Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.4766
Publisher DOI: 10.23967/eccomas.2022.237
Title: Robust topology optimization of static systems with unilateral frictional contact
Language: English
Authors: Schmidt, Timo Hendrik  
Kriegesmann, Benedikt  
Seifried, Robert  
Keywords: Robust topology optimization; Contact; Uncertainty
Issue Date: Jun-2022
Source: 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2022)
Abstract (english): 
In this paper a robust topology optimization algorithm for linear elastic structures in unilateral contact is presented. The deformation of the linear elastic structure is constrained by support structures that are modeled with the help of Signorini’s contact conditions. The contact conditions in turn are enforced with the augmented Lagrangian approach. Doing so, the robust optimization considers uncertainties at the support such as manufacturing tolerances and its local friction behavior. Due to high numerical costs in robust optimization, the first-order second-moment approach is used to approximate the mean and variance of the objective.
This approximation results in minimal additional costs to approximate the mean, the variance and their gradients. Consequentially, a gradient-based optimization algorithm can be used to minimize a weighted sum of both. The results show that the presented approach indeed improves the robustness with respect to uncertain contact conditions compared to a deterministic optimization.
Conference: 8th European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS 2022 
URI: http://hdl.handle.net/11420/14257
DOI: 10.15480/882.4766
Institute: Mechanik und Meerestechnik M-13 
Strukturmechanik im Leichtbau M-24 
Document Type: Chapter/Article (Proceedings)
Project: Simulationsbasierte Entwurfsoptimierung dynamischer Systeme unter Unsicherheiten 
Funded by: Freie und Hansestadt Hamburg (FHH) 
License: CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike) CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike)
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