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Record W2804428785 · doi:10.1139/tcsme-2017-1045

INVESTIGATION OF CO<sub>2</sub> ABSORPTION PERFORMANCE IN A GAS-LIQUID TWO-PHASE FLOW ATOMIZER ON THE BASIS OF A GAS DIFFUSION MODEL

2017· article· en· W2804428785 on OpenAlexvenueno aff
Jiafeng Yao, Minghao Yu, Tong Zhao, Akimaro KAWAHARA, Michio SADATOMI

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsDiffusionAbsorption (acoustics)MistFlow (mathematics)Materials scienceGaseous diffusionAnalytical Chemistry (journal)Liquid phasePhase (matter)ChemistryThermodynamicsMechanicsChromatographyComposite material

Abstract

fetched live from OpenAlex

Experimental and analytical studies of CO2 absorption performance are carried out in a gas-liquid two-phase flow atomizer on the basis of a gas diffusion model. The gas-liquid two-phase flow atomizer with high spray efficiency and low power consumption has been applied to CO2 absorption. Experiments for the CO2 absorption were conducted in an isolated room and the results showed that, the mist sprayed by the improved atomizer can effectively reduce the CO2 concentration. Furthermore, a CO2 diffusion model was developed to predict the absorption process. This model was validated through a comparison between calculation and experiment. Comparison results showed that the proposed model could predict the time-variations of CO2 concentration well in the test room.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.212
Teacher spread0.195 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicCarbon Dioxide Capture TechnologiesFrench-language works237,207