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Record W3088175140 · doi:10.1002/pc.25820

Numerical and experimental investigation of the erosion of zirconia particulate‐reinforced epoxy matrix composites by angular silicon carbide particles

2020· article· en· W3088175140 on OpenAlexafffund
Navid Heydarzadeh Arani, Majid Eghbal, Mohammad Mahdi Nekahi, M. Papini

Bibliographic record

VenuePolymer Composites · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceEpoxyComposite materialErosionSilicon carbideFinite element methodParticle (ecology)Composite numberCubic zirconiaPerpendicularFracture (geology)ReinforcementParticulatesCeramicStructural engineeringGeometry

Abstract

fetched live from OpenAlex

Abstract An smooth particle hydrodynamics/finite element (SPH/FEM) model was developed to simulate the erosion of zirconia particle‐reinforced epoxy composites, due to the overlapping impacts of angular particles at both perpendicular and oblique incidences. For each composite target, multiple sub‐volumes, each having their own random reinforcement distribution, were modeled. The reinforcements were assumed as noneroding and nondeforming and were distributed in the modeled sub‐volumes according to the random distributions measured in the actual samples. Erosion tests were performed to verify the numerical model, and it was found that the erosion rates of the zirconia‐reinforced composites were in all cases significantly lower than the neat epoxy. The experimentally‐observed erosion mechanisms were successfully simulated by the model, and the predicted erosion rates matched the measured ones to within 2% to 11%. It is anticipated that similar models can be used to predict erosion of other particulate composite systems whose reinforcement erosion and fracture is negligible compared to the matrix erosion.

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 score0.251
Threshold uncertainty score0.400

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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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

Citations9
Published2020
Admission routes2
Has abstractyes

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