Introducing the sequential linear programming level-set method for topology optimization

Peter D. Dunning, H.Alicia Kim

Research output: Contribution to journalArticle

  • 22 Citations

Abstract

This paper introduces an approach to level-set topology optimization that can handle multiple constraints and simultaneously optimize non-level-set design variables. The key features of the new method are discretized boundary integrals to estimate function changes and the formulation of an optimization sub-problem to attain the velocity function. The sub-problem is solved using sequential linear programming (SLP) and the new method is called the SLP level-set method. The new approach is developed in the context of the Hamilton-Jacobi type level-set method, where shape derivatives are employed to optimize a structure represented by an implicit level-set function. This approach is sometimes referred to as the conventional level-set method. The SLP level-set method is demonstrated via a range of problems that include volume, compliance, eigenvalue and displacement constraints and simultaneous optimization of non-level-set design variables.
LanguageEnglish
Pages631-643
JournalStructural and Multidisciplinary Optimization
Volume51
Issue number3
DOIs
StatusPublished - Mar 2015

Fingerprint

Level Set Method
Topology Optimization
Shape optimization
Linear programming
Level Set
Optimise
Shape Derivative
Simultaneous Optimization
Hamilton-Jacobi
Boundary Integral
Compliance
Derivatives
Eigenvalue
Optimization
Formulation
Estimate
Range of data
Design

Cite this

Introducing the sequential linear programming level-set method for topology optimization. / Dunning, Peter D.; Kim, H.Alicia.

In: Structural and Multidisciplinary Optimization, Vol. 51, No. 3, 03.2015, p. 631-643.

Research output: Contribution to journalArticle

@article{ae99f635d4ae42b7bb624022bd1b26bc,
title = "Introducing the sequential linear programming level-set method for topology optimization",
abstract = "This paper introduces an approach to level-set topology optimization that can handle multiple constraints and simultaneously optimize non-level-set design variables. The key features of the new method are discretized boundary integrals to estimate function changes and the formulation of an optimization sub-problem to attain the velocity function. The sub-problem is solved using sequential linear programming (SLP) and the new method is called the SLP level-set method. The new approach is developed in the context of the Hamilton-Jacobi type level-set method, where shape derivatives are employed to optimize a structure represented by an implicit level-set function. This approach is sometimes referred to as the conventional level-set method. The SLP level-set method is demonstrated via a range of problems that include volume, compliance, eigenvalue and displacement constraints and simultaneous optimization of non-level-set design variables.",
author = "Dunning, {Peter D.} and H.Alicia Kim",
year = "2015",
month = "3",
doi = "10.1007/s00158-014-1174-z",
language = "English",
volume = "51",
pages = "631--643",
journal = "Structural and Multidisciplinary Optimization",
issn = "1615-147X",
publisher = "Springer Verlag",
number = "3",

}

TY - JOUR

T1 - Introducing the sequential linear programming level-set method for topology optimization

AU - Dunning,Peter D.

AU - Kim,H.Alicia

PY - 2015/3

Y1 - 2015/3

N2 - This paper introduces an approach to level-set topology optimization that can handle multiple constraints and simultaneously optimize non-level-set design variables. The key features of the new method are discretized boundary integrals to estimate function changes and the formulation of an optimization sub-problem to attain the velocity function. The sub-problem is solved using sequential linear programming (SLP) and the new method is called the SLP level-set method. The new approach is developed in the context of the Hamilton-Jacobi type level-set method, where shape derivatives are employed to optimize a structure represented by an implicit level-set function. This approach is sometimes referred to as the conventional level-set method. The SLP level-set method is demonstrated via a range of problems that include volume, compliance, eigenvalue and displacement constraints and simultaneous optimization of non-level-set design variables.

AB - This paper introduces an approach to level-set topology optimization that can handle multiple constraints and simultaneously optimize non-level-set design variables. The key features of the new method are discretized boundary integrals to estimate function changes and the formulation of an optimization sub-problem to attain the velocity function. The sub-problem is solved using sequential linear programming (SLP) and the new method is called the SLP level-set method. The new approach is developed in the context of the Hamilton-Jacobi type level-set method, where shape derivatives are employed to optimize a structure represented by an implicit level-set function. This approach is sometimes referred to as the conventional level-set method. The SLP level-set method is demonstrated via a range of problems that include volume, compliance, eigenvalue and displacement constraints and simultaneous optimization of non-level-set design variables.

UR - http://www.scopus.com/inward/record.url?scp=84907486610&partnerID=8YFLogxK

UR - http://dx.doi.org/10.1007/s00158-014-1174-z

U2 - 10.1007/s00158-014-1174-z

DO - 10.1007/s00158-014-1174-z

M3 - Article

VL - 51

SP - 631

EP - 643

JO - Structural and Multidisciplinary Optimization

T2 - Structural and Multidisciplinary Optimization

JF - Structural and Multidisciplinary Optimization

SN - 1615-147X

IS - 3

ER -