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Experimental and Numerical Investigation of Particle Kinematics in Shotcrete

2014· article· en· W2085132848 on OpenAlexafffund
Nicolas Ginouse, Marc Jolin

Bibliographic record

VenueJournal of Materials in Civil Engineering · 2014
Typearticle
Languageen
FieldMaterials Science
TopicEngineering and Material Science Research
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNozzleShotcreteKinematicsMechanicsParticle (ecology)Particle velocityMechanical engineeringProcess (computing)AirflowStructural engineeringEngineeringMaterials scienceComputer scienceClassical mechanicsPhysicsGeology

Abstract

fetched live from OpenAlex

The intent of this paper is to identify the basic physical laws governing particle kinematics in a shotcrete spray. This paper presents a governing equation based on Newton’s second law where nozzle and airflow characteristics, and particle features are used to predict impact velocity of the material exiting the nozzle. Experimental values obtained using high-speed imaging measurements on marbles and aggregate particles correlate well with the results of numerical simulations on a simplified spraying system. With the controlling parameters of particle kinematics fully understood, the analysis of the governing equations offers essential elements for nozzle optimization and new mix design investigations. This paper also addresses the necessity of broadening the investigation to the entire spray of particles exiting the nozzle for a complete understanding of the spraying process on placement mechanisms at a process scale.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.266
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations22
Published2014
Admission routes2
Has abstractyes

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