GridapTopOpt.jl: A scalable Julia toolbox for level set-based topology optimisation

TL;DR

GridapTopOpt is a Julia-based, scalable, and user-friendly toolbox for level set topology optimisation, supporting distributed computing and automatic differentiation for complex PDE problems.

Contribution

It introduces a flexible, scalable Julia framework that simplifies implementation of diverse topology optimisation problems with near-mathematical notation.

Findings

Capable of solving large-scale benchmark problems

Supports parallel computation on HPC clusters

Includes automatic differentiation for sensitivities

Abstract

In this paper we present GridapTopOpt, an extendable framework for level set-based topology optimisation that can be readily distributed across a personal computer or high-performance computing cluster. The package is written in Julia and uses the Gridap package ecosystem for parallel finite element assembly from arbitrary weak formulations of partial differential equation (PDEs) along with the scalable solvers from the Portable and Extendable Toolkit for Scientific Computing (PETSc). The resulting user interface is intuitive and easy-to-use, allowing for the implementation of a wide range of topology optimisation problems with a syntax that is near one-to-one with the mathematical notation. Furthermore, we implement automatic differentiation to help mitigate the bottleneck associated with the analytic derivation of sensitivities for complex problems. GridapTopOpt is capable of solving a range of benchmark and research topology optimisation problems with large numbers of degrees of freedom. This educational article demonstrates the usability and versatility of the package by describing the formulation and step-by-step implementation of several distinct topology optimisation problems. The driver scripts for these problems are provided and the package source code is available at https://github$.$com/zjwegert/GridapTopOpt.jl.