Design of an Autonomic Software System for Dragonfly’s Wind Tunnel
Bibliographic record
Abstract
The Dragonfly mission – designed to explore Titan, Saturn’s largest moon – demands cutting-edge control and instrumentation systems to navigate the dense atmosphere and varied terrain, characterized by low gravity, extreme cold, and diverse surface features. At the core of this effort is the development of a highly-adaptive wind tunnel system, meticulously engineered to meet evolving performance testing requirements while generating high-fidelity data crucial for rotorcraft design. These data support the development and validation of advanced exploratory guidance, navigation, and control (GNC) algorithms and computational fluid dynamics (CFD) models, enabling Dragonfly to perform autonomous flight and scientific exploration in an uncharted extraterrestrial environment. To achieve these ambitious goals, the wind tunnel system leverages principles of autonomic computing, integrating self-monitoring, self-configuring, and self-optimizing capabilities to enhance its adaptability. These autonomic features ensure that the system can dynamically adjust to varying test conditions, such as integrating and instrumenting a variety of sensor types with unique signal conditioning needs, seamless switching between different communication protocols, and adjustments to rotorcraft configurations, without the need for constant human intervention or a complete system rearchitecture. Furthermore, the application of adaptable system design principles not only facilitates the creation of this unprecedented testing infrastructure but also ensures its flexibility to accommodate future refinements and mission-specific requirements. This paper details how these design principles are applied, enabling the successful implementation of a highly innovative and versatile system that aligns with the evolving needs of the Dragonfly mission.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".