Validation of Aero-Load Estimator for Convair 880 Aircraft Flight Simulator Benchmark with Electro-Hydrostatic Actuators
Bibliographic record
Abstract
Simulating an aircraft model using of high fidelity models of subsystems for its primary and secondary flight control actuators requires measuring or estimating aero-load data acting on flight control surfaces. One solution would be to incorporate the data recorded from flight tests, which is a time-consuming and costly process. This paper proposes another solution based on the validation of an aero-loads estimator or on the hinge moments predictor for fully electrical aircraft simulator benchmark. This estimator is based on an aerodynamic coefficient calculation methodology, inspired by Roskam’s method that uses the geometrical data of the wing and control surfaces airfoils. The hinge moment values are found from two-dimensional lookup tables where the deflections of the control surfaces, aircraft altitude, and aircraft angles of attack are the input vectors of the tables; and the resulting hinge moment coefficients are the output vectors. The resulting hinge moment coefficients of the Convair 880 primary flight control surfaces are compared to those of its recorded flight test data; the results from the new software solution were found to be very accurate. Hinge moment lookup tables are integrated in the Convair 880 high fidelity flight simulation benchmark using mathematical models of energy-efficient Electro-Hydrostatic Actuators (EHA). Autopilot controls are designed for the roll, pitch, attitude and yaw damper motions using Proportional Integral (PI) controller scheduled for different flight conditions. Several different aircraft simulation scenarios are evaluated to demonstrate the efficacy and accuracy of the predicted hinge moment results.
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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) | 0.001 | 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 | 0.000 |
| 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".