Auto-tuned thermal control on stratospheric balloon experiments
Bibliographic record
Abstract
Balloon-borne experiments present unique thermal design challenges, which are a combination of those present for both space and ground experiments. Radiation and conduction are the predominant heat transfer mechanisms with convection effects being minimal and difficult to characterize at 35-40 km. This greatly constrains the thermal design options and makes predicting flight thermal behaviour very difficult. Due to the limited power available on long duration balloon flights, efficient heater control is an important factor in minimizing power consumption. SuperBIT, or the Super-Pressure Balloon-borne Imaging Telescope, aims to study weak gravitational lensing using a 0.5m modified Dall-Kirkham telescope capable of achieving 0.02" stability<sup>1</sup> and capturing deep exposures from visible to near UV wavelengths. To achieve the theoretical stratospheric diffraction-limited resolution of 0.25",<sup>2</sup> mirror deformation gradients must be kept to within 20 nm. The thermal environment must be stable on time scales of an hour and the thermal gradients on the telescope must be minimized. During its 2018 test-flight, SuperBIT will implement two types of thermal parameter solvers: one for post-flight characterization and one for in-flight control. The payload has 85 thermistors as well as pyranometers and far-infrared sensors which will be used post-flight to further understand heat transfer in the stratosphere. This document describes the in-flight thermal control method, which predicts the thermal circuit of components and then auto-tunes the heater PID gains. Preliminary ground testing shows the ability to control the components to within 0.01 K.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".