S-97 RAIDER® Maneuvering Loads Flight Test and Simulation Correlation
Bibliographic record
Abstract
This paper demonstrates the recent success of applying advanced digital modeling and simulation tools to simulate challenging transient maneuver flight conditions and shows the resulting correlation with flight test. To understand and improve the predictive capabilities for advanced rotorcraft configurations such as the coaxial aircraft, a multi-year effort has been pursued to develop a coupled full aircraft Computational Fluid Dynamics (CFD) and Computational Structural Dynamics (CSD) methodology for both steady and transient maneuver flight conditions. The CFD-CSD full-aircraft transient maneuver simulation methodology developed was applied to simulate S-97 RAIDER® aircraft for both a low-speed level-body acceleration (using only the propulsor to accelerate the aircraft) and a 2.0g pull-up maneuver. In-depth studies were carried out to gain insights into flight test observations and to identify important modeling feature enhancements to better simulate the complex aerodynamic and structural couplings between the rotor and airframe systems. Overall, the simulation results at these transient maneuver conditions demonstrated reasonably good correlation with the corresponding flight test data. This study shows that usage of the current state-of-the-art methodology, when carefully validated and applied, can capture the complex coaxial rotor physics due to fluid and structure interaction with sufficient accuracy to support designs.
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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) | 0.003 | 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".