Investigation of thermo-physical fluid properties effect on binary fluid ejector performance
Why this work is in the frame
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Bibliographic record
Abstract
Supersonic Ejector (SE) is a thermally-driven fluidic compressor that replaces the electro-mechanical compressor in Reverse-Rankine refrigeration/heat pump cycles. These widely used thermal cycles account for billions of kWh of electric energy and produce hundreds of millions of metric tons of atmospheric carbon yearly in North America. As compared to mechanical compressors, ejectors are simple mechanical devices with no moving parts. It can be configured to provide residential and commercial space heating/cooling and water heating, industrial process heating/cooling, industrial distillation/desalination and drying. Rather than electricity, SE-based systems can make direct use of many forms of thermal energy including solar thermal, waste heat, biogas, or natural gas, depending on emission targets, price, or availability. It is known that the SE systems have a lower thermal efficiency as compared to mechanical compressor because of its lower performance at high compression ratios. Highly efficient ejector would thus play a critical role in unlocking the wide spread use of renewable energy such as waste heat, solar thermal, and geothermal. Even in the absence of renewable energy, such a device would enable fuel switching from electricity to natural gas, which would save 65 to 75% on energy costs, and relieve the power grid during peak times. In the present study, Computational Fluid Dynamics (CFD) is used to study the effect of fluids thermo-physical properties including molecular mass, viscosity and specific heat ratio on the performance of an ejector for distillation applications. It is found that molecular mass and specific heat ratio can significantly affect the entrainment ratio of the ejector and consequently the COP of the refrigeration system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it