Dragonfly CFD Validation and Uncertainty Quantification
Bibliographic record
Abstract
This work demonstrates an application of Computational Fluid Dynamics (CFD) model validation and model-form uncertainty quantification for the NASA Dragonfly lander in its Preparation for Powered Flight (PPF) configuration. A multi-fidelity CFD workflow is employed, utilizing both mid-fidelity Reynolds-Averaged Navier-Stokes (RANS) and high-fidelity Improved Delayed Detached Eddy Simulation (IDDES) models. Surrogate models are trained from CFD simulations to ingest operational condition uncertainty and propagate uncertainty to loads of interest. An area metric validation quantity is used to establish model-form uncertainty. FZ area metric values based on RANS CFD results alone indicate high discrepancy, >12 N, near pure descent orientations, high descent velocity, and high lander rotor RPM. These errors are reduced to <4 N through targeted IDDES simulations. MZ error is consistent between the different model fidelities considered. The results demonstrate the effectiveness of the proposed procedure in quantifying model-form uncertainty and identifying need for increased model fidelity for downstream vehicle performance simulations.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".