Dimensional stability performance of a CFRP sandwich optical bench for microsatellite payload
Bibliographic record
Abstract
Microsatellite market requires high performance while minimizing mass, volume and cost. Telescopes are specifically targeted by these trade-offs. One of these is to use the optomechanical structure of the telescope to mount electronic devices that may dissipate heat. However, such approach may be problematic in terms of distortions due to the presence of high thermal gradients throughout the telescope structure. To prevent thermal distortions, Carbon Fiber Reinforced Polymer (CFRP) technology can be used for the optomechanical telescope material structure. CFRP is typically about 100 times less sensitive to thermal gradients and its coefficient of thermal expansion (CTE) is about 200 to 600 times lower than standard aluminum alloys according to inhouse measurements. Unfortunately, designing with CFRP material is not as straightforward as with metallic materials. There are many parameters to consider in order to reach the desired dimensional stability under thermal, moisture and vibration exposures. Designing optomechanical structures using CFRP involves many challenges such as interfacing with optics and sometimes dealing with high CTE mounting interface structures like aluminum spacecraft buses. INO has designed a CFRP sandwich telescope structure to demonstrate the achievable performances of such technology. Critical parameters have been optimized to maximize the dimensional stability while meeting the stringent environmental requirements that microsatellite payloads have to comply with. The telescope structure has been tested in vacuum from -40°C to +50°C and has shown a good fit with finite element analysis predictions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".