Integrated Methods to Model and Update Rotorcraft Simulations with Experimental Data
Bibliographic record
Abstract
Updating simulation models based on flight, or other external data, is a laborious process. Despite this, simulation updates are necessary for high-fidelity simulations used in analysis, control development, handling qualities prediction, fleet training, etc. Recent development has yielded an integrated modeling and simulation update suite known as AUSPEX: Algorithms to Update Simulation Parameters with EXperimental Data. AUSPEX combines the steps of the simulation-update process into a single environment and provides a framework to track updates and associated metadata. Mathematically sound and statistically rigorous techniques are used to calculate updates of current model parameters, thereby improving correlation with observed vehicle response. The software can also suggest additional terms to the dynamic equations if these terms are missing from the simulation due to simplifying assumptions made in the modeling phase. Creation of a new model is also possible. This article describes application of AUSPEX to the V-22 tiltrotor specifically, and rotorcraft in general, using a statistically-based frequency domain method. The capability to accurately and automatically update a simulation database to account for nonlinear and unsteady aerodynamic effects embodied in the flight data is demonstrated. Useful features of this novel frequency domain approach are highlighted, including computational efficiency, noise rejection, automated model structure determination in the frequency domain for nonlinear and unsteady aerodynamics, and the ability to generate simulation updates without air flow angle measurements in the flight data. Examples are presented using V-22 simulation and flight test data with the vehicle in airplane, conversion, and helicopter modes. Analysis was performed with both the NAVAIR and Bell simulations of the V-22 hosted in GTRSIM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".