Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloNeural network based prediction of heat flux profiles on STRIKE
Anno di pubblicazione2019
Formato
  • Elettronico
  • Cartaceo
Autore/iDelogu R.S.; Montisci A.; Pimazzoni A.; Serianni G.; Sias G.
Affiliazioni autoriConsorzio RFX (CNR, ENEA, INFN, Università di Padova, Acciaierie Venete SpA), Corso Stati Uniti 4 - 35127 Padova, Italy; Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy.
Autori CNR e affiliazioni
  • GIANLUIGI SERIANNI
Lingua/e
  • inglese
AbstractThe instrumented calorimeter STRIKE (Short-Time Retractable Instrumented Kalorimeter Experiment) has been designed with the main purpose of characterizing the SPIDER (Source for Production of Ion of Deuterium Extracted from Radio Frequency plasma) negative ion beam in terms of beam uniformity and divergence during short pulse operations. STRIKE is made of 16 1D Carbon Fiber Composite (CFC) tiles, intercepting the whole beam and observed on the rear side by infrared (IR) cameras. The front observation presents some drawbacks due to optically emitting layer caused by the excited gas between the beam source and the calorimeter, and the material sublimated from the calorimeter surfaces due to the heating itself. This paper proposes a Neural Network-based approach to solve the inverse non-linear problem of determining the energy flux profile impinging on the calorimeter, considering the 2D temperature pattern measured on the rear side of the tiles. Most of the conventional methods used to evaluate the inverse heat flux are unbearably time consuming; since the objective is having a tool for heat flux evaluation for STRIKE real time operation, the need to have a ready-to-go instrument to understand the beam condition becomes stringent. For this reason, in this paper, a Multi-Layer Perceptron has been used to solve the problem. Once properly trained, the neural networks provide a fast evaluation of the impinging flux. Furthermore, there is no need to optimize any parameter since this operation is already included in the self-adjustment of the network weights during the training. The achieved results show the reliability of the proposed method both with stationary and non-stationary heat fluxes.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da2307
Pagine a2313
Pagine totali7
RivistaFusion engineering and design
Attiva dal 1986
Editore: North Holland. - Amsterdam
Paese di pubblicazione: Paesi Bassi
Lingua: inglese
ISSN: 0920-3796
Titolo chiave: Fusion engineering and design
Titolo proprio: Fusion engineering and design.
Titolo abbreviato: Fusion eng. des.
Numero volume della rivista146
Fascicolo della rivista-
DOI10.1016/j.fusengdes.2019.03.178
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85063884800)
  • ISI Web of Science (WOS) (Codice:000488313700198)
Parole chiaveSPIDER, Gas injection, Vacuum system, Neutral Beam Test Facility
Link (URL, URI)https://www.sciencedirect.com/science/article/pii/S0920379619305113
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione25/02/2019
Note/Altre informazioniAvailable online 4 March 2019. Electronic ISSN: 1873-7196 / The work leading to this publication has been funded partially byFusion for Energy under the Contract F4E-RFXPMS_A-WP-2018./ http://www.scopus.com/inward/record.url?eid=2-s2.0-85063884800&partnerID=q2rCbXpz
Strutture CNR
  • IGI — Istituto gas ionizzati
Moduli/Attività/Sottoprogetti CNR
  • DIT.AD020.017.001 : F4E-CONSORZIO RFX-NBTF
  • DIT.AD020.019.001 : attività di supporto a ITER e DEMO
Progetti Europei
Allegati
Neural network based prediction of heat flux profiles on STRIKE (documento privato )
Descrizione: L'allegato contiene l'articolo così come pubblicato. / The Annex contains the Article as published.
Tipo documento: application/pdf