Comparison of the Net Work Output between Stirling and Ericsson Cycles
Bibliographic record
Abstract
In this paper, we compare Stirling and Ericsson cycles to determine which engine produces greater net work output for various situations. Both cycles are for external heat engines that utilize regenerators, where the difference is the nature of the regeneration process, which is constant volume for Stirling and constant pressure for Ericsson. This difference alters the performance characteristics of the two engines drastically, and our comparison reveals which one produces greater net work output based on the thermodynamic parameters. The net work output equations are derived and analysed for three different scenarios: (i) equal mass and temperature limits; (ii) equal mass and pressure or volume; and (iii) equal temperature and pressure or volume limits. The comparison is performed by calculating when both cycles produce equal net work output and then analysing which one produces greater net work output based on how the parameters are varied. In general, the results demonstrate that Stirling engines produce more net work output at higher pressures and lower volumes, and Ericsson engines produce more net work output at lower pressures and higher volumes. For certain scenarios, threshold values are calculated to illustrate precisely when one cycle produces more net work output than the other. This paper can be used to inform the design of the engines and to determine when a Stirling or Ericsson engine should be selected for a particular application.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".