Abstract
Numerical layout optimization employing an adaptive ‘member adding’ solution scheme provides a computationally efficient means of generating (near-)optimum trusses for problems involving single or multiple load cases. To encourage usage of the method, a Python script is presented, allowing medium to large-scale problems to be solved efficiently. As well as handling multiple load cases, the short (98 line) script presented can tackle truss optimization problems involving unequal limiting tensile and compressive stresses, joint costs, and non-convex polygonal domains, with or without holes. Various numerical examples are used to demonstrate the efficacy of the script presented.
Highlights
Truss layout optimization using the ‘ground structure’ approach provides a fully automated means of identifyingoptimum truss structures
In order to provide an accessible yet computationally efficient educational implementation of numerical layout optimization, this paper introduces a simple Python script
It was found in previous studies (e.g. Gilbert and Tyas 2003; Pritchard et al 2005) that layout optimization will often generate structures that are complex in form, containing large numbers of short members
Summary
Truss layout optimization using the ‘ground structure’ approach provides a fully automated means of identifying (near-)optimum truss structures. Most significantly, proposed in 2003, the ‘member adding’ scheme has not yet been incorporated in any of the aforementioned publicly available scripts, potentially leading researchers to underestimate the potential computational efficiency of the layout optimization method. As an indication of the dominance of SIMP over numerical layout optimization in terms of published educational source codes, only one of the 22 scripts listed by Wei et al (2018) employ the latter. In order to provide an accessible yet computationally efficient educational implementation of numerical layout optimization, this paper introduces a simple Python script. The paper is organized as follows: first, the mathematical layout optimization formulation is introduced; second, important code sections are explained; third, numerical examples are shown to demonstrate the efficacy of the script; conclusions are drawn
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