| DOI | Trouver le DOI : https://doi.org/10.1016/j.jcp.2021.110863 |
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| Auteur | Rechercher : Cao, Xiulei; Rechercher : Fraser, Kirk1Identifiant ORCID : https://orcid.org/0000-0002-8998-7328; Rechercher : Song, Zilong; Rechercher : Drummond, Chris2; Rechercher : Huang, Huaxiong |
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| Affiliation | - Conseil national de recherches du Canada. Automobile et les transports de surface
- Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Sujet | friction stir welding; Navier-Stokes equation; heat transfer; proper orthogonal decomposition; neutral network |
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| Résumé | The friction stir welding process can be modeled using a system of heat transfer and Navier-Stokes equations with a shear dependent viscosity. Finding numerical solutions of this system of nonlinear partial differential equations over a set of parameter space, however, is extremely time-consuming. Therefore, it is desirable to find a computationally efficient method that can be used to obtain an approximation of the solution with acceptable accuracy. In this paper, we present a reduced basis method for solving the parametrized coupled system of heat and Navier-Stokes equations using a proper orthogonal decomposition (POD). In addition, we apply a machine learning algorithm based on an artificial neural network (ANN) to learn (approximately) the relationship between relevant parameters and the POD coefficients. Our computational experiments demonstrate that substantial speed-up can be achieved while maintaining sufficient accuracy. |
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| Date de publication | 2021-12-03 |
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| Maison d’édition | Elsevier |
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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 | 0044f2c0-8700-412b-98e4-af5a44dd2f84 |
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| Enregistrement créé | 2022-05-19 |
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| Enregistrement modifié | 2022-05-20 |
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