Development of a Full-Flight Simulator for Ab-initio Flight Training with Emphasis on Hardware and Motion Integration
Bibliographic record
Abstract
As commercially-available flight simulators are tailored mostly for commercial airlines, there is currently a lack of low-cost, type-specific, high-fidelity aircraft simulators for introductory flight training.A project was undertaken by the Carleton University Applied Dynamics Laboratory to assess the feasibility of developing a viable economical alternative to the currently-available small aircraft flight training devices, thereby providing a supplementary means of training that could be available to small flight schools.This thesis presents the development of a simulator prototype, built upon a Diamond DA20-A1 fuselage and developed through the effective use of original aircraft components and commercial off-the-shelf components.For this development, emphasis has been placed on the integration of the flight controls and electrical components, and their interface to the virtual environment.Further, a discussion is presented on making efficient use of a small motion base to replicate aircraft motion characteristics using an algorithm known as washout.Identification of the aircraft motion characteristics required the development and performance of a flight testing plan and associated instrumentation interface.Following completion of the prototype, an assessment of the simulator determined that despite a few deficiencies that could be circumvented, development of a low-cost, type-specific, high-fidelity aircraft simulator for ab-initio flight training is feasible.just the technical skills.Large contributions, through assistance and support, were provided by the researchers from the Applied Dynamics Laboratory, to whom a great thanks are owed.Further thanks extended to
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".