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Adaptive Multilevel Subset Simulation with Selective Refinement

.Motivated by an application involving additively manufactured bioresorbable polymer scaffolds supporting bone tissue regeneration, we investigate the impact of uncertain geometry perturbations on the effective mechanical properties of elastic rods. To be more precise, we consider elastic rods model...

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Bibliographic Details
Main Authors: Elfverson, Daniel (Author) , Scheichl, Robert (Author) , Weissmann, Simon (Author) , Diaz de la O, Alejandro (Author)
Format: Article (Journal)
Language:English
Published: 2024
In: SIAM ASA journal on uncertainty quantification
Year: 2024, Volume: 12, Issue: 3, Pages: 932-963
ISSN:2166-2525
DOI:10.1137/22M1515240
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1137/22M1515240
Verlag, lizenzpflichtig, Volltext: https://epubs.siam.org/doi/10.1137/22M1515240
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Author Notes:D. Elfverson, R. Scheichl, S. Weissmann, F.A. Diaz De La O
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Summary:.Motivated by an application involving additively manufactured bioresorbable polymer scaffolds supporting bone tissue regeneration, we investigate the impact of uncertain geometry perturbations on the effective mechanical properties of elastic rods. To be more precise, we consider elastic rods modeled as three-dimensional linearly elastic bodies occupying randomly perturbed domains. Our focus is on a model where the cross-section of the rod is shifted along the longitudinal axis with stationary increments. To efficiently obtain accurate estimates on the resulting uncertainty of the effective elastic moduli, we use a combination of analytical and numerical methods. Specifically, we rigorously derive a one-dimensional surrogate model by analyzing the slender-rod Gamma-limit. Additionally, we establish qualitative and quantitative stochastic homogenization results for the one-dimensional surrogate model. To compare the fluctuations of the surrogate with the original three-dimensional model, we perform numerical simulations by means of finite element analysis and Monte Carlo methods.
Item Description:Gefördert durch: Deutsche Forschungsgemeinschaft (DFG): EXC 2181/1 - 390900948
Gesehen am 18.03.2025
Physical Description:Online Resource
ISSN:2166-2525
DOI:10.1137/22M1515240

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