Artificial Neural Network (ANN) modeling of the pulsed heat load during ITER CS magnet operation

Autor:	Antonio Froio, Roberto Zanino, L. Savoldi Richard, Roberto Bonifetto, Stefano Carli, Arnaud Foussat
Rok vydání:	2014
Předmět:	Materials science Artificial neural network Nuclear engineering Artificial Neural Networks Nuclear fusion ITER Superconducting magnets Central Solenoid Pulsed heat load General Physics and Astronomy Solenoid Plasma Magnet General Materials Science Transient (oscillation) Heat load
Zdroj:	Cryogenics. 63:231-240
ISSN:	0011-2275
DOI:	10.1016/j.cryogenics.2014.03.003
Popis:	Artificial Neural Networks (ANNs) are applied to the development of a simplified transient model of the ITER Central Solenoid (CS), aiming at predicting the evolution of the pulsed heat load from the CS to the LHe bath during plasma operation. The ANNs are trained using the thermal–hydraulic evolution in the CS, computed with the 4C code, due to AC losses. The capability of the ANN model to predict the heat load to the LHe bath is successfully demonstrated in the case of different transients, among which a nominal plasma operating scenario. The gain in speed of the simplified model with respect to the 4C code results is by order of magnitudes, with a small loss of accuracy.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae3c9678f1d0e2266ff11d2177d11b3d https://doi.org/10.1016/j.cryogenics.2014.03.003 Zobrazit plný text záznamu Full Text from ScienceDirect