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INVESTIGATION OF THE EFFECTS OF POROUS MEDIA AT THE EXIT OF COUNTERFLOW COMBUSTION USING THE LATTICE BOLTZMANN METHOD

2015· article· en· W2526536488 on OpenAlexaff
Ali Tarokh, A. A. Mohamad

Bibliographic record

VenueSpecial Topics & Reviews in Porous Media An International Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPorous mediumLattice Boltzmann methodsCombustionMaterials sciencePorosityHeat transferThermal conductionMechanicsThermodynamicsChemical engineeringComposite materialChemistryPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

In this study, the lattice Boltzmann method is utilized to investigate the effects of porous medium at the exit of the counterflow combustion. Porous matrices with different arrangements are created by scattering solid obstacles inside the flow path. The LBM model is used for simulation of conduction heat transfer between the discrete parts of the porous medium. A single-step chemical reaction is used for modeling of the combustion of propane/air mixture and properties are recorded at the exit of the domain. However, to investigate the effects of porous media on the emission of the intermediate species like CO, a two-step chemical reaction is utilized to model the methane/air combustion. The results show that the presence of the porous media decreases the maximum exit temperature and causes a more uniform temperature profile at the exit compared with results of combustion without porous medium. Also, utilizing porous media reduces the CO and unburned fuel concentration at the exit and consequently increases the combustion efficiency. Effects of the different porous structure on exit temperature and species concentrations are also investigated in this study.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 science0.0010.000
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 head0.062
GPT teacher head0.330
Teacher spread0.268 · 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 designObservational
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

Citations3
Published2015
Admission routes1
Has abstractyes

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