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...

Celý popis

Podrobná bibliografie

Vydáno v:	Nuclear engineering and design Ročník 429; s. 113587
Hlavní autoři:	Zubair Khalid, Rehan, Ahmed, Ibrahim, Ullah, Atta, Zio, Enrico, Khan, Asifullah
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.12.2024 Elsevier
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!