1106 Grayscale-free topology optimization using the level-set method and zero-level boundary tracking mesh for the three-dimensional problem

2014 ◽  
Vol 2014.24 (0) ◽  
pp. _1106-1_-_1106-9_
Author(s):  
Seiichiro YAMANAKA ◽  
Shintaro YAMASAKI ◽  
Kikuo FUJITA
Keyword(s):  
Topology Optimization ◽  
Level Set Method ◽  
Level Set ◽  
Three Dimensional ◽  
Dimensional Problem ◽  
Boundary Tracking ◽  
Zero Level

A Parametric Level Set Method for Topology Optimization based on Deep Neural Network (DNN)

2021 ◽  
pp. 1-14
Author(s):  
Hao Deng ◽  
Albert C. To
Keyword(s):  
Neural Network ◽  
Topology Optimization ◽  
Differential Equations ◽  
Level Set Method ◽  
Level Set ◽  
Deep Neural Network ◽  
Implicit Function ◽  
Zero Level ◽  
Fully Connected ◽  
Parametric Level Set Method

Abstract This paper proposes a new parametric level set method for topology optimization based on Deep Neural Network (DNN). In this method, the fully connected deep neural network is incorporated into the conventional level set methods to construct an effective approach for structural topology optimization. The implicit function of level set is described by fully connected deep neural networks. A DNN-based level set optimization method is proposed, where the Hamilton-Jacobi partial differential equations (PDEs) are transformed into parametrized ordinary differential equations (ODEs). The zero-level set of implicit function is updated through updating the weights and biases of networks. The parametrized reinitialization is applied periodically to prevent the implicit function from being too steep or too flat in the vicinity of its zero-level set. The proposed method is implemented in the framework of minimum compliance, which is a well-known benchmark for topology optimization. In practice, designers desire to have multiple design options, where they can choose a better conceptual design base on their design experience. One of the major advantages of DNN-based level set method is capable to generate diverse and competitive designs with different network architectures. Several numerical examples are presented to verify the effectiveness of proposed DNN-based level set method.

Element-local level set method for three-dimensional dynamic crack growth

2009 ◽  
Vol 80 (12) ◽  
pp. 1520-1543 ◽  
Author(s):  
Qinglin Duan ◽  
Jeong-Hoon Song ◽  
Thomas Menouillard ◽  
Ted Belytschko
Keyword(s):  
Crack Growth ◽  
Level Set Method ◽  
Level Set ◽  
Local Level ◽  
Three Dimensional ◽  
Dynamic Crack ◽  
Dynamic Crack Growth ◽  
Local Level Set

scholarly journals Computing three-dimensional two-phase flows with a mass-conserving level set method

2008 ◽  
Vol 11 (4-6) ◽  
pp. 221-235 ◽  
Author(s):  
S. P. van der Pijl ◽  
A. Segal ◽  
C. Vuik ◽  
P. Wesseling
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Three Dimensional ◽  
Two Phase Flows ◽  
Two Phase

Topology optimization of hyperelastic structures using a level set method

2017 ◽  
Vol 351 ◽  
pp. 437-454 ◽  
Author(s):  
Feifei Chen ◽  
Yiqiang Wang ◽  
Michael Yu Wang ◽  
Y.F. Zhang
Keyword(s):  
Topology Optimization ◽  
Level Set Method ◽  
Level Set

scholarly journals A topology optimisation for three-dimensional acoustics with the level set method and the fast multipole boundary element method

2014 ◽  
Vol 1 (4) ◽  
pp. CM0039-CM0039 ◽  
Author(s):  
Hiroshi ISAKARI ◽  
Kohei KURIYAMA ◽  
Shinya HARADA ◽  
Takayuki YAMADA ◽  
Toru TAKAHASHI ◽  
...  
Keyword(s):  
Boundary Element Method ◽  
Boundary Element ◽  
Level Set Method ◽  
Level Set ◽  
Three Dimensional ◽  
Topology Optimisation ◽  
Fast Multipole ◽  
Element Method
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