Design and Investigation of a Dynamic Auto-Adjusting Ejector for the MED-TVC Desalination System Driven by Solar Energy
Bibliographic record
Abstract
Ejectors have been widely used in multi-effect distillation, thermal vapor compression (MED-TVC) desalination systems due to their simple structures and low energy consumption. However, traditional fixed geometry ejectors fail to operate under unstable working conditions driven by solar energy. Herein, a dynamic auto-adjusting ejector, equipped with a needle at the nozzle throat, is proposed to improve the ejector's performance under changeable operating conditions. A two-dimensional computational fluid dynamics (CFD) model is built to analyze the performance and flow field of the ejector. It is found that the achievable entrainment ratio gradually increases as the needle approaches the nozzle, and the entrainment ratio of the ejector is relatively stable, varying slightly between 1.1-1.2 when the primary pressure changes from 2.5 to 4 bar. Besides, the performance comparison between the proposed ejector and the traditional ejector is studied under the same primary pressure range. The entrainment ratio of the designed ejector was 1.6 times higher than that of the conventional ejector at a primary pressure of 2.5 bar. Furthermore, the average entrainment ratio of the designed ejector is 1.14, as compared to 0.84 for the traditional ejector. Overall, the proposed auto-adjusting ejector could be potentially used in MED-TVC desalination systems under variable conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".