Effects of Different CO2 equations of state on Condensation Position in Numerical Investigation
Bibliographic record
Abstract
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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".