2014/09/16 by S. O. Ojo, Saheed O. Ojo, S. Grivet‐Talocia +3
Engineering · Mathematics · Physics and Astronomy · #Algorithm #Applied mathematics #Computer science #Context (archaeology) #Control (management) #Control theory (sociology) #Engineering #Field (mathematics) #Fluid Dynamics and Vibration Analysis #Interpolation (computer graphics) #Materials science #Mathematical optimization #Mathematics #Mechanical engineering #Model Reduction and Neural Networks #Model order reduction #Nonlinear system #Numerical methods for differential equations #Photovoltaic system #Reduction (mathematics) #Thermal conduction #Transient (computer programming) #Work (physics) #cond-mat.mtrl-sci
paper · pdf · doi:10.1016/j.compstruct.2014.09.008
published as Composite Structures, Vol. 119 (2015) 477-486 · 20 pages, 9 figures
openalex publication_date 2014/09/16 · arxiv created 2015/05/20 · arxiv updated 2015/05/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Modelling of physical systems may be a challenging task when it requires solving large sets of numerical equations. This is the case of photovoltaic (PV) systems which contain many PV modules, each module containing several silicon cells. The determination of the temperature field in the modules leads to large scale systems, which may be computationally expensive to solve. In this context, Model Order Reduction (MOR) techniques can be used to approximate the full system dynamics with a compact model, that is much faster to solve. Among the several available MOR approaches, in this work we consider the Discrete Empirical Interpolation Method (DEIM), which we apply with a suitably modified formulation that is specifically designed for handling the nonlinear terms that are present in the equations governing the thermal behaviour of PV modules. The results show that the proposed DEIM technique is able to reduce significantly the system size, by retaining a full control on the accuracy of the solution.