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

By Peter D. Dunning, H. A. Kim. 2015

133 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/950f6d2f0b07e4734e5d52d0f0f8443673f53b69

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Stress-based shape and topology optimization with the level set method (2018, background): “The function for velocities showed to produce smooth boundaries and the possibility to address multiple constraints in [39,40].”
Topology optimization for functionally graded cellular composites with metamaterials by level sets (2018, methodology): “LSM [40, 41] have been used as an alternative method for structural shape and topology optimization design problems [42, 43, 52-58].”
Geometric constraints for shape and topology optimization in architectural design (2017, methodology): “The (constrained or unconstrained) optimization process is driven by an SLP-type algorithm similar to that presented in [33], implemented in Scilab [18].”
Level‐set topology optimization with many linear buckling constraints using an efficient and robust eigensolver (2016, background): “Full details on this approach for handling constraints in level-set-based optimization are given in [41].”