Skip to Main content Skip to Navigation
Conference papers

Embedded multilevel optimization for nonlinear time-stepping mesh-based reluctance network

Abstract : This paper presents an original methodology for machine design. The methodology is based on nonlinear reluctance network modeling and multilevel surrogate based optimization. The reluctance network is solved by computing the meshes magnetic flux and its topology is updated for each rotor position. In order to achieve an optimal design, in terms of satisfying some specifications, a surrogate based optimization inspired from the Space Mapping (SM) technique is considered. Optimization is held on the linear model and is iteratively corrected, through a new embedded strategy, by the nonlinear one. Finally, the proposed application is a constrained minimization of axial flux machine losses on an artemis cycles.
Complete list of metadatas

Cited literature [7 references]  Display  Hide  Download

https://hal-centralesupelec.archives-ouvertes.fr/hal-01677537
Contributor : Maya Hage-Hassan <>
Submitted on : Wednesday, March 11, 2020 - 9:18:43 AM
Last modification on : Wednesday, September 16, 2020 - 4:50:52 PM
Long-term archiving on: : Friday, June 12, 2020 - 1:32:21 PM

Identifiers

  • HAL Id : hal-01677537, version 1

Citation

Maya Hage-Hassan, Guillaume Krebs, Ghislain Remy, Claude Marchand. Embedded multilevel optimization for nonlinear time-stepping mesh-based reluctance network. 2013 Computation of Electromagnetic Fields (COMPUMAG), Jul 2013, Budapest, Hungary. ⟨hal-01677537⟩

Share

Metrics

Record views

177

Files downloads

17