Bibliographic record
Abstract
Supersonic separation is a novel technology for natural gas separation. The theoretical design uniquely combines concepts from aerodynamics, thermodynamics, physical separation and fluid-dynamics resulting in an innovative gas conditioning process. It is used to condition the gas by removing condensable vapors and natural gas liquids. The supersonic separator is composed of a converging section, a Laval nozzle and a diverging section.\nNatural gas flows from reservoirs with low velocity and high pressure. In the supersonic separation process, the temperature drops below the dew point of the natural gas. A multiphase flow is formed. Undesired components form liquid condensates that are centrifugally removed through side collection streams.\nThe goal of this work is to develop a one-dimensional thermodynamic numerical model that presents great potential as a fast and accurate tool that enables the simulation of supersonic separators with significant details. The model is to fill certain gaps found in literature with a shortcut modeling technique. This model would best fit the category of preliminary design tools with decreased computational loads.\nThe model was utilized to test several cases for validation. Air, 3-component natural gas and 13-component natural gas mixtures were tested as working fluids at different conditions and nozzle area ratios. Tests included nozzles with and without side streams. The shortcut model demonstrated matching results with previous models from benchmarked studies. The computational load was immensely decreased by reducing the number of locations tested in the diverging nozzle to locate the side streams and the shockwave.\nThe reduction of computational load was demonstrated by decreasing simulation time by 75%-97% depending on the nozzle geometry and conditions. The model proved to be a quick tool suitable for preliminary designs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".