A detailed introduction to density-based topology optimisation of fluid flow problems with implementation in MATLAB

TL;DR

This paper provides a comprehensive introduction to density-based topology optimisation for fluid flow, including theory, MATLAB implementation, and practical examples, aimed at helping newcomers quickly learn and apply the methods.

Contribution

It offers a detailed, step-by-step guide with extended MATLAB code, analysis of the Brinkman penalty approach, and modifications for complex problems, facilitating rapid entry into the research area.

Findings

Validated the accuracy of the Brinkman penalty method through parametric simulations.

Demonstrated the effectiveness of the optimisation approach with benchmark examples.

Analyzed computational performance and provided recommendations for efficiency.

Abstract

This article presents a detailed introduction to density-based topology optimisation of fluid flow problems. The goal is to allow new students and researchers to quickly get started in the research area and to skip many of the initial steps, often consuming unnecessarily long time from the scientific advancement of the field. This is achieved by providing a step-by-step guide to the components necessary to understand and implement the theory, as well as extending the supplied MATLAB code. The continuous design representation used and how it is connected to the Brinkman penalty approach, for simulating an immersed solid in a fluid domain, is illustrated. The different interpretations of the Brinkman penalty term and how to chose the penalty parameters are explained. The accuracy of the Brinkman penalty approach is analysed through parametric simulations of a reference geometry. The chosen finite element formulation and the solution method is explained. The minimum dissipated energy optimisation problem is defined and how to solve it using an optimality criteria solver and a continuation scheme is discussed. The included MATLAB implementation is documented, with details on the mesh, pre-processing, optimisation and post-processing. The code has two benchmark examples implemented and the application of the code to these is reviewed. Subsequently, several modifications to the code for more complicated examples are presented through provided code modifications and explanations. Lastly, the computational performance of the code is examined through studies of the computational time and memory usage, along with recommendations to decrease computational time through approximations.