Experimental Investigation of Steam Ejector System With an Extra Low Generating Temperature
Bibliographic record
Abstract
A steam ejector system is environmentally friendly but limited to utilizing thermal energy with a temperature typically ranging from 100 °C to 200 °C. As the steam generating temperature decreases, the utilization of the thermal energy from such a low-temperature heat source becomes very challenging. In this investigation, an experimental steam ejector system was designed and constructed to investigate the performance of the ejector system with water as the working fluid at steam generating temperatures ranging from 40 °C to 60 °C. A convergent nozzle and a de Laval nozzle were used in the steam ejector as the primary nozzles, respectively. The experimental results show that the steam ejector at a generating temperature ranging from 40 °C to 60 °C can function. The performance of the convergent nozzle is a little better than that of the de Laval nozzle in most cases at the given working condition. For power plant or desalination system applications, the system coefficient of performance (COP) of the ejector with convergent nozzle could reach 3.06 when the steam generating temperature is 40 °C and the evaporator temperature is 25 °C. For refrigeration application, the ejector with a de Laval nozzle can achieve a system COP of 0.21 and 0.4 at a generating temperature of 60 °C. The results of this investigation enabled a better understanding of system performance characteristics in a steam ejector system at a generating temperature below 100 °C.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".