MétaCan
Menu
Back to cohort

Nozzle design influence on the steam-driven ejector

2020· article· en· W3137081161 on OpenAlexaboutno aff
D. V. Brezgin, К. Э. Аронсон

Bibliographic record

VenueJournal of Physics Conference Series · 2020
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleInjectorMechanicsMass flow rateDischarge coefficientComputational fluid dynamicsMaterials scienceMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract In the present paper, a series of numerical simulations of wet steam flows within ejectors distinguished by four primary nozzle contours have been carried out with the objective to evaluate the overall ejector performance. The studied primary nozzle contours are the following: a standard Laval nozzle (LAVAL); a nozzle that is designed in order to provide Constant Expansion Rate (CER); and two nozzles (MOC_SHORT, MOC_LONG) that are designed by employing axisymmetric Method of Characteristics. At first, the wet steam flow simulation throughout solely nozzles are carried out. Within the CFD simulations routines most valuable flow parameters are compared: expansion rates, boundary layer displacement and momentum thicknesses; entropy generation rate from various thermodynamic forces; thrust and liquid mass fraction across the nozzle exit plane. A comprehensive analysis of solely nozzles revealed that the CER nozzle contour is the most aerodynamically efficient design, which provides the minimum liquid mass fraction by the nozzle exit and possess the minimum entropy generation rate. The CFD results analysis of a full ejector domain revealed that an ejector with a MOC_SHORT primary nozzle contour provides the maximum performance in terms of secondary mass flow rate. At that, the secondary mass flow rate in MOC_SHORT nozzle contour case is almost 4% greater than for the CER nozzle design case.

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 score0.450
Threshold uncertainty score0.257

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 head0.043
GPT teacher head0.220
Teacher spread0.177 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physics Conference SeriesSame topicRefrigeration and Air Conditioning TechnologiesFrench-language works237,207