Large Bondspot RTV Adhesion for NFIRAOS OAPs
Bibliographic record
Abstract
The Narrow Field Infrared Adaptive Optics System (NFIRAOS) within the Thirty Meter Telescope (TMT) will unlock new potential for ground-based astronomy. This subsystem is a series of optics that correct for atmospheric turbulence seen in the Infrared wavelength. One of the critical challenges in the NFIRAOS system is the ability to operate at -30 degrees Celsius. The use of RTV (Room Temperature Vulcanizing) silicone as an adhesive allows a more flexible bond between the optic and its mount. This material is capable of withstanding temperature changes without losing bond strength. Additionally, the large Off Axis Parabola (OAP) mirrors provide a unique technical challenge in their mounting configurations. The optics have with a mass of 90 kilograms and must be mounted able to withstand a 50-degree temperature differential from their ambient temperature bonding. This paper builds of initial conceptual and prototyping work done by ABB and provides the next steps scaling towards a final design of large RTV bondspot optical mounting. Through a combination of simulations, iterative prototyping, room temperature and operational temperature stress testing, a final design proposal is presented backed by statistical and in-house life cycle testing methods. The findings in this work have applications as the industry moves towards mounting larger optics in increasingly challenging environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".