Naturally-Forced Slug Flow Expander for Application in a Waste-Heat Recovery Cycle
Bibliographic record
Abstract
This paper investigates a slug-flow expander (SFE) for conversion of high-pressure gas/vapor into kinetic energy of liquid slugs. The energy transfer from high-pressure to kinetic energy is quantified using thrust plate measurements. Non-dimensional thrust data is used to quantify performance by normalizing measured thrust by thrust for the same water flow rate at zero air flow rate. A total of 13 expander configurations are investigated and geometries with the shortest cavity length and the smallest exit diameter are found to result in the largest non-dimensional thrust increase. Results show that thrust augmentation increases with the initiation of slug flow in the SFE. The analysis performed on the normalized thrust readings suggested that as the water and air flow were increased to critical conditions, the liquid slugs produced by the SFE augmented the thrust measurements. The final performance evaluation was based on linear regression of the normalized thrust measurements where slug flow was generated for each SFE architecture. Greater magnitudes of the slope from the linear regression indicated the propensity of the SFE to augment thrust. This analysis confirmed that for the SFE configurations over the range of values investigated, the SFE increased thrust up to three times its original value at no air flow. Given the inherent multiphase nature of the slug-flow expander, application to systems involving expansion of wetting fluids (water as part of a waste-heat recovery system) or air with water droplet formation (as part of a compressed-air energy storage system) could be considered.
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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 | 0.000 |
| Open science | 0.000 | 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".