Thin-film Smart Radiator Tiles With Dynamically Tuneable Thermal Emittance
Bibliographic record
Abstract
This paper describes recent advances in MPB's approach to spacecraft thermal control based on a passive thin-film smart radiator tile (SRT) that employs a variable heat-transfer/emitter structure. This can be applied to Al thermal radiators as a direct replacement for the existing OSR (optical second-reflector) radiator tiles with a net added mass under 100 gm/m2. The SRT employs a smart, integrated thin-film structure based on the nano-engineering of V1-x-yMxNyOn that facilitates thermal control by dynamically modifying the net infrared emittance passively in response to the temperature of the space structure. Dopants, M and N, are employed to tailor the transition temperature characteristics of the tuneable IR emittance. This facilitates thermal emissivities below 0.3 to dark space at lower temperatures that enhance the self-heating of the spacecraft to reduce heater requirements. As the spacecraft temperature increases above the preselected temperature setpoint, the thermal emissivity of the SRT to dark space gradually increases. Broad IR emittance dynamic tuneabilities exceeding 0.45 have been achieved. The overall coating structure is substantially simpler than typical electrochromic devices and avoids the use of volatile charge-storage layers. The thin-film SRT methodology has significant advantages over competitive technologies in terms of weight, cost, power requirements, structural simplicity and reliability with no mechanical components, and integration with the space structure. In the space environment, such as low Earth orbit (LEO), the coating will be subject to various stresses including VUV radiation and atomic oxygen (AO). AO testing in a simulated environment at the Canadian Space Agency (CSA) indicate no resolvable change in the morphology or mass of the SRT coating after exposure to AO equivalent to three years in low Earth orbit (LEO). The thermo-optic characteristics after AO exposure were similar to the “as deposited” characteristics. Work is currently underway to experimentally validate the expected performance for extended use up to 15 years GEO. In preliminary cyclic testing, there was no change in the emittance tuneability after about 3,500 temperature-induced cycles between the low and high emittance states.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".