Impact of Inlet Thermodynamic Conditions on sCO2 Compressor Performance
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".