Unsteady Body Force Methodology for Fan Operability Assessment under Clean and Distorted Inflow Conditions
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-0388.vid With more complex aircraft architectures, fast and cost-effective design iterations are key to improve overall fuel efficiency. This paper proposes to revisit a low-order unsteady modeling approach to replace costly full annulus URANS simulation. Unsteady Body Force Methods (UBFM) could allow a significant cost reduction for fan distortion ingestion and operability assessment. In this approach, the bladed area in the computational domain is replaced by source terms in the Navier–Stokes equations, and the cost of the simulation is reduced by a factor of 26. The operability of the fan is evaluated with and without distortion in order to assess the accuracy of the model. Previously published results of URANS simulations performed on the same fan subject to an unsteady vortex ingestion are used as reference. The results show that our UBFM is able to predict rotating stall cells, with patterns and rotating speed similar to the URANS data.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".