Modeling and experimental characterization of a tethered spherical aerostat
Bibliographic record
Abstract
Tethered helium balloons are known to be useful in applications where a payload must be deployed at altitude for a long duration. Perhaps the simplest such system is a helium-filled sphere tethered to the ground by a single cable. Despite its relative simplicity, there exists little data about light tethered spheres in a fluid stream. The current work focuses on an investigation of the dynamic characteristics of a spherical aerostat on single tether. A test facility was constructed to gather the experimental data required for a characterization of the system. The balloon's drag coefficient is extracted from the position measurements. Our experiments were all in the supercritical range that is, at Reynolds numbers greater than 3.7 x 105. We find that the balloon's large oscillations and surface roughness combined with the wind turbulence result in a substantial increase in the drag coefficient. A model of the dynamics of a spherical aerostat was previously developed at McGill University and our experimental data was used to refine and improve that simulation. The aerostat is modeled as a single body attached to the last node of a tether. It is subject to buoyancy, aerodynamic drag and gravity. The tether is modeled using a lumped-mass method. The dynamic simulation of the aerostat is obtained by setting up the equations of motion in 3D space and integrating them numerically. Finally, the model is validated through comparison with experimental data and a modal analysis is performed.
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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 | 0.000 | 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 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".