Coaxial Hub Drag Correlation With Water-Tunnel Model Using Two Flow Solvers
Bibliographic record
Abstract
Current paper summarizes a correlation study of two flow solvers (CREATE-AV™ Helios and STAR-CCM+), routinely used at Sikorsky, with the spinning coaxial hub drag and flow field measurements conducted by Penn State University at the 12' diameter water tunnel. The Helios modeling approach was aiming for a high fidelity accurate simulation, whereas the STAR-CCM+ modeling approach was aiming for a fast turn-around time with reasonable solution accuracy with a relatively coarse mesh and simplification. The two solvers generally agreed well with the test data within reasonable accuracy and captured the drag trend between two shaft fairing configurations. Impact of turbulence model selection (Spalart-Allmaras Detached Eddy Simulation and Spalart-Allmaras Reynolds-Averaged-Navier-Stokes model) has been demonstrated. The RANS model generally delayed separation and resulted in lower drag. The STAR-CCM+ runs simulated both air and water at matching Reynolds number and showed good agreement between the drag results for the two mediums. Also, the importance of accurate representation of geometric details including gaps, shafts, and holes is highlighted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".