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Nucleation and Condensation Modeling of Metal Vapor in Laval Nozzle

2014· article· en· W2085506696 on OpenAlexaboutno aff
Ehsan Zhalehrajabi, Nejat Rahmanian

Bibliographic record

VenueAdvanced materials research · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsnot available
FundersUniversiti Teknologi Petronas
KeywordsNucleationNozzleMercury (programming language)CondensationRankine cycleMaterials scienceSteam turbineTurbineThermodynamicsMechanicsPhysicsComputer science

Abstract

fetched live from OpenAlex

Nucleation and condensation of mercury vapor has been investigated in various divergent angle and operating condition. Divergent angle has a great effect on droplet size at the end of nozzle. Influence of operating condition such as pressure and temperature on the size of droplet has been investigated. A one-dimensional mathematical model based on classical nucleation and growth has been developed to calculate the nucleation and condensation of mercury vapor. A mercury vapour turbine has been used in conjunction with a steam turbine for generating electricity. The mercury cycle offers an efficiency increase compared to a steam-only cycle because energy can be injected into the Rankine Cycle at higher temperature. The target of modeling is predicting the droplet size of mercury nanoparticles during rapid expansion. The results are verified by accurate experimental data available in the literature. The governing equations were solved using Runge-Kutta third-order numerical method in MATLAB software.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.336
Teacher spread0.281 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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