Pores for thought: The use of generative adversarial networks for the stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries
The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices. This work implements a deep convolutional generative adversarial network (DC-GAN) for generating realistic n-phase microstructural data. The same network ar...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
17.02.2020
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Subjects | |
Online Access | Get full text |
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Summary: | The generation of multiphase porous electrode microstructures is a critical
step in the optimisation of electrochemical energy storage devices. This work
implements a deep convolutional generative adversarial network (DC-GAN) for
generating realistic n-phase microstructural data. The same network
architecture is successfully applied to two very different three-phase
microstructures: A lithium-ion battery cathode and a solid oxide fuel cell
anode. A comparison between the real and synthetic data is performed in terms
of the morphological properties (volume fraction, specific surface area,
triple-phase boundary) and transport properties (relative diffusivity), as well
as the two-point correlation function. The results show excellent agreement
between for datasets and they are also visually indistinguishable. By modifying
the input to the generator, we show that it is possible to generate
microstructure with periodic boundaries in all three directions. This has the
potential to significantly reduce the simulated volume required to be
considered representative and therefore massively reduce the computational cost
of the electrochemical simulations necessary to predict the performance of a
particular microstructure during optimisation. |
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DOI: | 10.48550/arxiv.2003.11632 |