scholarly journals Considerations On Relationship Between Each Sensitivity Term and Results of Topology Optimization in Unsteady Oscillation Problem for 3d Cantilever Beam

2021 ◽  
Author(s):  
M. Kishida ◽  
T. Kurahashi
Keyword(s):  
Topology Optimization ◽  
Cantilever Beam ◽  
Oscillation Problem

scholarly journals Sequentially coupled shape and topology optimization for 2.5D and 3D beam models

2021 ◽  
Author(s):  
Zhijun Wang ◽  
Akke S. J. Suiker ◽  
Hèrm Hofmeyer ◽  
Twan van Hooff ◽  
Bert Blocken
Keyword(s):  
Topology Optimization ◽  
Shape Optimization ◽  
Cantilever Beam ◽  
Computational Efficiency ◽  
Optimization Approach ◽  
Beam Model ◽  
Shape And Topology Optimization ◽  
Beam Elements ◽  
And Topology ◽  
Beam Type

AbstractA sequentially coupled shape and topology optimization framework is presented in which the outer geometry and the internal topological layout of beam-type structures are optimized simultaneously. The outer geometry of the beam-type structures is parametrically described by non-uniform rational B-splines (NURBS), which guarantees a highly accurate description of the structural shape and enable an efficient control of the design domain with only a few control points. The computational efficiency of the coupled optimization approach is assured by applying a gradient-based optimization algorithm, for which the sensitivities are derived in closed form. The formulation of the coupled optimization approach is tailored toward 2.5D and full 3D representations of beam structures used in engineering applications. The 2.5D beam model, which has been taken from the literature, uses standard beam elements to simulate the beam response in the longitudinal direction, whereby the cross-sectional properties of the beam elements are calculated from additional 2D finite element method (FEM) analyses. A comparison study of a cantilever beam problem subjected to pure shape optimization and pure topology optimization illustrates that the 2.5D and 3D beam models lead to similar shape and topology designs, but that the 2.5D beam model has a significantly higher computational efficiency. Specifically, the computational times for the 2.5D model are about a factor 70 (shape optimization) and 1.4 (topology optimization) lower than for the 3D model, which indicates that in the coupled optimization approach the optimization of the shape provides the largest contribution to the higher computational efficiency of the 2.5D model. The coupled shape and topology optimization analysis subsequently performed on the 2.5D cantilever beam model demonstrates that the specific order at which the alternating shape and topology optimization increments are performed in the staggered update procedure turns out to have some influence on the computational speed and the value of the minimal compliance computed. Despite these differences, the final beam structures following from the different staggered update procedures illustrate how shape and topology can be efficiently optimized in an integrated, coupled fashion.

scholarly journals Multi-solution nature of topology optimization and its application in design for additive manufacturing

2019 ◽  
Vol 25 (9) ◽  
pp. 1475-1481 ◽  
Author(s):  
Hassan Rezayat ◽  
Jared Richard Bell ◽  
Alex J. Plotkowski ◽  
Sudarsanam S. Babu
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Cantilever Beam ◽  
Mechanical Performance ◽  
Design Tool ◽  
Image Correlation ◽  
Content Type ◽  
Fused Deposition ◽  
Starting Point ◽  
Initial Starting

Purpose The purpose of this paper is to introduce the multi-solution nature of topology optimization (TO) as a design tool for additive manufacturing (AM). The sensitivity of topologically optimized parts and manufacturing constraints to the initial starting point of the optimization process leading to structures with equivalent performance is explored. Design/methodology/approach A modified bi-directional evolutionary structural optimization (BESO) code was used as the numerical approach to optimize a cantilever beam problem and reduce the mass by 50 per cent. Several optimized structures with relatively equivalent mechanical performance were generated by changing the initial starting point of the TO algorithm. These optimized structures were manufactured using fused deposition modeling (FDM). The equivalence of strain distribution in FDM parts was tested with the digital image correlation (DIC) technique and compared with that from the modified BESO code. Findings The results confirm that TO could lead to a wide variety of non-unique solutions based on loading and manufacturability constraints. The modified BESO code was able to reduce the support structure needed to build the simple two-dimensional cantilever beam by 15 per cent while keeping the mechanical performance at the same level. Originality/value The originality of this paper lies in introduction and application of the multi-solution nature of TO for AM as a design tool for optimizing structures with minimized features in the overhang condition and the need for support structures.

scholarly journals Topology optimization using PETSc: a Python wrapper and extended functionality

2021 ◽  
Author(s):  
Thijs Smit ◽  
Niels Aage ◽  
Stephen J. Ferguson ◽  
Benedikt Helgason
Keyword(s):  
Topology Optimization ◽  
Open Source ◽  
Cantilever Beam ◽  
Real World ◽  
Large Scale ◽  
Source Code ◽  
Problem Definition ◽  
Potential User ◽  
Local Volume ◽  
Optimization Framework

AbstractThis paper presents a Python wrapper and extended functionality of the parallel topology optimization framework introduced by Aage et al. (Topology optimization using PETSc: an easy-to-use, fully parallel, open source topology optimization framework. Struct Multidiscip Optim 51(3):565–572, 2015). The Python interface, which simplifies the problem definition, is intended to expand the potential user base and to ease the use of large-scale topology optimization for educational purposes. The functionality of the topology optimization framework is extended to include passive domains and local volume constraints among others, which contributes to its usability to real-world design applications. The functionality is demonstrated via the cantilever beam, bracket and torsion ball examples. Several tests are provided which can be used to verify the proper installation and for evaluating the performance of the user’s system setup. The open-source code is available at https://github.com/thsmit/, repository $$\texttt {TopOpt\_in\_PETSc\_wrapped\_in\_Python}$$ TopOpt _ in _ PETSc _ wrapped _ in _ Python .

Topology Optimization of IPM Motors using Fourier Series

2017 ◽  
Vol 137 (3) ◽  
pp. 245-253
Author(s):  
Hidenori Sasaki ◽  
Hajime Igarashi
Keyword(s):  
Fourier Series ◽  
Topology Optimization

Topology Optimization of Superconducting Electromagnets for Particle Accelerators

2019 ◽  
Vol 139 (9) ◽  
pp. 568-575
Author(s):  
Yusuke Sakamoto ◽  
Daisuke Ishizuka ◽  
Tetsuya Matsuda ◽  
Kazuhiro Izui ◽  
Shinji Nishiwaki
Keyword(s):  
Topology Optimization ◽  
Particle Accelerators

Topology Optimization using Deep Learning

2020 ◽  
Vol 140 (12) ◽  
pp. 858-865
Author(s):  
Hidenori Sasaki ◽  
Yuki Hidaka ◽  
Hajime Igarashi
Keyword(s):  
Deep Learning ◽  
Topology Optimization
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