Development of a sorption-based Joule-Thomson cooler for the METIS instrument on E-ELT
Bibliographic record
Abstract
METIS, the Mid-infrared E-ELT Imager and Spectrograph, is one of the proposed instruments for the European Extremely Large Telescope (E-ELT) that will cover the thermal/mid-infrared wavelength range from 3-14 m. Its detectors and optics require cryogenic cooling at four temperature levels, 8 K for the N-band detectors, 25 K for the N-band imager, 40 K for the L/M-band detectors and 70 K for the optics. To provide cooling below 70 K, a vibration-free cooling technology based on sorption coolers is developed at the University of Twente in collaboration with Airbus Defence and Space Netherlands B.V. (former Dutch Space B.V.). We propose a sorption-based cooler with three cascaded Joule-Thomson (JT) coolers of which the sorption compressors are all heat sunk at the 70 K platform. A helium-operated cooler is used to obtain the 8 K level with a cooling power of 0.4 W. Here, three pre-cooling stages are used at 40 K, 25 K and 15 K. The latter two levels are provided by a hydrogen-based cooler, whereas the 40 K level is realized by a neon-based sorption cooler. To validate the designs, three demonstrators were built and tested: 1. Full-scale 8 K helium JT cold stage; 2. Scaled helium sorption compressor; 3. Scaled 40 K neon sorption JT cooler. In this paper, we present the design of these demos. We discuss the experiment results obtained so far, the lessons that were learned from these demos and the future development towards a real METIS cooler.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".