Modeling and Analysis of Scroll Compressor Conversion into an Expander for Rankine Cycles
Bibliographic record
Abstract
In this article, we analyze through a model the use of a refrigeration scroll compressor as expander for power generation applications with small-power Rankine cycle. A scroll compressor is selected from a refrigeration manufacturer catalog used for the study. Based on catalog data and our model the specific parameters of the compressor such as built-in volume ratio and leakage coefficient are determined through mathematical regression. The expander model is used to predict the efficiency and other important parameters of the scroll expander. The expander operation within Rankine cycle is studied and compared for several working fluids. The expander does not operate optimally when converted from a compressor without any modifications. Therefore it is developed a method and a code for determining the geometry of the expander with respect to rolling angle in order to obtain the built-in volume ratio which assures better efficiency of the Rankine heat engine. The article reports a parametric study with respect to scroll geometry, working fluid, and operating conditions. The optimum rotational speed varies from 1,500–2,500 RPM depending on operating pressure. The method of conversion of scroll compressor into expander may be useful in development of cost-effective expanders for small scale Rankine cycles for power and heating generation from renewable energy resources.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".