Investigation on ejector design for CO2 heat pump applications us-ing Dymola
Bibliographic record
Abstract
In this paper, the Dymola modelling tool is used to study the influence of ejector design onto the whole heat pump cycle working with carbon dioxide. The cycle is built using the components provided by the TIL Modelica library. It is found that the ejector models in TIL are quite limited, namely by their inability to properly capture the on-design plateau and rapid decrease in performance in off-design operation. Therefore, an in-house state-of-the-art ejector model, originally developed in Python, is implemented as a Dymola object. This model is then calibrated onto CO2 experimental data. The operation of a simple CO2 heat pump system is investigated, with focus on the ejector sizing at fixed geometry. It is found that there exists an ejector size that maximises the COP of the cycle. Furthermore, critical ejector pressure is not reached at this optimum COP point; the ejector is operating well under the on-design regime.
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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) | 0.001 | 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) | 0.003 | 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".