Effects of fluid type and pressure order on performance of convergent–divergent nozzles: An efficiency model for supersonic separation
Bibliographic record
Abstract
Abstract A deep analysis on the hydrodynamics of convergent–divergent nozzles is performed by changing the working conditions and fluid type. The nozzle geometry lowers the temperature of the flowing fluid, which contributes to condensation and phase change. The focus of this paper is to evaluate the nozzle performance and cooling capacity in terms of temperature, pressure, and gas type in a fixed geometry of Sajben Laval nozzle. The analysis has been conducted via a 2‐dimensional turbulent computational fluid dynamics simulation for illustrating the behavior of the fluid. A criterion for the nozzle performance is provided by prediction of exact shock wave position. An investigation on 6 different gas types demonstrates that heat capacity and thermal conductivity are the most rolling fluid properties of the nozzle performance. Furthermore, it is found that the shock wave position is unchanged during alteration of fluid type or pressure scale. A new model is provided for prediction of convergent–divergent nozzle performance in the supersonic conditions for dehydration of natural gas as a well‐known industrial application of the nozzles. The model is developed by the genetic algorithm as a multivariable optimization method.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".