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Title: | Implementation of Dual Number Automatic Differentiation with John Newman's BAND Algorithm | Authors: | BRADY, Nick Mees, Maarten Vereecken, Philippe M. SAFARI, Momo |
Issue Date: | 2021 | Publisher: | ELECTROCHEMICAL SOC INC | Source: | Journal of the Electrochemical Society, 168 (11) (Art N° 113501) | Abstract: | This paper asserts that the development of continuum-scale mathematical models utilizing John Newman’s BAND subroutine can be simplified through the use of dual number automatic differentiation. This paper covers the salient features of the BAND algorithm as well as dual numbers and how they can be leveraged to algorithmically linearize systems of partial differential equations; these two concepts can be combined to produce accurate and computationally efficient models while significantly reducing the amount of personnel time necessary by eliminating the time-consuming process of equation linearization. As a result, this methodology facilitates more rapid model prototyping and establishes a more intuitive relationship between the numerical model and the differential equations. By utilizing an existing and validated programming module, dnadmod, these advantages are achieved without burdening the general user with significant additional programming overhead. | Keywords: | Batteries-Li-ion;Theory and Modelling;Batteries-Lithium | Document URI: | http://hdl.handle.net/1942/35814 | ISSN: | 0013-4651 | e-ISSN: | 1945-7111 | DOI: | 10.1149/1945-7111/ac3274 | ISI #: | 000716131800001 | Rights: | 2021 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited. This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives 4.0 License (CC BYNC-ND, http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is not changed in any way and is properly cited. For permission for commercial reuse, please email: permissions@ioppublishing.org. [DOI: 10.1149/1945-7111/ac3274] | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
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Brady_2021_J._Electrochem._Soc._168_113501.pdf | Published version | 2.81 MB | Adobe PDF | View/Open |
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