| Téléchargement | - Voir la version finale : A physics-based neural network for flight dynamics modelling and simulation (PDF, 1.6 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.1186/s40323-022-00227-7 |
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| Auteur | Rechercher : Stachiw, Terrin1; Rechercher : Crain, Alexander1Identifiant ORCID : https://orcid.org/0000-0003-4961-8437; Rechercher : Ricciardi, Joseph1 |
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| Affiliation | - Conseil national de recherches du Canada. Aérospatiale
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| Format | Texte, Article |
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| Sujet | flight simulation; modelling and simulation; aerospace |
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| Résumé | The authors have developed a novel physics-based nonlinear autoregressive exogeneous neural network model architecture for flight modelling across the entire flight envelope, called FlyNet. When using traditional parameter estimation and output-error methods, aircraft models are captured about a single point in the flight envelope using a first-order Taylor series to approximate forces and moments. To enable analysis throughout the entire operational envelope, the traditional models can be extended by interpolating or stitching between a number of these single-condition models. This paper completes the evolutionary next step in aircraft modelling to consider all second-order Taylor series terms instead of a subset of those and by exploiting the ability of neural networks to capture more complex and nonlinear behaviour for the efficient development of a continuous flight simulation model valid across the entire envelope. This method is valid for fixed- and rotary-wing aircraft. The behaviour of a conventional model is compared to FlyNet using flight test data collected from the National Research Council of Canada’s Bell 412HP in forward flight. |
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| Date de publication | 2022-07-04 |
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| Maison d’édition | Springer |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | d8a83d58-2096-4ee7-97a6-7542edb05e72 |
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| Enregistrement créé | 2023-09-21 |
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| Enregistrement modifié | 2023-09-21 |
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