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Record W1537001953 · doi:10.4271/2003-01-2472

Passive Dynamically-Variable Thin-film Smart Radiator Device

2003· article· en· W1537001953 on OpenAlexafffund
Roman V. Kruzelecky, E. Haddad, Wes Jamroz, M. Soltani, Mohamed Chaker, Darius Nikanpour, Xin Xian Jiang

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2003
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsCanadian Space AgencyInstitut National de la Recherche Scientifique
FundersCanadian Space Agency
KeywordsRadiator (engine cooling)Computer scienceVariable (mathematics)Materials scienceOptoelectronicsOpticsPhysics

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">This paper describes a new approach to spacecraft thermal control based on a passive thin-film smart radiator device (SRD) that employs a variable heat-transfer/emitter structure. The SRD employs an integrated thin-film structure based on V<sub>1-x-y</sub>M<sub>x</sub>N<sub>y</sub>O<sub>n</sub> that can be applied to existing Al thermal radiators. The SRD operates passively in response to changes in the temperature of the space structure. The V<sub>1-x-y</sub>M<sub>x</sub>N<sub>y</sub>O<sub>n</sub> exhibits a metal/insulator transition with temperature, varying from an IR transmissive insulating state at lower temperatures, to a semiconducting state at higher temperatures. Dopants, M and N, are employed to tailor the thermo-optic characteristics and the transition temperature of the passive SRD. The transition temperature can be preset over a wide range from below -30°C to above 68°C using suitable dopants. A proprietary SRD structure has been developed that facilitates emissivities below 0.2 to dark space at lower temperatures to reduce heater requirements. As the spacecraft temperature increases above the selected transition temperature, the thermal emissivity of the SRD to dark space increases by a factor of 2.5 to 3. The thin-film SRD methodology has significant advantages over competitive technologies in terms of weight, cost, power requirements, mechanical simplicity and reliability Preliminary results on an active electrochromic SRD based on the VO<sub>2</sub> system are also presented.</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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.010
GPT teacher head0.234
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; both teacher heads agree on what is shown here.

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

Citations10
Published2003
Admission routes2
Has abstractyes

Explore more

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