A hybrid twin based on machine learning enhanced reduced order model for real-time simulation of magnetic bearings
Article dans une revue avec comité de lecture
Author
Date
2024-02Journal
Advanced Modeling and Simulation in Engineering SciencesAbstract
The simulation of magnetic bearings involves highly non-linear physics, with high dependency on the input variation. Moreover, such a simulation is time consuming and can’t run, within realistic computation time for control purposes, when using classical computation methods. On the other hand, classical model reduction techniques fail to achieve the required precision within the allowed computation window. To address this complexity, this work proposes a combination of physics-based computing methods, model reduction techniques and machine learning algorithms, to tackle the requirements. The physical model used to represent the magnetic bearing is the classical Cauer Ladder Network method, while the model reduction technique is applied on the error of the physical model’s solution. Later on, in the latent space a machine learning algorithm is used to predict the evolution of the correction in the latent space. The results show an improvement of the solution without scarifying the computation time. The solution is computed in almost real-time (few milliseconds), and compared to the finite element reference solution.
Files in this item
- Name:
- PIMM_AMSES_2024_GHNATIOS.pdf
- Size:
- 3.061Mb
- Format:
- Description:
- A hybrid twin based on machine ...
Related items
Showing items related by title, author, creator and subject.
-
Article dans une revue avec comité de lectureGHNATIOS, Chady; ABISSET-CHAVANNE, Emmanuelle; AMMAR, Amine; CUETOS, Elias; DUVAL, Jean-Louis; CHINESTA SORIA, Francisco (Elsevier, 2019)This work aims at proposing a new procedure for parametric problems whose separated representation has been considered difficult, or whose SVD compression impacted the results in terms of performance and accuracy. The ...
-
Article dans une revue avec comité de lectureGHNATIOS, Chady; DELPLACE, Frank; BARASINSKI, Anais; DUVAL, Jean-Louis; CUETO, Elias; AMMAR, Amine; CHINESTA SORIA, Francisco (Wiley, 2020)Composite manufacturing processes usually proceed from preimpregnated preforms that are consolidated by simultaneously applying heat and pressure, so as to ensure a perfect contact compulsory for making molecular diffusion ...
-
Article dans une revue avec comité de lectureSIMACEK, Pavel; ADVANI, Suresh G.; GHNATIOS, Chady; CHINESTA SORIA, Francisco (Springer Verlag, 2020)In this work we develop a void filling and void motion dynamics model using volatile pressure and squeeze flow during tape placement process. The void motion and filling are simulated using a non-local model where their ...
-
Article dans une revue avec comité de lectureREILLE, Agathe; HASCOET, Nicolas; CUETO, Elias; DUVAL, Jean-Louis; KEUNINGS, Roland; GHNATIOS, Chady; AMMAR, Amine; CHINESTA SORIA, Francisco (Elsevier Masson, 2019)The present work aims at proposing a new methodology for learning reduced models from a small amount of data. It is based on the fact that discrete models, or their transfer function counterparts, have a low rank and then ...
-
Article dans une revue avec comité de lectureTERTRAIS, Hermine; IBANEZ PINILLO, Ruben; BARASINSKI, Anais; GHNATIOS, Chady; CHINESTA SORIA, Francisco (Elsevier, 2019)Many electrical and structural components are constituted of a stacking of multiple thin layers with different electromagnetic, mechanical and thermal properties. When 3D descriptions become compulsory the approximation ...