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Record W1534032733 · doi:10.4271/2009-01-2575

Multilayer Tuneable Emittance Coatings with Low Solar Absorptance for Improved Smart Thermal Control in Space Applications

2009· article· en· W1534032733 on OpenAlexafffund
E. Haddad, Roman V. Kruzelecky, Brian J. F. Wong, Wes Jamroz, M. Soltani, M. Benkahoul, Mohamed Chaker, P. Poinas

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2009
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersCanadian Space AgencyEuropean Space AgencyPolytechnique Montréal
KeywordsThermal emittanceMaterials scienceAbsorptanceThermalSpace (punctuation)OptoelectronicsAerospace engineeringEngineering physicsOpticsComputer sciencePhysicsMeteorologyReflectivityEngineering

Abstract

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<div class="htmlview paragraph">MPB has developed advanced technologies based on smart radiator thin-film tiles (SRTs) employing V1−x−yMxNyOn, for the passive dynamic thermal control of space structures and payloads. The SRT has passed successfully the 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 this technology This paper describes the optimization of MPB's smart radiator and its validation of an efficient thermal control with the tuneability of thermo-optical properties.</div> <div class="htmlview paragraph">The thermal control of satellites is a critical subsystem that impacts on the performance and longevity of space payloads. MPB has developed advanced smart radiator devices (SRDs) for passive, dynamic thermal control of space structures and payload. The SRDs employ a nano-engineered, thin-film structure based on V1−x−yMxNyOn. Dopants, M and N, tailor the transition temperature of the IR emittance.</div> <div class="htmlview paragraph">Preliminary ground testing of the SRD tiles and assembly was completed. The objectives of the tests are to demonstrate that the VO2 based thin film SRD can withstand the space environment of a GEO Satellite, with a lifetime of 15 years (including the harsh launch conditions), towards its validation as an efficient thermal control device for space applications. A set of environmental tests was performed in order to validate the coating resistance and performance stability in space for a single layer SRD, including extended thermal cycling and thermal shock testing. This layer demonstrated good emittance tuneability (Δε), however, the single layer can exhibit a relatively high solar absorptance (α) at the larger thicknesses needed for a high Δε.</div> <div class="htmlview paragraph">Recently MPB developed a multilayer thin-film structure to decrease the net solar absorptance (α), while maintaining high emittance tuneability. The approach uses a relatively simple thin dielectric stack selective reflector based on SiO2 (e.g. SiO2(λ/4)/VO2 (λ/4) to provide peak reflectance at λ=500 nm, the spectral position of the peak solar AM0 radiation. However depositing each of these additional layers may interfere with the original properties of the lower layers, changing their residual stresses and morphology. This is the main challenge for all the technologies based on multilayer thin or thick films proposed as tuneable emittance devices.</div> <div class="htmlview paragraph">MPB demonstrated the feasibility of reducing the net solar absorptance, without any significant decrease of the emittance tuneability. Six samples based on a three-layer structure on Al (Substrate Al/VO2/SiO2(λ/4)/VO2(λ/4)) were studied. The best sample obtained has an emittance tuneability (Δε) of 0.36 (e-low = 0.38, e-high = 0.74), and a solar absorptance of 0.32. Thermal vacuum cycling (up to 4000 cycles) and thermal shock test (17 cycles between Liquid Nitrogen and 165°C) validated the stability of the emittance tuneability and solar absorptance.</div>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.232
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2009
Admission routes2
Has abstractyes

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