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A combined reduced order‐full order methodology for the solution of 3D magneto‐mechanical problems with application to magnetic resonance imaging scanners
- Source :
- UPCommons. Portal del coneixement obert de la UPC, Universitat Politècnica de Catalunya (UPC)
- Publication Year :
- 2020
- Publisher :
- Wiley, 2020.
-
Abstract
- This is the peer reviewed version of the following article: Seoane, M. [et al.]. A combined reduced order-full order methodology for the solution of 3D magneto-mechanical problems with application to magnetic resonance imaging scanners. "International journal for numerical methods in engineering", 1 Gener 2020, vol. 121, núm. 16, p. 3529-3559. , which has been published in final form athttps://onlinelibrary.wiley.com/doi/abs/10.1002/nme.6369. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. The design of a new magnetic resonance imaging (MRI) scanner requires multiple numerical simulations of the same magneto-mechanical problem for varying model parameters, such as frequency and electric conductivity, in order to ensure that the vibrations, noise, and heat dissipation are minimized. The high computational cost required for these repeated simulations leads to a bottleneck in the design process due to an increased design time and, thus, a higher cost. To alleviate these issues, the application of reduced order modeling techniques, which are able to find a general solution to high-dimensional parametric problems in a very efficient manner, is considered. Building on the established proper orthogonal decomposition technique available in the literature, the main novelty of this work is an efficient implementation for the solution of 3D magneto-mechanical problems in the context of challenging MRI configurations. This methodology provides a general solution for varying parameters of interest. The accuracy and efficiency of the method are proven by applying it to challenging MRI configurations and comparing with the full-order solution. Generalitat de Catalunya, 2017‐SGR‐1278; Marie Sklodowska‐Curie Innovative Training Network AdMoRe, 675919; Spanish Ministry of Economy and Competitiveness, DPI2017‐85139‐C2‐2‐R
- Subjects :
- Proper Orthogonal Decomposition
Multifield systems
Full order
02 engineering and technology
Newton methods
01 natural sciences
Reduced order
Matemàtiques i estadística::Anàlisi numèrica [Àrees temàtiques de la UPC]
symbols.namesake
Magneto-mechanical coupling
0203 mechanical engineering
medicine
70 Mechanics of particles and systems::70H Hamiltonian and Lagrangian mechanics [Classificació AMS]
Applied mathematics
Order (group theory)
0101 mathematics
Hamiltonian systems
Magneto
Lagrangian
Physics
Numerical Analysis
medicine.diagnostic_test
ROM
Applied Mathematics
Numerical analysis
General Engineering
Magnetic resonance imaging
MRI scanner
010101 applied mathematics
020303 mechanical engineering & transports
Hamilton, Sistemes de
symbols
Proper orthogonal decomposition
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- UPCommons. Portal del coneixement obert de la UPC, Universitat Politècnica de Catalunya (UPC)
- Accession number :
- edsair.doi.dedup.....db1686e7cbde6dbe280d6f499d1475b8