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Record W2225953135 · doi:10.1115/icnmm2008-62252

The Effects of Water Injections in Wet Steam Flow in Different Regions of a Mini Laval Nozzle

2008· article· en· W2225953135 on OpenAlexaboutno aff
Mohammad Reza Mahpeykar, Esmail Lakzian

Bibliographic record

VenueASME 2008 6th International Conference on Nanochannels, Microchannels, and Minichannels · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsnot available
Fundersnot available
KeywordsCondensationNozzleTurbineSuperheatingMechanicsSteam turbineNucleationMaterials scienceShock (circulatory)Superheated steamThermodynamicsLatent heatHeat transferPhysics

Abstract

fetched live from OpenAlex

Progress in the development of the steam turbines brings about a renewal of interest in wetness associated problems. In turbine steam expansion, the vapour first supercools and then condenses spontaneously to become a two phase mixture. The flow initially is single phase but after Wilson point water droplets are developed and there is a non equilibrium two phase flow. The formation and behavior of the liquid create problems that lower the performance of the turbine wet stage and the mechanisms underlying this are insufficiently understood. This growing droplets release their latent heat to the flow and this heat addition to the supersonic flow cause a pressure rise called condensation shock. Because of irreversible heat transfer in this region the entropy will increase tremendously. Removal of condensates from wet steam flow in the last stage of steam turbines significantly promotes stage efficiency and prevents erosion of rotors. The following study investigates the spraying water droplets at inlet and at throat of mini Laval nozzle and their effects on nucleation rate and condensation shock. According to the results, the nucleation rate is considerably suppressed and therefore the condensation shock nearly disappeared. In other words the injecting droplets would decrease the thermodynamic losses or improve the turbine efficiency.

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 score0.159
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.022
GPT teacher head0.238
Teacher spread0.216 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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