Optimization of gas–liquid two-phase flow law and structural parameters of Laval supersonic atomizing nozzle
Bibliographic record
Abstract
In order to get rid of the dilemma of gas well fluid accumulation in the gas field, we propose an innovative scheme of using Laval nozzle as a downhole atomizing nozzle for atomized drainage gas extraction. First, the preliminary design of the atomizing nozzle is carried out by combining the actual production situation of the gas field with the working principle of the Laval nozzle. Second, the single-phase flow of gas inside the Laval nozzle as well as the law of GLP (gas–liquid two-phase flow) was studied through CFD (computational fluid dynamics) numerical simulation calculations, focusing on analyzing key parameters such as flow rate, LVF (liquid-phase volume fraction), and pressure. The results show that the GLP field has similar characteristics to the single-phase flow field of gases, in which the flow velocity can be as high as the supersonic speed of 1060 m/s. However, the instability of the flow is enhanced, and the turbulent kinetic energy and energy dissipation are significantly increased. Inside the nozzle, LVF was maintained at a stable level of 0.008 34%, which verifies the feasibility of the Laval nozzle for downhole applications. Finally, the optimal structural parameters of the atomizing nozzle were obtained using the maximum volume fraction of the liquid phase, the effective conversion efficiency of the energy, the pressure change, and the change of the Mach number as the evaluation criteria: the length of the contraction section was 70 mm, the expansion angle was 10°, the expansion scale was 9.6 mm, and the Mach number of the outlet cross section was 2. This study provided theoretical guidance and practical basis for the field application of the Laval nozzle on the atomized dewatering gas extraction tool, and it also provided a good basis for future research and technological development.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".