Bibliographic record
Abstract
The Performance Error Budget of the Thirty Meter Telescope (TMT) suggests that nearly one third of the total image degradation is due to aero-thermal disturbances (mirror and dome seeing, dynamic wind loading and thermal deformations of the optics, telescope structure and enclosure). An update of the current status of aero-thermal modeling and Computational Fluid-Solid Dynamics (CFSD) simulations for TMT is presented. A fast three-dimensional transient conduction-convection-radiation bulk-air-volume model has also been developed for the enclosure and selected telescope components in order to track the temperature variations of the surfaces, structure and interstitial air over a period of three years using measured environmental conditions. It is used for Observatory Heat Budget analysis and also provides estimates of thermal boundary conditions required by the CFD/FEA models and guidance to the design. Detailed transient CFSD conjugate heat transfer simulations of the mirror support assemblies determine the direction of heat flow from important heat sources and provide guidance to the design. Finally, improved CFD modeling is used to calculate wind forces and temperature fields. Wind loading simulations are demonstrated through TMT aperture deflector forcing. Temperature fields are transformed into refractive index ones and the resulting Optical Path Differences (OPDs) are fed into an updated thermal seeing model to estimate seeing performance metrics. Keck II simulations are the demonstrator for the latter type of modeling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
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".