Frequency-domain complementary operators for finite-elements simulation

2000 ◽  
Vol 48 (4) ◽  
pp. 629-631 ◽  
Author(s):  
O.M. Ramahi
Keyword(s):  
Finite Elements ◽  
Frequency Domain ◽  
Finite Elements Simulation

Frequency domain approach for probabilistic flutter analysis using stochastic finite elements

Meccanica ◽  
2019 ◽  
Vol 54 (14) ◽  
pp. 2207-2225 ◽  
Author(s):  
Sandeep Kumar ◽  
Amit Kumar Onkar ◽  
Manjuprasad Maligappa
Keyword(s):  
Finite Elements ◽  
Frequency Domain ◽  
Flutter Analysis ◽  
Stochastic Finite Elements ◽  
Frequency Domain Approach

Modelling the Optical Properties of Metal Nanoparticles: Analytical vs Finite Elements Simulation

2015 ◽  
Vol 2 (1) ◽  
pp. 161-170 ◽  
Author(s):  
Eleonora Mencarelli ◽  
Livio Fanò ◽  
Luigi Tarpani ◽  
Loredana Latterini
Keyword(s):  
Optical Properties ◽  
Finite Elements ◽  
Metal Nanoparticles ◽  
Finite Elements Simulation

Deep 3D Convolution Neural Network Methods for Brain White Matter Hybrid Computational Simulations

2020 ◽  
Author(s):  
Xuehai Wu ◽  
Assimina A. Pelegri
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
White Matter ◽  
Finite Elements ◽  
Frequency Domain ◽  
Finite Element Methods ◽  
Material Properties ◽  
Output Data ◽  
Micro Scale ◽  
Brain White Matter

Abstract Material properties of brain white matter (BWM) show high anisotropy due to the complicated internal three-dimensional microstructure and variant interaction between heterogeneous brain-tissue (axon, myelin, and glia). From our previous study, finite element methods were used to merge micro-scale Representative Volume Elements (RVE) with orthotropic frequency domain viscoelasticity to an integral macro-scale BWM. Quantification of the micro-scale RVE with anisotropic frequency domain viscoelasticity is the core challenge in this study. The RVE behavior is expressed by a viscoelastic constitutive material model, in which the frequency-related viscoelastic properties are imparted as storage modulus and loss modulus for the composite comprised of axonal fibers and extracellular glia. Using finite elements to build RVEs with anisotropic frequency domain viscoelastic material properties is computationally very consuming and resource-draining. Additionally, it is very challenging to build every single RVE using finite elements since the architecture of each RVE is arbitrary in an infinite data set. The architecture information encoded in the voxelized location is employed as input data and is consequently incorporated into a deep 3D convolution neural network (CNN) model that cross-references the RVEs’ material properties (output data). The output data (RVEs’ material properties) is calculated in parallel using an in-house developed finite element method, which models RVE samples of axon-myelin-glia composites. This novel combination of the CNN-RVE method achieved a dramatic reduction in the computation time compared with directly using finite element methods currently present in the literature.

Age and gender effects on bone mass density variation: finite elements simulation

2016 ◽  
Vol 16 (2) ◽  
pp. 521-535 ◽  
Author(s):  
Abdelwahed Barkaoui ◽  
Rabeb Ben Kahla ◽  
Tarek Merzouki ◽  
Ridha Hambli
Keyword(s):  
Finite Elements ◽  
Bone Mass ◽  
Mass Density ◽  
Density Variation ◽  
Gender Effects ◽  
Bone Mass Density ◽  
Age And Gender ◽  
And Gender ◽  
Finite Elements Simulation
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