MétaCan
Menu
Back to cohort
Record W2792915565 · doi:10.1002/cjce.23147

Study of gas solid turbulent flow in a thermal reactor: Experimental and numerical comparison

2018· article· en· W2792915565 on OpenAlexvenueno aff
Ahmed Bellil, Karim Benhabib, Patrice Coorevits, Aïssa Ould‐Dris

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsnot available
Fundersnot available
KeywordsExtrapolationNuclear engineeringFlow (mathematics)TurbulenceResidence time distributionWork (physics)Finite volume methodMechanicsEnvironmental scienceControl volumeProcess engineeringResidence time (fluid dynamics)ThermalComputer scienceMaterials scienceMeteorologyMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Dysfunctions observed in thermochemical conversion reactors, like dead zones and short‐ circuits, generally lead to inaccurate pricing of energy resources and air pollution, and originate in the air flow conditions in these aeraulic reactors. [1–3] Therefore, they can be prevented by better control of flows. In this work, we propose to develop a new tool for determining the distribution of residence time of the solid phase, based on the luminescence of particles previously coated with phosphorescent pigments. This optical method, which is non‐intrusive and flexible, has been implemented at a laboratory scale, on an aeraulic test bench. [3] On the other hand, we have developed a numerical model allowing the determination of the distribution of the residence time. This development aims to master the flows at the exit of surrounding walls, which optimizes them and allows their extrapolation to the industrial scale. Our analytical approach is based on modelling by coupling MFN by finite volume types via the Code Saturn, and DEM by discrete elements of the solid behaviour by means of the code SIGRAME. Finally, a confrontation of the RTD of the digital model with the experimental RTD has been conducted.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.417

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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicParticle Dynamics in Fluid FlowsFrench-language works237,207