| DOI | Resolve DOI: https://doi.org/10.1016/j.jcp.2021.110863 |
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| Author | Search for: Cao, Xiulei; Search for: Fraser, Kirk1ORCID identifier: https://orcid.org/0000-0002-8998-7328; Search for: Song, Zilong; Search for: Drummond, Chris2; Search for: Huang, Huaxiong |
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| Affiliation | - National Research Council of Canada. Automotive and Surface Transportation
- National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | friction stir welding; Navier-Stokes equation; heat transfer; proper orthogonal decomposition; neutral network |
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| Abstract | 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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| Publication date | 2021-12-03 |
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| Publisher | Elsevier |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 0044f2c0-8700-412b-98e4-af5a44dd2f84 |
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| Record created | 2022-05-19 |
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| Record modified | 2022-05-20 |
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