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Record W7030605906

Numerical simulation of condensation in a supersonic nozzle for application to ejectors in refrigeration

2014· dissertation· en· W7030605906 on OpenAlexaboutno aff

Bibliographic record

VenueRECERCAT (Consorci de Serveis Universitaris de Catalunya) · 2014
Typedissertation
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleSupersonic speedRefrigerationInjectorIdeal gasComputer simulationCondensation
DOInot available

Abstract

fetched live from OpenAlex

This thesis is a CFD study of a supersonic nozzle for ejector refrigeration applications.\nCondensation of the working \nuid is aimed inside the nozzle in order to study its conse-\nquences. As a first approach to verify the built-in Wet Steam model in ANSYS Fluent,\na Laval nozzle is tested with water and compared with experimental data from Moses\nand Stein.\nThanks to the User Defined Wet Steam Property Functions in the User Defined Wet\nSteam model, the code is adapted for R134a both ideal and real gas properties and the\nstudy is done with perfect and real gas properties. Anyway, the software presents certain\nlimitations and the real gas Peng-Robinson Equation Of State could not be implemented\ndue to convergence problems.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.248
Teacher spread0.239 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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