Increasing Boiling Fluid Flowing Efficiency from Motive Nozzles of Two-Phase Ejectors
Bibliographic record
Abstract
Abstract The article contains the possibility of increasing boiling fluid flowing efficiency from expanding channels. This process takes place in the motive flow nozzle of a liquid-vapor ejector, working on the principle of thermal stream compression. Efficiency increasing by profiling the diffuser part of the nozzle. Modern industry uses nozzles, which are like de Laval nozzles, with straight walls of the diffusers. The authors suggest paying closer attention to profiling these nozzles, which might increase their efficiency and improve their gas-dynamic characteristics. For comparison, we choose a channel of a traditional form (with straight walls of the diffuser) and a channel of parabolic shape. The article contains a mathematical model to calculate the process of flowing the boiling fluid from the authors-designed channels – the peculiarities of this model that appear after changing the geometry of its streaming part. We obtain comparative analysis calculation results based on the mathematical model and the Ansys CFX workflow model. As a result of numerical calculation using the authors mathematical model and modelling in the Ansys CFX software package, it concludes that the parabolic shape of the diffuser is the most favourable. In the boiling process, the liquid central core is boiling at the optimum distance from the nozzle throat, and the flow of a stable vapor structure with the required pressure value for each regime forming at the outlet.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".