|Title:||OpenImpala: OPEN source IMage based PArallisable Linear Algebra solver||Authors:||Le Houx, James
|Language:||eng||Keywords:||High-performance computing;Image-based modelling;Li-ion battery||Subject (DDC):||DDC::500 Naturwissenschaften und Mathematik::540 Chemie::541 Physikalische Chemie||Issue Date:||4-Jun-2021||Publisher:||Elsevier||Document Type:||Article||Journal / Series / Working Paper (HSU):||SoftwareX||Volume:||15||Issue:||July 2021||Pages:||ca. 5 Seiten||Publisher Place:||Amsterdam [u.a.]||Abstract:||
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.
|Organization Units (connected with the publication):||Computational Material Design||ISSN:||2352-7110||Publisher DOI:||10.1016/j.softx.2021.100729|
|Appears in Collections:||Publications of the HSU Researchers|
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