Grid study for Delayed Detached Eddy-Simulation's grid of a pre-stalled wing
Bibliographic record
Abstract
For wing in medium or deep stalled configuration, strong vortices occur whereas the boundary layer still affects the aerodynamic coefficients' results. In the past recent years, RANS' model (Reynolds-Averaged Navier-Stokes) has been widely used to predict aerodynamic phenomena, but it showed its weakness in predicting the modulation in vortex shedding (Forsythe, Squires, Wurtzler, & Spalart, 2004; Liang & Xue, 2014). Concomitantly Large Eddy Simulation (LES) succeeds in modelling eddy phenomena, while it fails predicting boundary-layer's phenomena with the current computation's power (Mockett, 2009). Using the advantage of both methods, Delayed Detached Eddy-Simulation (DDES) shows better results, but the solutions given seem to show more sensitivity to grid refinement than RANS or LES (Forsythe et al., 2004). In order to spare time and resources while increasing the results' accuracy of the stalled wing configuration's aerodynamic coefficients, this study offers a parametric grid study for the DDES model. For three different grid refinements, characteristics of lift and eddy phenomena are presented and compared to determine, for an infinite wing, the best compromise between time and resources' consumption, and results' accuracy. Using the open software SU2 6.1 (Stanford University Unstructured), we generate three different types of grid refinements around an airfoil, developed spanwise to obtain a straight wing. On the same stalled configuration for each mesh, CFD solutions are ran with the DDES model, and the raw data are postprocessed with the open software ParaView 5.6. We then compare the aerodynamic coefficients' distributions obtained by the three mesh. The general modelling of vortex shedding's topology and turbulence viscosity are compared with the literature to ensure the right rendering of vortex structures. Chordwise pressure and friction coefficients' distributions as well as the spanwise lift coefficient are also compared. We conclude with the optimum mesh in term of results and resources' consumption.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".