Switchless Sorption-Compressor Design
Bibliographic record
Abstract
Sorption coolers are free of vibrations and EMI, and have the potential of a long lifetime. They are, therefore, an attractive for cooling sensitive optical detector systems in space, as well as in ground applications. Generally, gas-gap heat switches are utilized for reducing the input power in the desorption phase of the compressor sorption cycle. This is specifically required in space applications since the compressor cells are cooled by radiators which are proportional in size to the compressor input power. In terrestrial applications, such as the METIS instrument in the European Extremely Large Telescope, low input powers may not be the major concern. Especially in applications requiring both a high cooling powers and a large number of compressor cells, complexity and cost may become more important. In order to reduce these, we developed an alternative switchless sorption-compressor design using short-pulse heating. A dynamic thermal model was built for evaluating this concept and for optimizing the compressorcell dimensions and operating parameters. This paper describes the design of compressor cells with and without heat switches, related to the METIS cooler. Furthermore, it presents experimental results on a switchless compressor that were used for validating a dynamic model. In the paper, the impact of the switchless compressor design on the METIS cooler configuration is also 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".