| Issue |
EPJ Web Conf.
Volume 247, 2021
PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
|
|
|---|---|---|
| Article Number | 12003 | |
| Number of page(s) | 8 | |
| Section | Fuel Performance and Management | |
| DOI | https://doi.org/10.1051/epjconf/202124712003 | |
| Published online | 22 February 2021 | |
https://doi.org/10.1051/epjconf/202124712003
SURROGATE MODEL OPTIMIZATION OF A ‘MICRO CORE’ PWR FUEL ASSEMBLY ARRANGEMENT USING DEEP LEARNING MODELS
University of Cambridge Department of Engineering, Trumpington Street, Cambridge, CB2 1PZ
ajw287@cam.ac.uk
gtp10@cam.ac.uk
Published online: 22 February 2021
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.
Key words: deep learning / fuel management / PWR / optimization, / surrogate model
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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