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Record W2783333362 · doi:10.1115/imece2017-70266

Studies on Combustion Characteristics of Two Interacting Coal Particles at Different Separation Distance

2017· article· en· W2783333362 on OpenAlexaff
Xianhui He, Mingyan Gu, Dawei Yan, Xue Chen, Dan Yan, Huaqiang Chu, Fengshan Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCharCombustionLaminar flowCoalParticle (ecology)MechanicsCoal combustion productsIgnition systemUpstream (networking)Materials scienceRange (aeronautics)Waste managementChemistryThermodynamicsPhysicsComposite materialEngineeringGeology

Abstract

fetched live from OpenAlex

To understand the interactions of coal particles on devolatilization, volatile burning, and char combustion, the combustion characteristics of two interacting equal-sized coal particles placed in the upstream and downstream configuration in a hot laminar flow are numerically investigated. A two-dimensional mathematical model was developed based on the wall surface reaction theory in the commercial software FLUENT. The numerical results show that the particle interaction has different effects on the coal ignition time and combustion characteristics of the upstream and downstream particles. The upstream coal particle undergoes a faster temperature rise, earlier coal devolatilization, and faster char burnout than the downstream one. With increasing the particle separation distance within a certain range, the temperature rise, coal devolatilization, and char combustion processes are further enhanced and weakened for the upstream particle and the downstream particle, respectively. There exists a critical particle separation distance beyond which the combustion processes of the two particles are similar to those of a single particle.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.307
Teacher spread0.276 · 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 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
Published2017
Admission routes1
Has abstractyes

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