Bibliographic record
Abstract
Abstract In the present paper, a series of numerical simulations of wet steam flows within ejectors distinguished by four primary nozzle contours have been carried out with the objective to evaluate the overall ejector performance. The studied primary nozzle contours are the following: a standard Laval nozzle (LAVAL); a nozzle that is designed in order to provide Constant Expansion Rate (CER); and two nozzles (MOC_SHORT, MOC_LONG) that are designed by employing axisymmetric Method of Characteristics. At first, the wet steam flow simulation throughout solely nozzles are carried out. Within the CFD simulations routines most valuable flow parameters are compared: expansion rates, boundary layer displacement and momentum thicknesses; entropy generation rate from various thermodynamic forces; thrust and liquid mass fraction across the nozzle exit plane. A comprehensive analysis of solely nozzles revealed that the CER nozzle contour is the most aerodynamically efficient design, which provides the minimum liquid mass fraction by the nozzle exit and possess the minimum entropy generation rate. The CFD results analysis of a full ejector domain revealed that an ejector with a MOC_SHORT primary nozzle contour provides the maximum performance in terms of secondary mass flow rate. At that, the secondary mass flow rate in MOC_SHORT nozzle contour case is almost 4% greater than for the CER nozzle design case.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".