Exergetic Optimisation of Vortex Tubes using a Thermodynamic Model
Why this work is in the frame
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Bibliographic record
Abstract
This article identifies sources of exergy losses in a vortex tube working with air by using a recently developed thermodynamic model and a reference experiment from the literature. Exergetic efficiency considering transiting exergy is used as the efficiency metrics in this work. When both the cold and hot outlets are useful, the exergetic efficiency reaches its maximum value for a cold mass fraction equal to 0.7. Interestingly, up to 45% of the inlet exergy is lost downstream of the vortex tube under this condition because of pressure losses in the cold tube and through measuring instruments. These losses do not contribute to the energy separation mechanism. Inside the vortex tube, the exergy irreversibly is mainly caused by the dissipation of kinetic exergy. The thermodynamic model is also used to identify the working conditions, which maximize the vortex tube efficiency. The efficiency is always at its maximum value when the inlet Mach number is equal to one. The optimum value of the cold outlet diameter, the mass fraction and the cold outlet axial Mach number changes depending on whether thermal exergy from both outlets can be used or not. Increasing the cold outlet pressure increases the exergetic efficiency as well as changing the optimal condition for all variables except the inlet Mach number. At the end, the optimal vortex tube is twice as efficient as the reference vortex tube. Finally, the model is employed to identify the best vortex tubes’ arrangement to maximize the exergetic efficiency for an open cycle with a fixed inlet pressure of six bar. This analysis demonstrates that the best arrangement is a cascade of vortex tubes, where a vortex tube unit with the maximum efficiency is placed first. Two other vortex tubes are two other vortex tubes are placed to recover waste pressure on the cold and hot streams from the first unit.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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