Development of a Cost-Effective High-Fidelity Type-Specific Flight Simulator with Emphasis on Flight Modelling
Bibliographic record
Abstract
This project was an investigation into the feasibility of developing a cost-effective highfidelity type-specific small aircraft simulator for use as part of an ab-initio flight training program.The development of the KatanaSim, a Diamond DA20-A1 Katana flight simulator built with commercial off-the-shelf components and original aircraft parts wherever possible, proved the feasibility of the endeavour.Particularly successful was the high level of physical fidelity achieved by the use of original aircraft parts, most notably a Katana fuselage.The development cost of the KatanaSim was significantly less than the six and seven figure costs usually associated with high-fidelity flight simulators.In order to acquire the data necessary for flight model evaluation and tuning, a minimally-intrusive flight testing methodology for small aircraft was developed.A compact instrumentation package was designed and tested, and a flight permit from Transport Canada was obtained.Two flight tests in a DA20-A1 Katana were completed and several hours worth of flight testing data was acquired.X-Plane, by Laminar Research, was chosen as the core simulator software and Plane Maker, a part of the same package, was used to develop the flight model.Aircraft model parameters were obtained from published data sources, and empirical measurements and observations.The resulting flight model required additional tuning to meet the desired performance specifications.A curve-fitting genetic algorithm was developed to automate the tuning process and was validated using a variety of dynamic models.This genetic algorithm was proven to be capable of tuning the stability derivatives of an aircraft given simulated flight performance data.In future, the genetic algorithm can be used with a blade element theory mathematical model to complete the tuning of the KatanaSim flight model using the acquired flight test data.I'd like to acknowledge the faculty, staff, and students at Carleton who made this ambitious project possible; our industry sponsors, particularly the OAS maintenance team for their support and guidance throughout the project; and Ashraf Othman, for his generous donation of time and expertise that made flight testing possible.My biggest thanks goes to Team KatanaSim, for sticking with this project through a seemingly endless set of challenges.iii To my parents, the pilot and the scientist, who inspired my long and winding journey and made me who I am today.To Katie, Drew, Mike, Jeremy, Brent,
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".