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Record W2052641659 · doi:10.1117/12.828057

Thermal performance prediction of the TMT telescope structure

2009· article· en· W2052641659 on OpenAlexfundno aff
Myung‐Haing Cho, Andrew Corredor, Konstantinos Vogiatzis, George Z. Angeli

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaNational Research Council CanadaResearch and Innovation FoundationGordon and Betty Moore Foundation
KeywordsThermalTelescopeFinite element methodMaterials scienceThermal analysisHeat fluxMechanical engineeringStructural engineeringOpticsMechanicsHeat transferEngineeringPhysicsMeteorology

Abstract

fetched live from OpenAlex

Thermal analysis for the Thirty Meter Telescope (TMT) structure was performed using finite element analysis in ANSYS and I-DEAS. In the thermal analysis, the telescope structural parts with simplified optical assembly systems were modeled for various thermal conditions including air convections, conductions, heat flux loadings, and radiations. Thermal responses of the TMT telescope structure were predicted and the temperature distributions of the optical assembly systems were calculated under sample thermal loading conditions. The thermo-elastic analysis was made to obtain the thermal deformation based on the resulting temperature distributions. The line of sight calculation was made using the thermally induced structural deformations. The goal of this thermal analysis is to establish thermal models by the FEA programs to simulate for an adequate thermal environment. These thermal models can be utilized for estimating the thermal responses of the TMT structure. Thermal performance prediction of the TMT structure will be able to guide us to control and maintain the system from the "seeing" effects.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.201
Teacher spread0.194 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdaptive optics and wavefront sensingFrench-language works237,207