Nonlinear Aero-Propulsive Modeling for Fixed-Wing eVTOL UAV from Flight Test Data
Bibliographic record
Abstract
This paper presents a methodology for constructing an aero-propulsive system identification model for a fixed-wing propeller-driven aircraft with limited flight data. Developing a flight dynamics model is an iterative process involving costly and time-consuming flight testing to collect relevant data. To maximize the utilization of available data, this study employs a time-domain system identification procedure on flight data from various maneuvers and flights. The methodology incorporates multivariate orthogonal functions to capture the nonlinear terms representing the coupled effects of the propeller and airframe dynamics. A stepwise regression is then employed to identify the most pertinent model parameters. Estimation of variables associated with the propulsive contribution is accomplished through an electrical propulsive model, identified using command and battery data from static thrust and flight tests. Aero-propulsive derivatives are determined using the output-error method, where the accuracy is assessed based on a colored noise correction. To validate the predictability of the flight dynamics model, out-of-sample data are employed. The construction of the electrical propulsive model and identification of aerodynamic derivatives from flight data were specifically carried out for a particular eVTOL flight test vehicle; however, the techniques employed are generalizable and applicable to other aircraft models.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".