MétaCan
Menu
Back to cohort
Record W1997047455 · doi:10.1002/cjce.20676

Hydrodynamic characterisation of liquid–solid two–phase fluidised beds: Vibration signature and pressure fluctuations analyses

2011· article· en· W1997047455 on OpenAlexvenueaboutno aff
Amir Sheikhi, Rahmat Sotudeh‐Gharebagh, Mehrdad Alfi, Navid Mostoufi, Reza Zarghami

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
Fundersnot available
KeywordsKurtosisVibrationStandard deviationMechanicsPhase (matter)Materials scienceIntersection (aeronautics)SkewnessThermodynamicsMathematicsPhysicsEngineeringStatisticsAcoustics

Abstract

fetched live from OpenAlex

Abstract Hydrodynamic behaviour of a two–phase liquid–solid fluidised bed was investigated over a wide range of liquid velocities by means of simultaneous vibration and pressure fluctuations analyses. The liquid velocities were set in a way that covered two most important hydrodynamic events in the bed, namely minimum fluidisation and circulating‐solid regime. To prevent solids from being carried out of the bed, the maximum liquid velocity was kept lower than the terminal velocity of solids. Statistical analysis on the vibration signatures of bed shell proved to be a strong representative for minimum fluidisation characterisation and solid regime change. The minimum fluidisation velocity can be obtained from the intersection of two linear parts in the standard deviation of vibration fluctuation signals. Moreover, the kurtosis of vibration signals could predict the minimum fluidisation and approximate solid regime transition successfully. Meanwhile, statistical parameters, such as standard deviation, skewness, and kurtosis as well as newly‐introduced parameters, namely the energy and average cycle frequency of pressure signals, determined both of minimum fluidisation condition and circulating‐solid flow regime. © 2011 Canadian Society for Chemical Engineering

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 score0.214
Threshold uncertainty score0.454

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 head0.230
Teacher spread0.215 · 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

Citations16
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicGranular flow and fluidized bedsFrench-language works237,207