Numerical simulation of supersonic natural gas flow passing through a Laval nozzle to assess the possibility of Dehumidification
Bibliographic record
Abstract
The Supersonic separator is a convergent-divergent nozzle with condensation and phase change at supersonic speeds. In this separator, the flow is converted from subsonic to supersonic, and this change in the flow regime leads to a sharp decrease in temperature and the formation of a liquid within the gas. Fluid flow, mass and heat transfer in supersonic separators are poorly understood due to the complex interaction of supersonic flow and phase change. In this study, numerical modeling of water vapor condensation has been carried out to investigate the fluid flow in the supersonic separator. The mixture method is used to simulate the multiphase flow and the implemented turbulence model is the standard k-ε. The Lee phase change and UDF mathematical models have been used to accurately predict the spontaneous condensation phenomenon. The problem is simulated in two-dimensional mode, the inlet and outlet pressures and the inlet temperatures are assumed as boundary conditions, and the nozzle wall is adiabatic. The results obtained from the numerical model are in good agreement with the experimental data. Based on the analysis, the phase change of water from vapor to liquid and the dehumidification, which was the main objective of this research, has been successfully achieved. Also, with an increase of 6.8% in the inlet temperature, the liquid mass fraction decreased by more than 2%. With an increase of about 12.5% in the inlet pressure under the same conditions, the maximum liquid mass fraction increased by more than 19%. Considering natural gas as the inlet fluid of the separator, all water vapor is converted to liquid and the separation efficiency is very high.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".