Fast Analysis of Unsteady Wing Aerodynamics via Stochastic Models
Bibliographic record
Abstract
Lifting surfaces often operate in highly unsteady inflow conditions, such as gusty wind or waves. These inflows are unsteady on many time scales and have to be considered stochastic processes. For fluid dynamics practitioners, this leads to a challenge: how can long-term, random design loads (e.g., fatigue or 20 year return extreme) be quantified efficiently? The conventional approach involves analysis of a large set of short-term inflow realizations and extrapolates the results to long-term loads via their assumed probability distributions. However, this requires separately solving many simulations. This is computationally expensive and presents a handicap, especially in early design stages (optimization), where rapid evaluations of candidate designs and performance gradients are required. To tackle this problem, we introduce two alternative stochastic methods: one based on a Galerkin projection onto Fourier modes, and the other based on a polynomial chaos expansion. This approach enables us to carry the randomness though the solution process to directly obtain a stochastic result. Thus, long-term loads can be directly constructed from the stochastic solution, without having to analyze specific realizations of the inflow inputs. The new processes are illustrated and discussed with an example based on a rectangular wing lifting-line model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".