| DOI | Trouver le DOI : https://doi.org/10.1016/j.jcp.2021.110863 |
|---|
| Auteur | Rechercher : Cao, Xiulei; Rechercher : Fraser, Kirk1Identifiant ORCID : https://orcid.org/0000-0002-8998-7328; Rechercher : Song, Zilong; Rechercher : Drummond, Chris2; Rechercher : Huang, Huaxiong |
|---|
| Affiliation | - Conseil national de recherches Canada. Automobile et les transports de surface
- Conseil national de recherches Canada. Technologies numériques
|
|---|
| Format | Texte, Article |
|---|
| Sujet | friction stir welding; Navier-Stokes equation; heat transfer; proper orthogonal decomposition; neutral network |
|---|
| 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. |
|---|
| Date de publication | 2021-12-03 |
|---|
| Maison d’édition | Elsevier |
|---|
| Dans | |
|---|
| Langue | anglais |
|---|
| Publications évaluées par des pairs | Oui |
|---|
| Exporter la notice | Exporter en format RIS |
|---|
| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
|---|
| Identificateur de l’enregistrement | 0044f2c0-8700-412b-98e4-af5a44dd2f84 |
|---|
| Enregistrement créé | 2022-05-19 |
|---|
| Enregistrement modifié | 2022-05-20 |
|---|
