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Record W2979846725 · doi:10.33737/gpps19-bj-086

On the Three-Dimensional Body Force Model

2019· article· en· W2979846725 on OpenAlexaboutno aff
Yinbo Mao, Thong Q. Dang

Bibliographic record

VenueProceedings · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsThermodynamicsSupercoolingSupercritical fluidCondensationNozzleEquation of stateNon-equilibrium thermodynamicsReal gasMechanicsMaterials scienceChemistryPhysics

Abstract

fetched live from OpenAlex

Accurate prediction of nonequilibrium condensation in turbomachinery is a critical issue for the design of lowpressure steam turbines and supercritical CO 2 compressors.Previously developed numerical methods for high-pressure flows with nonequilibrium condensations used REFPROP to simulate the effect of real gases; however, the estimation of the equation of state for supercooled gas was simply extrapolated from the saturated line.In this paper, an estimation method of the equation of state of a supercooled gas applicable to high-and low-pressure condensing flows is proposed.Laval nozzle flows of steam and CO 2 in high-and low-pressure conditions were then numerically investigated using the proposed method.Although the effect of real gas is much important in high-pressure conditions, the estimation method for supercool condition had relatively little effect on the pressure distribution in high-pressure flows with nonequilibrium condensation.In low-pressure steam flows containing nonequilibrium condensation, the slight difference of supercooled - diagram by the estimating method has the crucial effect on the pressure distribution due to high supersaturation ratio.Subscript in inlet condition l liquid phase g gas phase sat saturated point s value on isentropic line

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.996

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)0.0050.006

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 head0.017
GPT teacher head0.216
Teacher spread0.199 · 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; both teacher heads agree on what is shown here.

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
Published2019
Admission routes1
Has abstractyes

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