Mánek, P;
Van Goffrier, G;
Gopakumar, V;
Nikolaou, N;
Shimwell, J;
Waldmann, I;
(2023)
Fast regression of the tritium breeding ratio in fusion reactors.
Machine Learning: Science and Technology
, 4
(1)
, Article 015008. 10.1088/2632-2153/acb2b3.
Preview |
Text
Fast regression of the tritium breeding ratio in fusion reactors.pdf - Published Version Download (5MB) | Preview |
Abstract
The tritium breeding ratio (TBR) is an essential quantity for the design of modern and next-generation D-T fueled nuclear fusion reactors. Representing the ratio between tritium fuel generated in breeding blankets and fuel consumed during reactor runtime, the TBR depends on reactor geometry and material properties in a complex manner. In this work, we explored the training of surrogate models to produce a cheap but high-quality approximation for a Monte Carlo (MC) TBR model in use at the UK Atomic Energy Authority. We investigated possibilities for dimensional reduction of its feature space, reviewed 9 families of surrogate models for potential applicability, and performed hyperparameter optimization. Here we present the performance and scaling properties of these models, the fastest of which, an artificial neural network, demonstrated <?CDATA $R^2 = 0.985$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>=</mml:mo> <mml:mn>0.985</mml:mn> </mml:math> and a mean prediction time of <?CDATA $0.898~\mu\textrm{s}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>0.898</mml:mn> <mml:mtext> </mml:mtext> <mml:mi>μ</mml:mi> <mml:mrow> <mml:mtext>s</mml:mtext> </mml:mrow> </mml:math> , representing a relative speedup of <?CDATA $8\times 10^6$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>8</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>6</mml:mn> </mml:msup> </mml:math> with respect to the expensive MC model. We further present a novel adaptive sampling algorithm, Quality-Adaptive Surrogate Sampling, capable of interfacing with any of the individually studied surrogates. Our preliminary testing on a toy TBR theory has demonstrated the efficacy of this algorithm for accelerating the surrogate modelling process.
| Type: | Article |
|---|---|
| Title: | Fast regression of the tritium breeding ratio in fusion reactors |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/2632-2153/acb2b3 |
| Publisher version: | http://dx.doi.org/10.1088/2632-2153/acb2b3 |
| Language: | English |
| Additional information: | Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
| Keywords: | nuclear fusion, surrogate model, tritium breeding, regression, fast approximation, adaptive sampling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10164433 |
Archive Staff Only
 |
View Item |
