Updating Rotorcraft Simulation Environments by Using Black-Box Input Filters
Bibliographic record
Abstract
Rotorcraft simulation environments such as training simulators or engineering simulations require high-fidelity models to be suitable for tasks such as pilot training, handling qualities analysis, and flight control system development. Deficits in model fidelity can arise from inaccurate data and unmodeled higher-order dynamic effects. Mitigating these deficits through physics-based modeling usually requires high effort. Therefore, a method is presented that improves a baseline simulation model by a "black-box" (i.e. non-physical) low-order input filter. The baseline can be a linear model or a nonlinear simulation model. Several options for deriving the input filter are elaborated and demonstrated in this paper using data from the CH-47, AH 135, and Bell 412 helicopters. In all cases the simulation fidelity was improved by approximately a factor of two.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".