Geometrical Optimization of a Steam Jet-Ejector Using the Computational Fluid Dynamics
Bibliographic record
Abstract
The vacuum systems play crucial role in various industries including, but not limited to, power generation, refrigeration, desalination, and aerospace engineering. There are different types of vacuum systems. Among them, the ejector or vacuum pump is highly utilized due to its low capital cost and easy maintenance. Generally, the better operation of a vacuum system can dramatically affect the performance of its upper-hand systems, e.g., the general efficiency of a thermal power plant cycle. This can be achieved if such vacuum systems are correctly designed, implemented, and operated. The focus of this work is on an existing steam jet-ejector, whose primary flow is a high pressure superheated steam and the suction flow is a mixture of steam and air. The main goal of this work is to optimize the geometry of the ejector including the nozzle exit position (NXP), the primary nozzle diverging angle, and the secondary throat length, etc. From the computational fluid dynamics perspective, there are some major challenges to simulate this ejector. It requires predicting the correct turbulent fluid flow and heat transfer phenomena with great complexities in treating the mixed subsonic and supersonic flow regimes, very high and very low pressure regions adjacent to each other, and complex mixing two phase flow jets. Indeed, the latter one has been almost neglected in literature. The main concern of this study is to reduce the consumption of motive steam, i.e., to increase the entrainment ratio via modifying the ejector geometry and investigating its performance under different operating conditions that helps to save the water consumption.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".