Nonlinear model order reduction for the fast solution of induction heating problems in time-domain. Issue 2 (6th March 2017)

• Record Type: Journal Article
• Authors: Codecasa, Lorenzo; Moro, Federico; Alotto, Piergiorgio
• Is Part Of: Compel. Volume 36:Issue 2(2017)
• Page Start: 469
• Page End: 475
• Publication Date: 2017-03-06
• Subjects: Nonlinearity -- Induction heating -- Model order reduction -- Time domain -- Volterra series; Electrical engineering -- Data Processing -- Periodicals; Electrical engineering -- Mathematics -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Data Processing -- Periodicals; Electronics -- Mathematics -- Periodicals; 621.3
• DOI: 10.1108/COMPEL-05-2016-0215
• Languages: English
• ISSNs: 0332-1649

Abstract

Abstract : Purpose: This paper aims to propose a fast and accurate simulation of large-scale induction heating problems by using nonlinear reduced-order models. Design/methodology/approach: A projection space for model order reduction (MOR) is quickly generated from the first kernels of Volterra's series to the problem solution. The nonlinear reduced model can be solved with time-harmonic phasor approximation, as the nonlinear quadratic structure of the full problem is preserved by the projection. Findings: The solution of induction heating problems is still computationally expensive, even with a time-harmonic eddy current approximation. Numerical results show that the construction of the nonlinear reduced model has a computational cost which is orders of magnitude smaller than that required for the solution of the full problem. Research limitations/implications: Only linear magnetic materials are considered in the present formulation. Practical implications: The proposed MOR approach is suitable for the solution of industrial problems with a computing time which is orders of magnitude smaller than that required for the full unreduced problem, solved by traditional discretization methods such as finite element method. Originality/value: The most common technique for MOR is the proper orthogonal decomposition. It requires solving the full nonlinear problem several times. The present MOR approach can be built directly at a negligible computational cost instead. From the reduced model, magnetic and temperature fields can be accurately reconstructed in whole time and space domains.