Modeling and Simulation of Supersonic Natural Gas Dehydration using De Laval Nozzle
Bibliographic record
Abstract
The purpose of this report is to provide an overview of the writer’s Final Year Project. Current techniques in dehydration of natural gas, such as absorption, adsorption and membrane require relatively large facilities, a large investment, complex mechanical work, and the possibility of having a negative impact on the environment. Separation with supersonic flow is proposed as a solution to some of the disadvantages of conventional methods. The objectives of the project is to perform simulation which model natural gas flow through a convergent-divergent nozzle which separates water from natural gas and study pressure and temperature drop as well as the effectiveness of the separation. FLUENT and GAMBIT are the major tool used in running the simulation. Simple explanation on the methods is provided in this report. Gas is accelerated up to velocities exceeding the sound propagation velocity in gas through a convergent-divergent nozzle due to transformation of a part of the potential energy of flow to kinetic energy the gas is cooled greatly. The result of the simulation shows velocity of gas increases significantly at the choke, resulting in temperature drop which condenses water vapour in the gas mixture. By removing water liquid droplets, water content in system can be reduced. Temperature, pressure, velocity and component mass fraction profiles are included in the report. Furthermore, effects of different inlet mass flow rate are studied. Higher inlet mass flow rate increases temperature drop, hence more water vapour is condensed and lower water content left in natural gas. For effective separation, sufficient inlet mass flow rate is required to achieve sonic flow in a 3-inch pipeline. Recommendations for future work expansion and continuation are provided at the end of the report.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".