Acceptance testing and commissioning of a flight simulator for rotorcraft simulation fidelity research
Bibliographic record
Abstract
The rotorcraft industry faces a number of challenges today regarding the replacement of ageing airframes, an expansion in the operational roles of helicopters and a requirement to improve safety whilst reducing the environmental impact of rotorcraft operations. The quantification of simulation fidelity underpins the confidence required for the expanding use of modelling and simulation to develop solutions to these challenges in a timely and cost-efficient manner. Current simulator certification standards do not provide a fully quantitative method for assessing simulation fidelity, especially in a research environment. This article details the commissioning and acceptance process of the new research flight simulation facility at the University of Liverpool, HELIFLIGHT-R, and its subsequent use in a research project ‘Lifting Standards: A Novel Approach to the Development of Fidelity Criteria for Rotorcraft Flight Simulators’ aimed at developing new predicted and perceptual measures of simulator fidelity. Some initial results from both piloted simulation and flight tests using the Bell 412 Advanced Systems Research Aircraft are reported within the context of the rotorcraft simulation fidelity project.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".