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Record W2916382252 · doi:10.1115/1.4042975

Modeling Multiphase Effects in CO2 Compressors at Subcritical Inlet Conditions

2019· article· en· W2916382252 on OpenAlexaboutno aff
Ashvin Hosangadi, Zisen Liu, Timothy Weathers, Vineet Ahuja, Judy Busby

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2019
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
FundersNational Energy Technology LaboratorySmall Business Innovative Research and Small Business Technology TransferU.S. Department of Energy
KeywordsMechanicsInletNozzleMultiphase flowComputational fluid dynamicsGas compressorThermodynamicsMaterials scienceImpellerCondensationEngineeringPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

An advanced numerical framework to model CO2 compressors over a wide range of subcritical conditions is presented in this paper. Thermodynamic and transport properties are obtained through a table look-up procedure with specialized features for subcritical conditions. Phase change is triggered by the difference between the local values of pressure and saturation pressure, and both vaporization and condensation can be modeled. Rigorous validation of the framework is presented for condensation in high pressure CO2 using test data in a De Laval nozzle. The comparisons between computations and test data include: condensation onset locations, Wilson line, and nozzle pressure profiles as a function of inlet pressures. The framework is applied to the Sandia compressor that has been modeled over broad range of conditions spanning the saturation dome including: near critical inlet conditions (305.4 K, and 7.843 MPa), pure liquid inlet conditions (at 295 K), pure vapor inlet conditions (at 302 K), and two-phase inlet conditions (at 290 K). Multiphase effects ranging from cavitation at the liquid line to condensation at the vapor line have been simulated. The role of real fluid effects in enhancing multiphase effects at elevated temperatures closer to the critical point has been identified. The performance of the compressor has been compared with test data; the computational fluid dynamics (CFD) results also show that the head-flow coefficient curve collapses with relatively minor scatter, similar to the test data, when the flow coefficient is defined on the impeller exit meridional velocity.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.420

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 head0.006
GPT teacher head0.223
Teacher spread0.218 · 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 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

Citations35
Published2019
Admission routes1
Has abstractyes

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