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Computational Mechanics

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10-11-2017 | Original Paper

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

Author: C. Soize

Published in: Computational Mechanics | Issue 3/2018

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Abstract

This paper deals with the optimal design of a titanium mesoscale implant in a cortical bone for which the apparent elasticity tensor is modeled by a non-Gaussian random field at mesoscale, which has been experimentally identified. The external applied forces are also random. The design parameters are geometrical dimensions related to the geometry of the implant. The stochastic elastostatic boundary value problem is discretized by the finite element method. The objective function and the constraints are related to normal, shear, and von Mises stresses inside the cortical bone. The constrained nonconvex optimization problem in presence of uncertainties is solved by using a probabilistic learning algorithm that allows for considerably reducing the numerical cost with respect to the classical approaches.

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Title: Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm
Author: C. Soize
Publication date: 10-11-2017
Publisher: Springer Berlin Heidelberg
Published in: Computational Mechanics / Issue 3/2018
Print ISSN: 0178-7675
Electronic ISSN: 1432-0924
DOI: https://doi.org/10.1007/s00466-017-1509-x

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Improved nine-node shell element MITC9i with reduced distortion sensitivity

Modeling of power transmission and stress grading for corona protection

A new multi-layer approach for progressive damage simulation in composite laminates based on isogeometric analysis and Kirchhoff–Love shells. Part II: impact modeling

On the convergence rate of the Dirichlet–Neumann iteration for unsteady thermal fluid–structure interaction

Material and morphology parameter sensitivity analysis in particulate composite materials

A thick shell model based on reproducing kernel particle method and its application in geometrically nonlinear analysis