LBM and iLES Comparison for the Aaerodynamic and Acoustic Characteristics of a Low-speed Rotor
Bibliographic record
Abstract
Two high fidelity numerical methods, Navier-Stokes iLES and LBM/VLES, are compared to address low-speed rotors operating in the transitional regime. The results assess the capabilities of both approaches to predict the flow physics of these systems by comparing them with experimental data collected in an anechoic chamber in hover conditions. A temporal convergence has been reached for both the iLES and LBM simulation yielding a reasonable agreement of the rotor thrust and torque. Several main flow features have been also been evidenced. First, both methods yield a similar tip vortex that is slowly convected downstream and grazes under the next blade. In both cases, both suction and pressure sides appear to have detached flow similar to laminar separation bubbles on airfoils, making them contribute to the far-field noise. The tapered blade foot in the LBM simulation further generates a corner vortex that yields an additional flow separation propagating diagonally toward mid-span. Both methods clearly show a dipolar-like noise radiation centered around the blade tip. Both methods capture the Blade Passing Frequency (BPF) and its first harmonics within 1-2 dB in all directions. Discrepancies up to 10-15 dB are seen on the broadband noise, more in the mid frequency range for the LBM and rather at high frequencies for the iLES. Filtered power spectra densities of wall-pressure fluctuations in different frequency ranges suggest that the flow separation are the sound sources, particularly in the tip trailing edge region. Interaction of the grazing tip vortex with the blade leading edge is also a contributor beyond the second BPF harmonic.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".