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Record W4312522022 · doi:10.33737/gpps21-tc-247

Effects of Different CO2 equations of state on Condensation Position in Numerical Investigation

2022· article· en· W4312522022 on OpenAlexaboutno aff
Lei Zhang, Zhenyu Yang, Zheng Dong, Qian Zhang, Enhui Sun

Bibliographic record

VenueProceedings · 2022
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSupercritical fluidCondensationGas compressorEquation of stateMechanicsComputer simulationPosition (finance)Critical point (mathematics)ThermodynamicsFluid dynamicsMaterials sciencePhysicsMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Supercritical CO2 power generation system have been gaining interest in the field of thermal power conversion due to its high efficiency and compactness. The low power consumption of the near-critical compressor is the key factor for its high efficiency. However, when CO2 is pressurized from a near-critical state to supercritical state, condensation may occur locally due to the high nonlinear behavior of the flow properties in the compressor. In the numerical simulation of compressor, sharp change of CO2 fluid properties near critical point also brings difficulties in capturing the condensation phenomenon. In order to explore the influence of fluid properties on the condensation phenomenon, this article first discussed the difference among the RK equation of state, the PENG-ROB equation of state, and the CO2 real fluid properties from the NIST REFPROP database. Then a numerical three-dimensional simulation was done using a de Laval nozzle model and the results were used to study the influence of fluid properties calculated from different equations of state in CO2 numerical simulation on the condensation position.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.262

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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