Optimization of Tuneable Emittance Smart Coatings for Thermal Control in Small Satellites
Bibliographic record
Abstract
MPB has developed advanced technologies based on smart radiator devices with thin-film tiles (SRDs) employing V1-x-yMxNyOn, for the passive dynamic thermal control of space structures and payloads. M and N dopants tailor the transition temperature characteristics of the tuneable IR emittance. The SRD has successfully passed major ground tests and validated its performance for extended use in the harsh space environment, with a target of up to 15 years GEO, in preparation for a flight demonstration of the technology [1]. This paper describes the optimization and validation of the SRD as an efficient thermal control system with tuneable thermo-optical properties for a microsat mission. The optimization involves tailoring the transition temperature characteristics of the tuneable IR emittance to near room temperature by using Tungsten-doped Vanadium targets for the deposition of V1-xWxOn. An additional layer is being investigated in order to bias the emittance tuneability upwards while maintaining the emittance tuneability. This will minimize the required radiator area for a given heat load. A multiple layer coating based on quarter-wavelength (e.g. SiOn/VOn) selective solar reflector is being employed to minimize the solar optical absorption that has a peak intensity near 500 nm. MPB's passive thin-film SRD 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 but with the added benefits of dynamic variation in the thermal radiation to space to significantly improve the thermal stability of the spacecraft for varying operating conditions at a significant mass and power savings relative to traditional techniques.
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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 | 0.000 |
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".