Rotorcraft Flight Simulation Model Fidelity Improvement and Assessment
Notice bibliographique
Résumé
Rotorcraft flight dynamics simulation models require high levels of fidelity to be suitable as prime items in support of life cycle practices, particularly vehicle and control design and development, and system and trainer certification. On the civil side, both the FAA (US) and EASA (Europe) have documented criteria (metrics and practices) for assessing model and simulator fidelity as compared to flight-test data, although these have not been updated for several decades. On the military side, the related practices in NATO nations are not harmonised and often only developed for specific applications. Methods to update the models for improved fidelity are mostly ad hoc and lack a rational and methodical approach. Modern rotorcraft System Identification (SID) and inverse simulation methods have been developed in recent years that provide new approaches well suited to pilot-in-the-loop fidelity assessment and systematic techniques for updating simulation models to achieve the needed level of fidelity. To coordinate efforts and improve the knowledge in this area, STO Applied Vehicle Technology Panel Research Task Group (STO AVT-296 RTG) was constituted to evaluate update methods used by member nations to find best practices and suitability for different applications including advanced rotorcraft configurations. This report presents the findings of the AVT-296 RTG. An overview of previous rotorcraft simulation fidelity Working Groups is presented, followed by a review of the metrics that have been used in previous studies to quantify the fidelity of a flight model or the overall perceptual fidelity of a simulator. The theoretical foundations of the seven different update methods and a description of the eight flight databases (Bell 412, UH-60, IRIS+, EC135, CH-47, AW139, AW109, and X2, provided by the National Research Council of Canada, US Army, Airbus Helicopters, Boeing, Leonardo Helicopter Division, and Sikorsky) used by the RTG is presented. Both time- and frequency-domain fidelity assessment methods are considered, including those in current use by simulator qualification authorities and those used in the research community. Case studies are used to show the application, utility, and limitations of the update and assessment methods to the flight-test data. The work of the RTG has shown that time- and frequency-domain SID based metrics are suitable for use for assessing the model fidelity across a wide range of rotorcraft configurations. Gain and time delay update methods work well for well-developed flight dynamics models and can be used for flight control system design, but do not provide physical insights into the sources of errors in a model. Deriving stability and control derivatives from flight-test data using SID and nonlinear simulation models using perturbation extraction methods provides insight into the missing dynamics of the simulation model, which can subsequently be updated using additional forces and moments to significantly improve the fidelity of the model and can be used to update models for flight simulation training application methods. Reduced order model and physics-based correction methods provide large benefits when extrapolating to other flight conditions but does require detailed flight-test data. SID can quickly provide accurate point models, if detailed flight-test data are available, which can be ‘stitched’ together to produce models suitable for real-time piloted simulation and control design applications. However, the dependency on flight-test data means that this method is not suitable for early aircraft development activities.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».