High-order simulation of the Caradonna and Tung rotor in hover
Bibliographic record
Abstract
In this paper, we validate a previously proposed highorder method for simulating unsteady flows for a helicopter rotor in hover.To demonstrate the performance and efficiency of this strategy, three cases have been studied.The first case involves a threedimensional circular cylinder at the onset of the shear-layer transition regime with a Reynolds number of Re = 1000 and Mach number of M = 0.2, while the second case examines the turbulent flow over a 3D SD 7003 airfoil undergoing heaving and pitching motions with a Reynolds number of Re = 10000 and Mach number of M = 0.1.These cases aim to illustrate the accuracy and efficiency of the routine when applied to transitional and turbulent flows.Finally, a hovering model of the Caradonna and Tung helicopter rotor with a tip Mach number of Mt = 0.526, Re = 2.358 × 10 6 , an angular velocity of Ω = 29.9237radian per second, and blade pitching angle of θ = 8 • is studied.This strategy is validated and compared against numerical and experimental reference data in terms of accuracy and computational cost, considering functional targets such as lift, drag, and thrust coefficients of the simulations.Results demonstrate that the algorithm can track regions of interest, such as boundary layers and wake regions, and yields a considerable speed-up when applied to parallel simulations.Qualitative and quantitative results showed equivalent levels of accuracy with significant speed-up when applied to parallel simulations.Hence, the proposed algorithm is an effective and accurate approach for simulating unsteady transitional and turbulent flows.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".