A multiphysics-based artificial neural networks model for atherosclerosis

Atherosclerosis is a medical condition involving the hardening and/or thickening of arteries' walls. Mathematical multi-physics models have been developed to predict the development of atherosclerosis under different conditions. However, these models are typically computationally expensive. In this study, we used machine learning techniques, particularly artificial neural networks (ANN), to enhance the computational efficiency of these models. A database of multi-physics Finite Element Method (FEM) simulations was created and used for training and validating an ANN model. The model is capable of quick and accurate prediction of atherosclerosis development. A remarkable computational gain is obtained using the ANN model compared to the original FEM simulations

An efficient numerical integration algorithm for the single mode compressible Leonov model – Complas XI

In this contribution, an algorithm for numerical integration of the Leonov elastoviscoplastic model is proposed. The operator split methodology and the Newton-Raphson method are used to derive the state update algorithm and obtain the numerical solution of the discretized evolution equations. Particular effort is devoted to the reduction of the number of required residual equations in order to have a more efficient numerical implementation. The consistent tangent module is expressed in a closed form as a result of the exact linearization of the discretized evolution equations. The performance of the algorithm is validated through comparison with existing experimental data

A multiscale deep learning model for elastic properties of woven composites

Time-consuming and costly computational analysis expresses the need for new methods for generalizing multiscale analysis of composite materials. Combining neural networks and multiscale modeling is favorable for bypassing expensive lower-scale material modeling, and accelerating coupled multi-scale analyses (FE2). In this work, neural networks are used to replace the time-consuming micromechanical finite element analysis of unidirectional composites, representing the local material properties of yarns in woven fabric composites in a multiscale framework. Leveraging the fast multiscale data generation procedure, we presented a second neural networks model to estimate the elastic engineering coefficients of a particular weave architecture based on a broad range of dry resin and fiber properties and yarn fiber volume fraction. As an outcome, this paper provides the user with a generalized, neural network-based approach to tackle the balance of computational efficiency and accuracy in the multiscale analysis of elastic woven composites

A micromechanics-based recurrent neural networks model for path-dependent cyclic deformation of short fiber composites

The macroscopic response of short fiber reinforced composites (SFRCs) is dependent on an extensive range of microstructural parameters. Thus, micromechanical modeling of these materials is challenging and in some cases, computationally expensive. This is particularly important when path-dependent plastic behavior is needed to be predicted. A solution to this challenge is to enhance micromechanical solutions with machine learning techniques such as artificial neural networks. In this work, a recurrent deep neural network model is trained to predict the path-dependent elasto-plastic stress response of SFRCs, given the microstructural parameters and the strain path. Micromechanical mean-field simulations are conducted to create a database for training the validating the model. The model gives very accurate predictions in a computationally efficient manner when compared with independent micromechanical simulations

A computationally efficient coupled multi-scale model for short fiber reinforced composites

A coupled multi-scale (macro–micro) model is developed to predict non-linear elasto-plastic behavior of short fiber reinforced composites. At the microscopic level, a recently proposed micro-mechanical model, developed based on a two-step orientation averaging approach, is used. A wide range of micro-structural parameters, including matrix and fiber constitutive parameters, fiber volume fraction and fiber aspect ratio, can be accommodated in the model. Different interactions including Voigt, Reuss and a self-consistent assumption are considered in the model. This micro-mechanical model is then incorporated in a Finite Element model of the macro-scale problem, enabling coupled macro–micro simulations of real-life structures/specimens. Numerical examples and comparisons with experimental data, taken from literature, show that the model gives good predictions. Besides, several strategies and techniques are employed to improve the computational efficiency of the model. These techniques include replacing originally utilized trapezoidal integration (for fiber orientations and calculation of the Eshelby tensor) with more efficient integration schemes, and using a more efficient method for data storage. Comparisons of the computational efforts shows that these improvements substantially decreased the computational cost of the model

NURBS distance fields for extremely curved cracks

This paper presents the first methodology that combines a meshless method and the exact representation of cracks using Non-Uniform Rational B-Splines (NURBS). The methodology consists on developing an enrichment function based on distance functions to NURBS curves.The examples show the potential of the proposed approach and demonstrate the applicability to problems involving complex cracks that appear in sol-gel films

Oxygen reduction reaction features in neutral media on glassy carbon electrode functionalized by chemically prepared gold nanoparticles

Gold nanoparticles (AuNPs) were prepared by chemical route using 4 different protocols by varying reducer, stabilizing agent and solvent mixture. The obtained AuNPs were characterized by transmission electronic microscopy (TEM), UV-Visible and zeta potential measurements. From these latter surface charge densities were calculated to evidence the effect of the solvent mixture on AuNPs stability. The AuNPs were then deposited onto glassy carbon (GC) electrodes by drop-casting and the resulting deposits were characterized by cyclic voltammetry (CV) in H2SO4 and field emission gun scanning electron microscopy (FEG-SEM). The electrochemical kinetic parameters of the 4 different modified electrodes towards oxygen reduction reaction (ORR) in neutral NaCl-NaHCO3 media (0.15 M / 0.028 M, pH 7.4) were evaluated by rotating disk electrode voltammetry and subsequent Koutecky-Levich treatment. Contrary to what we previously obtained with electrodeposited AuNPs [Gotti et al., Electrochim. Acta 2014], the highest cathodic transfer coefficients were not obtained on the smallest particles, highlighting the influence of the stabilizing ligand together with the deposits morphology on the ORR kinetics

A multiphysics-based artificial neural networks model for atherosclerosis

Atherosclerosis is a medical condition involving the hardening and/or thickening of arteries' walls. Mathematical multi-physics models have been developed to predict the development of atherosclerosis under different conditions. However, these models are typically computationally expensive. In this study, we used machine learning techniques, particularly artificial neural networks (ANN), to enhance the computational efficiency of these models. A database of multi-physics Finite Element Method (FEM) simulations was created and used for training and validating an ANN model. The model is capable of quick and accurate prediction of atherosclerosis development. A remarkable computational gain is obtained using the ANN model compared to the original FEM simulations