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Record W4406627595 · doi:10.1002/cjce.25598

Effect of structure parameters on solid particle erosion resistance coupled biomimetic anti‐erosion functional surface: A <scp>CFD</scp>‐<scp>DPM</scp> investigation

2025· article· en· W4406627595 on OpenAlexvenueno aff
Yunshan Dong, Jialiang Guo, Kun Yang, Xiaodong Si, Hongyu Pan

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsnot available
Fundersnot available
KeywordsErosionComputational fluid dynamicsParticle (ecology)Particle sizeMaterials scienceGeotechnical engineeringEnvironmental scienceChemical engineeringGeologyEngineeringAerospace engineeringGeomorphology

Abstract

fetched live from OpenAlex

Abstract In this study, the effect of structure parameters on solid particle erosion resistance coupled biomimetic surface was investigated, revealing the optimal structural parameters that minimized erosion. A calculation process of erosion characteristics using computational fluid dynamics (CFD) was developed to simulate the erosion physical process. Results indicated that as the bump diameter and the groove width increased, the erosion rate of the biomimetic surface decreased for a given particle size, but the influence of groove width was greater than that of bump diameter. For a given particle size, as the groove width‐to‐depth ratio increased, the erosion rate of the biomimetic surface decreased initially and then increased, with an optimal value between 2.5 and 3 that yielded the lowest erosion. Additionally, changes in particle collision velocity were primarily governed by the vortex scale and intensity within the groove, while changes in particle collision angle were mainly influenced by the groove scale and angle.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.085
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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