Implementation of Topological Derivative as an Evolutionary Approach
Lecture Notes in Mechanical Engineering, ISSN: 2195-4364, Page: 1511-1520
2020
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
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Conference Paper Description
Structural topology optimization finds the optimal material distribution in the given domain by optimizing the objective function subject to the loading and boundary constraints. Solid Isotropic Material Penalization (SIMP) method is widely used to solve topology optimization due to its simplicity in implementation but the resulting optimal design has fuzzy elements. Topological derivative is another alternative method to solve the topology optimization that finds the variation of objective function due to a small perturbation. In this paper, we implement the topological derivative as an alternative to the SIMP method to get the optimal design without any fuzzy elements. The implementation of the topological derivative as an evolutionary approach presented in this work gives the intermediate designs from full solid to required volume which are fuzzy element free.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85079154876&origin=inward; http://dx.doi.org/10.1007/978-981-15-0124-1_132; http://link.springer.com/10.1007/978-981-15-0124-1_132; http://link.springer.com/content/pdf/10.1007/978-981-15-0124-1_132; https://doi.org/10.1007%2F978-981-15-0124-1_132; https://dx.doi.org/10.1007/978-981-15-0124-1_132; https://link.springer.com/chapter/10.1007/978-981-15-0124-1_132
Springer Science and Business Media LLC
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