Dynamic Stall Simulation with Direct-Forcing Immersed Boundary Method
Bibliographic record
Abstract
To predict fluid-structure interactions accurately, a variety of computational methods have been proposed. The most common method to simulate the flow with a complicated solid boundary is to use a body-fitted technique with grids fitting and clustering along the complex boundary. The immersed boundary method is becoming popular since 1972 due to its capability to handle simulations for a moving complex boundary with lower computational cost and memory requirements than the conventional body-fitted method. This method can be categorized as a continuous forcing method in which a forcing term is added to the continuous Navier-Stokes equations before they are discretized. The direct-forcing immersed boundary method (DFIB) is one of the immersed boundary methods. This method uses a virtual forcing term determined by the difference between the interpolated velocities at the boundary points and the desired boundary velocities. The DFIB method with both virtual force and heat source is developed by
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".