Deep Neural Network and Monte Carlo Tree Search applied to Fluid-Structure Topology Optimization
File(s)
Published version
Author(s)
Gaymann, Audrey
Montomoli, Francesco
Type
Journal Article
Abstract
This paper shows the application of Deep Neural Network algorithms for Fluid-Structure Topology Optimization. The strategy offered is a new concept which can be added to the current process used to study Topology Optimization with Cellular Automata, Adjoint and Level-Set methods.
The design space is described by a computational grid where every cell can be in two states: fluid or solid. The system does not require human intervention and learns through an algorithm based on Deep Neural Network and Monte Carlo Tree Search. In this work the objective function for the optimization is an incompressible fluid solver but the overall optimization process is independent from the solver.
The test case used is a standard duct with back facing step where the optimizer aims at minimizing the pressure losses between inlet and outlet. The results obtained with the proposed approach are compared to the solution via a classical adjoint topology optimization code.
The design space is described by a computational grid where every cell can be in two states: fluid or solid. The system does not require human intervention and learns through an algorithm based on Deep Neural Network and Monte Carlo Tree Search. In this work the objective function for the optimization is an incompressible fluid solver but the overall optimization process is independent from the solver.
The test case used is a standard duct with back facing step where the optimizer aims at minimizing the pressure losses between inlet and outlet. The results obtained with the proposed approach are compared to the solution via a classical adjoint topology optimization code.
Date Issued
2019-11-04
Date Acceptance
2019-09-13
Citation
Scientific Reports, 2019, 9 (1), pp.1-16
ISSN
2045-2322
Publisher
Nature Publishing Group
Start Page
1
End Page
16
Journal / Book Title
Scientific Reports
Volume
9
Issue
1
Copyright Statement
© The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per-mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Sponsor
Engineering and Physical Sciences Research Council
Identifier
https://www.nature.com/articles/s41598-019-51111-1
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
LEVEL SET METHOD
Publication Status
Published online
Article Number
15916
Date Publish Online
2019-11-04