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Record W4414511276 · doi:10.33737/gpps25-tc-263

Impact of Inlet Thermodynamic Conditions on sCO2 Compressor Performance

2025· article· en· W4414511276 on OpenAlexaboutno aff
Sanghyun Lee, Jentung Ku, Youngkuk Yoon, Kilyoung Kim

Bibliographic record

VenueProceedings · 2025
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsIsentropic processGas compressorImpellerDiffuser (optics)InletCentrifugal compressorCompressibilityNozzleAerodynamicsHead (geology)

Abstract

fetched live from OpenAlex

This study investigates the influence of inlet thermodynamic conditions on the performance of a centrifugal compressor operating near the critical point of CO2. In this region, steep property gradients cause strong real gas effects and phase change, which significantly impact aerodynamic performance. To numerically investigate its effect, a CFD framework is established, incorporating a look-up table approach and a Homogeneous Binary Mixture model for phase change. The numerical method is validated against experimental data from a published De Laval nozzle test. Compressor performance– including head coefficient, pressure ratio, and isentropic efficiency–are evaluated for 24 sampled inlet conditions under the design operating condition. As the inlet entropy decreases, the pressure ratio increases by up to 30% while the head coefficient decreases by up to 9%. However, isentropic efficiency varies relatively little across all cases, with deviations less than 0.7%. The observed performance trends are explained by the non-ideal behavior of CO2 near the critical point. The reduction in head is attributed to a decrease in swirl velocity at the impeller exit caused by changes in the isentropic density-pressure derivative, (𝐷𝜌⁄𝐷𝑃)!. The increased pressure ratio is associated with a lower compressibility factor Z. Moreover, the reduced swirl velocity leads to a higher incidence at the diffuser inlet, resulting in additional aerodynamic losses in the diffuser region.

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 score0.398
Threshold uncertainty score0.291

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 head0.254
Teacher spread0.247 · 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
Published2025
Admission routes1
Has abstractyes

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