Fidelity enhancement of a rotorcraft simulation model through system identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract High fidelity modelling and simulation are prerequisites for ensuring confidence in decision making during aircraft design and development, including performance and handling qualities, control law developments, aircraft dynamic loads analysis, and the creation of a realistic simulation environment. The techniques of system identification provide a systematic framework for ‘enhancing’ a physics–based simulation model derived from first principles and aircraft design data. In this paper we adopt a frequency domain approach for model enhancement and fidelity improvement of a baseline FLIGHTLAB Bell 412 helicopter model developed at the University of Liverpool. Predictability tests are based on responses to multi–step control inputs. The techniques have been used to generate one, three, and six degree-of-freedom linear models, and their derivatives and predictability are compared to evaluate and augment the fidelity of the FLIGHTLAB model. The enhancement process thus involves augmenting the simulation model based on the identified parameters. The results are reported within the context of the rotorcraft simulation fidelity project, Lifting Standards, involving collaboration with the Flight Research Laboratory (NRC, Ottawa), supported with flight testing on the ASRA research helicopter.
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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 it