Efficient reduced-order modeling of unsteady aerodynamics under light dynamic stall conditions
Bibliographic record
Abstract
In this research, a reduced-order modeling is developed to predict the unsteady aerodynamic forces under light dynamic stall conditions at low-speed regimes. The filtered white Gaussian noise is selected as input signals for computational fluid dynamics solver in order to generate training data, containing the information of reduced frequency and amplitude. Because of the time history influences, the reduced-order modeling combines the Kriging function and recurrence framework together in this approach. An airfoil NACA0012 undergoing pitching motions with different reduced frequency, amplitude, and mean angle of attack is designed to illustrate the methodology. The developed model can predict the lift, drag, and moment coefficients in seconds on a single-core computer processor. To reduce the prediction errors between reduced-order modeling predictions and computational fluid dynamics simulations, the aerodynamic loads in static conditions are applied as initial inputs. The predictions via the proposed approach are in agreement with the results using a high precision computational fluid dynamics solver over the designed ranges of amplitude and reduced frequency, which is suitable for engineering applications, such as fluid-structure interaction, and aircraft design optimizations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".