SURROGATE MODEL OPTIMIZATION OF A 'MICRO CORE' PWR FUEL ASSEMBLY ARRANGEMENT USING DEEP LEARNING MODELS.

Autor:	Margulis, M., Blaise, P., Whyte, Andy, Parks, Geoff
Předmět:	DEEP learning NUCLEAR fuel management NUCLEAR fission NUCLEAR reactors NEUTRON diffusion
Zdroj:	EPJ Web of Conferences; 2021, Vol. 247, p1-8, 8p
Abstrakt:	This paper investigates the applicability of surrogate model optimization (SMO) using deep learning regression models to automatically embed knowledge about the objective function into the optimization process. This paper demonstrates two deep learning SMO methods for calculating simple neutronics parameters. Using these models, SMO returns results comparable with those from the early stages of direct iterative optimization. However, for this study, the cost of creating the training set outweighs the benefits of the surrogate models. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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