A study on scroll compressor conversion into expander for Rankine cycles
Bibliographic record
Abstract
In this paper, we investigate using a refrigeration scroll compressor as expander for power generation applications with a Rankine cycle. The methodology employed here has three steps: In the first step, a scroll compressor is selected from a refrigeration manufacturer catalog. Based on catalog data and our simplified model, the specific parameters of the compressor such as the built-in volume ratio and leakage coefficient are determined through mathematical regression. In the second step, the parameters and the efficiency of the Rankine cycle are determined, which use the selected scroll machine in reverse, namely as expander, without any geometrical modifications. The range of temperatures and pressures are kept the same as that characterizing the compressor operation. A simplified expander model is used to predict the efficiency of the prime mover and of the Rankine cycle. A range of working fluids are considered and compared. The expander does not operate optimally when converted from a compressor without any modifications. In the third phase, the geometry of the expander is modified with respect to the rolling angle only in order to obtain the appropriate built-in volume ratio which assures better efficiency of the Rankine heat engine. This paper also presents a parametric study in terms of geometry, working fluid and operating 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".