Finite element-based probabilistic analysis tool for orthopaedic applications

Easley, Sarah K.; Pal, Saikat; Tomaszewski, Paul R.; Petrella, Anthony J.; Rullkoetter, Paul J.; Laz, Peter J.

doi:10.1016/j.cmpb.2006.09.013

Cited by 81 publications

(58 citation statements)

References 18 publications

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“…While the AMV method is a discrete and approximate solution, it has shown excellent agreement with Monte Carlo analysis for monotonic system. This has been shown in other applications which utilize FE analysis with geometric perturbations and realistic loading conditions [8,9]. The major advantage of the AMV is that it requires a small number of trials, which saves significant computational time when the CFD analysis requires multiple iterations to reach convergence.…”

Section: Probabilistic Modelmentioning

confidence: 98%

A Combined Probabilistic and Optimization Approach for Improved Chemical Mixing Systems Design

Opgenorth¹,

McDermott²,

Laz³

et al. 2011

ENG

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A design analysis of a mixing nozzle was performed using a combination of probabilistic and optimization techniques. A novel approach was utilized where probabilistic analysis was used to reduce the number of geometric constraints based on sensitivity factors. An optimization algorithm used only the most significant parameters to maximize mixing. A second probabilistic analysis was performed after optimization was complete in order to quantitatively predict the effects of manufacturing tolerances on mixing performance. This process for automated design is attractive over full parameter optimization techniques due to the computational efficiency resulting from an intelligent reduction in evaluated variables.

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Section: Probabilistic Modelmentioning

confidence: 98%

A Combined Probabilistic and Optimization Approach for Improved Chemical Mixing Systems Design

Opgenorth¹,

McDermott²,

Laz³

et al. 2011

ENG

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show abstract

“…In order to reduce analysis time, especially when deterministic model run times are long, a series of approximate most probable point (MPP) methods have been utilized that are more computationally efficient [1,3]. The MPP represents the combination of input parameter values that predict performance at a specified probability level (Fig.…”

Section: Overview Of Analysis Methodsmentioning

confidence: 99%

A review of probabilistic analysis in orthopaedic biomechanics

Laz

Browne

2010

Proc Inst Mech Eng H

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Probabilistic analysis methods are being increasingly applied in the orthopaedics and biomechanics literature to account for uncertainty and variability in subject geometries, properties of various structures, kinematics and joint loading, as well as uncertainty in implant alignment. As a complement to experiments, finite element modelling, and statistical analysis, probabilistic analysis provides a method of characterizing the potential impact of variability in parameters on performance. This paper presents an overview of probabilistic analysis and a review of biomechanics literature utilizing probabilistic methods in structural reliability, kinematics, joint mechanics, musculoskeletal modelling, and patient-specific representations. The aim of this review paper is to demonstrate the wide range of applications of probabilistic methods and to aid researchers and clinicians in better understanding probabilistic analyses.

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“…With regard to the growing potential of finite-element-analysis (FEA) in the field of orthopedic biomechanics [16][17][18], this study aims to contribute for the development of this kind of tools. Using a commercial model of an AO external fixator in the uniplanar-unilateral configuration and a side beam, the optimum position of the fixator mechanical components are obtained for the case of transverse fractures of the tibial diaphysis.…”

Section: Introductionmentioning

confidence: 99%