Comptes Rendus
no. 11
Nonintrusive data-based learning of a switched control heating system using POD, DMD and ANN
Tarik Fahlaoui1; Florian De Vuyst1 
1 Laboratoire de mathématiques appliquées de Compiègne EA 2222, Université de technologie de Compiègne, Alliance Sorbonne Université, 60200 Compiègne, France
Comptes Rendus. Mécanique, Data-Based Engineering Science and Technology, Volume 347 (2019) no. 11, pp. 793-805.

The aim of this work is to derive an accurate model of two-dimensional switched control heating system from data generated by a Finite Element solver. The nonintrusive approach should be able to capture both temperature fields, dynamics and the underlying switching control rule. To achieve this goal, the algorithm proposed in this paper will make use of three main ingredients: proper orthogonal decomposition (POD), dynamic mode decomposition (DMD) and artificial neural networks (ANN). Some numerical results will be presented and compared to the high-fidelity numerical solutions to demonstrate the capability of the method to reproduce the dynamics.

Received:
Accepted:
Published online:
DOI: 10.1016/j.crme.2019.11.005
Mots-clés : Data-driven model, Heating system, Switched control, Heat equation, Model order reduction, POD, DMD, ANN, Machine learning

Tarik Fahlaoui 1; Florian De Vuyst 1

1 Laboratoire de mathématiques appliquées de Compiègne EA 2222, Université de technologie de Compiègne, Alliance Sorbonne Université, 60200 Compiègne, France 
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Tarik Fahlaoui; Florian De Vuyst. Nonintrusive data-based learning of a switched control heating system using POD, DMD and ANN. Comptes Rendus. Mécanique, Data-Based Engineering Science and Technology, Volume 347 (2019) no. 11, pp. 793-805. doi : 10.1016/j.crme.2019.11.005. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.1016/j.crme.2019.11.005/

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