Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162835
Title: Computational acceleration of topology optimization using parallel computing and machine learning methods – analysis of research trends
Authors: Maksum, Y.
Amirli, A.
Amangeldi, A.
Inkarbekov, M.
Ding, Y.
Romagnoli, Alessandro
Rustamov, S.
Akhmetov, Bakytzhan
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Maksum, Y., Amirli, A., Amangeldi, A., Inkarbekov, M., Ding, Y., Romagnoli, A., Rustamov, S. & Akhmetov, B. (2022). Computational acceleration of topology optimization using parallel computing and machine learning methods – analysis of research trends. Journal of Industrial Information Integration, 28, 100352-. https://dx.doi.org/10.1016/j.jii.2022.100352
Journal: Journal of Industrial Information Integration
Abstract: Development of advanced structures using modern manufacturing methods has become attractive since they allow to improve system efficiency and performance, fuel consumption reduction, lightweighting to decrease weight and durability of structures, and many more. Designing tools such as topology optimization (TO) has contributed to such developments and facilitated in adapting new manufacturing methods such as 3D printing and computer numerical control machining in many areas of engineering and industry. TO requires computational resources, which can be significantly complex and time consuming when complicated designs and multiphysics problems are considered. To overcome these difficulties, computational acceleration techniques have been applied together with high performance computing. In the current work, various up-to-date research studies in computational acceleration of TO methods are analysed, classified and research trends are evaluated. Thus, the results of the work clearly shows that earlier works relied on central processing unit (CPU)-based computational acceleration techniques, while latest research studies mostly consider graphics processing unit (GPU) and machine learning (ML)-based approaches. The latter got significant attention within last few years and becoming one of the research areas in computational TO. From the reviewed works, it can be concluded that in all of the acceleration techniques, solid mechanics problems were mostly studied, while a few number of research studies are dedicated to heat transfer, fluid flow and electro thermomechanical applications.
URI: https://hdl.handle.net/10356/162835
ISSN: 2452-414X
DOI: 10.1016/j.jii.2022.100352
Rights: © 2022 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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