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Enregistrement W3135381672 · doi:10.14339/sto-tr-avt-296-nu

Rotorcraft Flight Simulation Model Fidelity Improvement and Assessment

2021· article· en· W3135381672 sur OpenAlexaboutno aff
Mark B. Tischler, Mark White, Michael Jones, Pavle Šćepanović, Susanne Seher-Weiß, Vincent Myrand-Lapierre, Michel Nadeau-Beaulieu, Sylvain Richard, Andrea Ragazzi, Stefano D’Agosto, Chengjian He, R. K. Lehman, David G. Miller, Xin Hong, Arthur Gubbels, Kenneth Hui, Armin Taghizad, Eric Tobias, Steffen Greiser, Marilena Pavel, Olaf Stroosma, İlkay Yavrucuk, Gareth D. Padfield, Neil Cameron, J. V. R. Prasad, Feyyaz Güner, Ondrej Juhasz, Josef Horn, Jonathan Soong, Samuel Nadell, Olivia Lee

Notice bibliographique

Revueelib (German Aerospace Center) · 2021
Typearticle
Langueen
DomaineEngineering
ThématiqueAerospace and Aviation Technology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFidelityFlight simulatorCertificationTrainerComputer scienceIdentification (biology)EngineeringSimulationFlight testAeronauticsSystems engineering

Résumé

récupéré en direct d'OpenAlex

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.
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\nThis 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.
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\nThe 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 and nonlinear simulation models using SID 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 is 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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\nThis documentation of rotorcraft simulation fidelity assessment and model update strategies will benefit NATO nations by allowing for common, agreed-upon best practices and recommendations, ensuring each country's flight dynamics and simulation models are of the highest calibre possible. The collaboration between industry, academia, and government laboratories has been key to the success of this RTG; this cooperation model should be adopted in future research activities. As industries strive to achieve greater efficiency and safety in their products, the fidelity of simulation should match commercial aspirations to ensure that the "right first time" ethos is fully embedded into industrial best practices. Militaries will be able to use the methods and metrics presented to set criteria that will underpin the use of modelling and simulation in certification to accelerate development and acquisition and reduce the cost of new aircraft systems, e.g. advanced high-speed rotorcraft and legacy system upgrades. The criteria may also set standards for training devices used to support the expansion of synthetic environments for training to offset the high costs of flight hours. This RTG has identified that current flight training simulator standards could be updated to use the flight model and perceptual fidelity metrics presented in this report to ensure that models are not "over-tuned" and a more rigorous method of subjective simulator assessment is adopted.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,455
Score d'incertitude au seuil0,957

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,009
Tête enseignante GPT0,273
Écart entre enseignants0,263 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2021
Routes d'admission1
Résumé présentoui

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