Dennis, A.A. orcid.org/0000-0002-3347-2747, Smyl, D.J., Stirling, C.G. et al. (1 more author) (2023) A branching algorithm to reduce computational time of batch models: application for blast analyses. International Journal of Protective Structures, 14 (2). pp. 135-167. ISSN 2041-4196
Abstract
Numerical analysis is increasingly used for batch modelling runs, with each individual model possessing a unique combination of input parameters sampled from a range of potential values. Whilst such an approach can help to develop a comprehensive understanding of the inherent unpredictability and variability of explosive events, or populate training/validation data sets for machine learning approaches, the associated computational expense is relatively high. Furthermore, any given model may share a number of common solution steps with other models in the batch, and simulating all models from birth to termination may result in large amounts of repetition. This paper presents a new branching algorithm that ensures calculation steps are only computed once by identifying when the parameter fields of each model in the batch becomes unique. This enables informed data mapping to take place, leading to a reduction in the required computation time. The branching algorithm is explained using a conceptual walk-through for a batch of 9 models, featuring a blast load acting on a structural panel in 2D. By eliminating repeat steps, approximately 50% of the run time can be saved. This is followed by the development and use of the algorithm in 3D for a practical application involving 20 complex containment structure models. In this instance, a ∼20% reduction in computational costs is achieved.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2022. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
Keywords: | Risk; probability; computation time; data; batch |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council 2574387 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 May 2022 11:11 |
Last Modified: | 10 Jul 2024 11:26 |
Status: | Published |
Publisher: | SAGE Publications |
Refereed: | Yes |
Identification Number: | 10.1177/20414196221085720 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:187106 |
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