CUED Publications database

Design of a deep learning surrogate model for the prediction of FHR design parameters

Whyte, AJ and Xing, Z and Parks, GT and Shwageraus, E (2019) Design of a deep learning surrogate model for the prediction of FHR design parameters. In: UNSPECIFIED pp. 298-307..

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Abstract

© 2019 American Nuclear Society. All rights reserved. Following previous work by Xing and Shwageraus, a large corpus of data has been collected for simulated AGR-style fuel assembly design in FHRs. The results exhibit a nonlinear system response, so a ‘deep’ multi-layer perceptron surrogate model is designed and tested for prediction of design parameters. This neuro-surrogate regression model could be useful for the fast optimization of the design parameters, for example in multiobjective optimization problems, due to the extremely fast evaluation time. Source code is made available for the audit and authentication of the scientific method.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div A > Energy
Depositing User: Cron Job
Date Deposited: 06 Dec 2019 20:29
Last Modified: 30 Jul 2020 14:33
DOI: