scholarly journals Viscoelastic Effects on Drop Deformation Using a Machine Learning-Enhanced, Finite Element method

Polymers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1652
Author(s):  
Juan Luis Prieto
Keyword(s):  
Machine Learning ◽  
Finite Element Method ◽  
Finite Element ◽  
Stress Tensor ◽  
Numerical Study ◽  
Dynamics Simulation ◽  
Machine Learning Techniques ◽  
Drop Deformation ◽  
Viscoelastic Effects ◽  
Element Method

This paper presents a numerical study of the viscoelastic effects on drop deformation under two configurations of interest: steady shear flow and complex flow under gravitational effects. We use a finite element method along with Brownian dynamics simulation techniques that avoid the use of closed-form, constitutive equations for the “micro-”scale, studying the viscoelastic effects on drop deformation using an interface capturing technique. The method can be enhanced with a variance-reduced approach to the stochastic modeling, along with machine learning techniques to reconstruct the shape of the polymer stress tensor in complex problems where deformations can be dramatic. The results highlight the effects of viscoelasticity on shape, the polymer stress tensor, and flow streamlines under the analyzed configurations.

scholarly journals Numerical study of Fisher's equation by a Petrov-Galerkin finite element method

1991 ◽  
Vol 33 (1) ◽  
pp. 27-38 ◽  
Author(s):  
S. Tang ◽  
R. O. Weber
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Numerical Study ◽  
Galerkin Finite Element Method ◽  
Linear Diffusion ◽  
Galerkin Finite Element ◽  
Fisher’S Equation ◽  
Nonlinear Reaction ◽  
Fisher's Equation ◽  
Element Method

AbstractFisher's equation, which describes a balance between linear diffusion and nonlinear reaction or multiplication, is studied numerically by a Petrov-Galerkin finite element method. The results show that any local initial disturbance can propagate with a constant limiting speed when time becomes sufficiently large. Both the limiting wave fronts and the limiting speed are determined by the system itself and are independent of the initial values. Comparing with other studies, the numerical scheme used in this paper is satisfactory with regard to its accuracy and stability. It has the advantage of being much more concise.

scholarly journals Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations

2020 ◽  
Vol 143 ◽  
pp. 113083 ◽  
Author(s):  
Oscar J. Pellicer-Valero ◽  
María José Rupérez ◽  
Sandra Martínez-Sanchis ◽  
José D. Martín-Guerrero
Keyword(s):  
Machine Learning ◽  
Finite Element Method ◽  
Finite Element ◽  
Real Time ◽  
Biomechanical Modeling ◽  
Learning Models ◽  
Machine Learning Models ◽  
Element Method

Numerical Study of Hydrodynamic Forces on a Submarine Piggyback Pipeline Under Wave Action

2012 ◽  
Author(s):  
Xiaofei Cheng ◽  
Yongxue Wang ◽  
Bing Ren ◽  
Guoyu Wang
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Numerical Model ◽  
Stokes Equations ◽  
Numerical Study ◽  
Hydrodynamic Forces ◽  
Wave Action ◽  
Numerical Results ◽  
Physical Model Test ◽  
Element Method

In the paper, a 2D numerical model is established to simulate the hydrodynamic forces on a submarine piggyback pipeline under regular wave action. The two-dimensional Reynolds-averaged Navier-Stokes equations with a κ-ω turbulence model closure are solved by using a three-step Taylor-Galerkin finite element method (FEM). A Computational Lagrangian-Eulerian Advection Remap Volume of Fluid (CLEAR-VOF) method is employed to simulate free surface problems, which is inherently compatible with unstructured meshes and finite element method. The numerical results of in-line force and lift (transverse) force on the piggyback pipeline for e/D = G/D = 0.25 and KC = 25.1 are compared with physical model test results, which are conducted in a marine environmental flume in the State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, China. It is indicated that the numerical results coincide with the experimental results and that the numerical model can be used to predict the hydrodynamic forces on the piggyback pipeline under wave action. Based on the numerical model, the surface pressure distribution and the motion of vortices around the piggyback pipeline for e/D = G/D = 0.25, KC = 25.1 are investigated, and a characteristic vortex pattern around the piggyback pipeline denoted “anti-phase-synchronized” pattern is recognized.

Simulation of Earing during Deep Drawing of bcc Steel by Use of a Texture Component Crystal Plasticity Finite Element Method

2005 ◽  
Vol 495-497 ◽  
pp. 1529-1534 ◽  
Author(s):  
Dierk Raabe ◽  
Franz Roters ◽  
Yan Wen Wang
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Crystal Plasticity ◽  
Texture Component ◽  
Numerical Study ◽  
Texture Evolution ◽  
Plastic Anisotropy ◽  
Crystal Plasticity Finite Element ◽  
Cup Drawing ◽  
Element Method

We present a numerical study on the influence of crystallographic texture on the earing behavior of a low carbon steel during cup drawing. The simulations are conducted by using the texture component crystal plasticity finite element method which accounts for the full elastic-plastic anisotropy of the material and for the explicit incorporation of texture including texture update. Several important texture components that typically occur in commercial steel sheets were selected for the study. By assigning different spherical scatter widths to them the resulting ear profiles were calculated under consideration of texture evolution. The study reveals that 8, 6, or 4 ears can evolve during cup drawing depending on the starting texture. An increasing number of ears reduces the absolute ear height. The effect of the orientation scatter width (texture sharpness) on the sharpness of the ear profiles was also studied. It was observed that an increase in the orientation scatter of certain texture components entails a drop in ear sharpness while for others the effect is opposite.

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