OpenImpala: OPEN source IMage based PArallisable Linear Algebra solver

Image-based modelling has emerged as a popular method within the field of lithium-ion battery modelling due to its ability to represent the heterogeneity of the porous electrodes. A common challenge from image-based modelling is the size of 3D tomography datasets, which can be of the order of several billion voxels. Previously, different approximation methods have been used to simplify the computational problem, but each of these come with associated limitations. Here we develop a data-driven, fully parallelisable, image-based modelling framework called OpenImpala. Micro X-ray computed tomography (CT) is used to obtain 3D microstructural data from samples non-destructively. These 3D datasets are then directly used as the computational domain for finite-differences based direct physical modelling (e.g. to solve the diffusion equation directly on the CT obtained datasets). OpenImpala then calculates the equivalent homogenised transport coefficients for the given microstructure. These coefficients are written into parameterised files for direct compatibility with two popular continuum battery models: PyBamm and DandeLiion, facilitating the link between different scales of computational battery modelling. OpenImpala has been shown to scale well with an increasing number of computational cores on distributed memory architectures, making it applicable to large datasets typical of modern tomography.

High-performance computing, Image-based modelling, Li-ion battery

10.1016/j.softx.2021.100729

2352-7110

Houx, James Le

c3130024-47fa-44ee-b10e-a4d76877fb9a

Kramer, Denis

1faae37a-fab7-4edd-99ee-ae4c30d3cde4

July 2021

Houx, James Le

c3130024-47fa-44ee-b10e-a4d76877fb9a

Kramer, Denis

1faae37a-fab7-4edd-99ee-ae4c30d3cde4

Houx, James Le and Kramer, Denis (2021) OpenImpala: OPEN source IMage based PArallisable Linear Algebra solver. SoftwareX, 15, [100729]. (doi:10.1016/j.softx.2021.100729).

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Published date: July 2021

Additional Information: Funding Information: This work was financially supported by the EPSRC Centre for Doctoral Training (CDT) in Energy Storage and its Applications [grant ref: EP/R021295/1 ]. We thank the Vis laboratory at the University of Southampton. The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. The authors also thank the Faraday Institution for discussion of how to interact with PyBamm and DandeLiion. Funding Information: This work was financially supported by the EPSRC Centre for Doctoral Training (CDT) in Energy Storage and its Applications [grant ref: EP/R021295/1]. We thank the ? Vis laboratory at the University of Southampton. The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. The authors also thank the Faraday Institution for discussion of how to interact with PyBamm and DandeLiion. Publisher Copyright: © 2021

Keywords: High-performance computing, Image-based modelling, Li-ion battery