Fidelity enhancement of a rotorcraft simulation model through system identification
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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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".