Enhancing accuracy of prediction of critical heat flux in Circular channels by ensemble of deep sparse autoencoders and deep neural Networks

•A novel ensemble of Deep Sparse AEs and DNN based CHF prediction method was proposed;•The method used an ensemble of deep sparse AEs to extract robust features from inputs and a DNN to predict the CHF;•A validation of the method on a comprehensive CHF experimental dataset in vertical tubes was perf...

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Vydané v:	Nuclear engineering and design Ročník 429; s. 113587
Hlavní autori:	Zubair Khalid, Rehan, Ahmed, Ibrahim, Ullah, Atta, Zio, Enrico, Khan, Asifullah
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.12.2024 Elsevier
Predmet:	Critical Heat Flux Deep Neural Network Engineering Sciences Ensemble Model Nuclear Reactors Process Safety Sparse Autoencoders Critical Heat Flux Nuclear Reactors Sparse Autoencoders Deep Neural Network Process Safety Ensemble Model
ISSN:	0029-5493, 1872-759X
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