle (ecology)Deformation (meteorology)Particle sizeComposite materialChemical engineeringGeology

Abstract

fetched live from OpenAlex

The paper presents a study of model development for predicting the oxide thickness on metals under high temperature solid-particle erosion. The model is created based on the theory of solid-particle erosion that characterizes the erosion damage as deformation wear and cutting wear, incorporating the effect of the oxide scale on the eroded surface under high temperature erosion. Then the instantaneous oxide thickness is the result of the synergetic effect of erosion and oxidation. The developed model is applied on a Ni -based Al -containing ( Ni – Al ) alloy to investigate the oxide thickness variation with erosion duration of the alloy at high temperatures. The results show that the thickness of the oxide scale on the alloy surface increases with the exposure time and temperature when the surface is not attacked by particles. However, when particles impact on the alloy surface, the oxide thickness is reduced, although oxidation is continuing. This indicates that oxidation does not benefit the erosion resistance of this alloy at high temperatures due to the low growth rate of the oxide.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.353

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 head0.027
GPT teacher head0.296
Teacher spread0.269 · 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