Enhancement of an Engineering Simulation Model to Improve the Correlation with Flight Test Data in Climb/Descent and Autorotation
Bibliographic record
Abstract
A high-fidelity engineering simulation model has been developed in FLIGHTLAB for a Sikorsky production helicopter to support future design modifications. The simulation model consists of major subsystems for main rotor, tail rotor, fuselage, empennage, landing gear, flight control system, and propulsion system. As the manufacturer, Sikorsky was able to provide a complete and validated set of model data and a large database of flight test records to ensure the model quality and fidelity. Although the model correlation with test data is satisfactory in most flight conditions including hover, low-speed flight, level flight, and vertical climb, some model-data discrepancies were seen in the forward climb/descent and autorotation test cases. An additional study was conducted at Sikorsky to investigate these discrepancies. Based on the study, a set of model enhancements were developed to improve the model correlation with test data in forward climb/descent and autorotation. These enhancements allow for adjustment of certain semi-empirical corrections to address model limitations at these challenging conditions such as fuselage characteristics and interference at high angles of attack and rotor inflow and interference at low collective settings and near 90-degree wake skew. These enhancements were carefully designed such that the effects were localized so that the model-data correlation was not adversely impacted in other flight conditions. The model-data correlation in forward climb/descent and autorotation were significantly improved by implementing these model enhancements with little to no impact on the other flight conditions resulting in a high-fidelity engineering simulation model validated in the entire flight envelope.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".