Dynamic Wake Distortion in the UTIAS Real-time Helicopter Models
Bibliographic record
Abstract
The University of Toronto Institute for Aerospace Studies has a number of previously developed real-time helicopter models, which are currently in the process of being updated and improved. An ongoing concern with helicopter simulations is that they often have an incorrect off-axis response to cyclic control inputs when compared with the corresponding flight test data. One of the more commonly suggested contributing factors for this discrepancy is the influence of rotor wake curvature. The dynamic wake distortion model with four states (curvature in two directions, stretch, and skew) and corresponding augmented Pitt-Peters dynamic inflow model developed by Zhao to account for this effect is computationally compact and therefore suitable for use in a real-time simulation. This paper presents the results obtained for the various UTIAS helicopter models with the dynamic wake distortion included compared to the original Pitt-Peters inflow, examining the on-axis and off-axis responses to cyclic inputs under various conditions. These results are also compared to flight test data where available. The results cover a wide cross section of helicopter designs to examine the impact of including the dynamic wake distortion in realtime helicopter simulations. The addition of wake distortion resulted in an altered inflow distribution across the rotor disk during dynamic maneuvers, with limited changes to the on-axis response to a cyclic input. The off-axis response was altered, however the changes were not as significant as expected. Overall the off-axis response was only slightly improved, and in some test cases the off-axis response was actually worse than that obtained with an undistorted wake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".