Rotorcraft simulation fidelity: new methods for quantification and assessment
Bibliographic record
Abstract
Abstract Flight simulators are integral to the design/development, testing/qualification, training and research communities and their utilisation is ever expanding. The use of flight simulation to provide a safe environment for pilot training, and in research and development, must be underpinned by quantification of simulator fidelity. While regulatory simulator standards exist for flight training simulators and new standards are in development, previous research has shown that current standards do not provide a fully quantitative approach for assessing simulation fidelity, especially in a research environment. This paper reports on progress made in a research project at the University of Liverpool (Lifting Standards), in which new predicted and perceptual measures of simulator fidelity have been developed. The new metrics have been derived from handling qualities engineering practice. Results from flight tests on the National Research Council (Canada) Bell 412 ASRA research aircraft and piloted simulation trials using the HELIFLIGHT-R simulator at Liverpool are presented to show the efficacy of adopting a handling qualities approach for fidelity assessment. Analysis of the new metrics has shown an appropriate degree of sensitivity to differences between flight and simulation.
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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 | 0.016 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".