Numerical Study of Iced Swept-Wing Performance Degradation using RANS
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper studies the level of confidence and applicability of CFD simulations using steady-state Reynolds-Averaged Navier-Stokes (RANS) in predicting aerodynamic performance losses on swept-wings due to contamination with ice accreted in-flight. The wing geometry selected for the study is the 65%-scale Common Research Model (CRM65) main wing, for which NASA Glenn Research Center’s Icing Research Tunnel has generated experimental ice shapes for the inboard, mid-span, and outboard sections. The reproductions at various levels of fidelity from detailed 3D scans of these ice shapes have been used in recent aerodynamic testing at the Office National d’Etudes et Recherches Aérospatiales (ONERA) and Wichita State University (WSU) wind tunnels. The ONERA tests were at higher Reynolds number range in the order of 10 million, while the WSU tests were in the order of 1 million. RANS CFD results for the lower-Reynolds 8.9% WSU model up to α = 10° were previously generated by University of Virginia and NASA Glenn Research Center using Ansys Fluent and k-ω SST on the clean and low-fidelity (smooth) iced wings. The current CFD study focuses on the high-fidelity ice shape, presenting RANS results for both low and high Reynolds numbers, for the full α sweep up to α = 25° for which there is experimental data on the iced wings. A straight-forward automatable CAD-to-solution workflow is established using surface curvature and proximity-based mesh sizing functions for grid generation. The simulations results include C<sub>L</sub>, C<sub>M</sub>, C<sub>D</sub> curves for both clean and iced wings, their comparison to the experimentally obtained counterparts, iced surface Cp distributions, and a detailed breakdown of the computational cost and user effort in generating these solutions.</div></div>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".