The Evolution of Dragonfly's Design Using CFD at Titan
Bibliographic record
Abstract
This paper presents the development and application of a computational fluid dynamics (CFD) modeling approach for the Dragonfly rotorcraft lander, a NASA New Frontiers mission to study prebiotic chemistry on Titan, Saturn's largest moon. The primary CFD approach uses Siemens Digital Industries Simcenter STAR-CCM+ to generate a large database of aerodynamic loads for various flight phases, including Preparation for Powered Flight (PPF), Transition to Powered Flight (TPF), and surface flights. The mid-fidelity CFD approach relies on a steady-state Reynolds Averaged Navier Stokes (RANS) and Virtual Disk Blade Element Momentum Theory (BEMT) model to produce the aerodynamic loads for more than 3000 flight conditions. The CFD was used with Gaussian Process Regression (GPR) to create a surrogate model for predicting aerodynamic loads, aerodynamic performance, handling qualities and control margins; the surrogate is queried over 10 billion times during flight dynamics analyses. Higher fidelity CFD runs, using CREATE-AV Helios, were conducted to build confidence in the midfidelity approach and understand its limitations. The results demonstrate the effectiveness of the CFD approach in supporting the design and maturation of the Dragonfly lander from Preliminary Design to Critical Design and provide valuable insights into the complex aerodynamics of the vehicle during various flight phases.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".