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Record W4283575967 · doi:10.11159/ffhmt22.213

Enhancement Of The Parallel And Series Mode Of The Ultrasonic Atomizer On The Ammonia-Water Falling Film Absorber

2022· article· en· W4283575967 on OpenAlexvenueno aff
Runfa Zhou, Minqi Wang, Shuhong Li

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsFalling (accident)Ultrasonic sensorMaterials scienceSeries (stratigraphy)AmmoniaMode (computer interface)AcousticsOpticsOptoelectronicsComputer sciencePhysicsChemistryGeology

Abstract

fetched live from OpenAlex

Falling film absorber is widely used in the ammonia-water absorption refrigeration system, and the mass transfer area can be enlarged by the atomization device to overcome the shortages of low system energy efficiency and large system size. Ultrasonic atomizer can produced micron size droplets, so the liquid-vapor contact area can be further enlarged. Considering the large flight resistance of the small size droplets, the ultrasonic atomizer devices are located at the top of the absorber, and two installation strategies (i.e., parallel and series) are proposed. To analyze the improvement of the novel absorber with the installation of the atomizer, a mathematical model to describe the mass and heat transfer of the novel absorber is established. The model is verified by the reported data of literature, and the enhancement effects of the parallel and series installation strategies of the atomizer are discussed by the validated model. In the condition of this paper, compared with traditional falling film absorber, the ammonia absorbed rate is increased by 22.5% for the parallel installation strategy and 17% for the series installation strategy. Compared with the improvement of the cooling capacity, the energy consumption of the atomizer is worth.

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 score0.221
Threshold uncertainty score0.369

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 science0.0010.000
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 head0.012
GPT teacher head0.202
Teacher spread0.189 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicFluid Dynamics and Heat TransferFrench-language works237,207