Topology Optimization of Flow Channels with Heat Transfer Using a Genetic Algorithm Assisted by the Kriging Model

Mitsuo Yoshimura, Takashi Misaka, Koji Shimoyama, Shigeru Obayashi

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)


A global optimization method for topology optimization using a genetic algorithm is proposed in this paper. The genetic algorithm used in this paper is assisted by the Kriging surrogate model to reduce computational cost required for function evaluation. To validate the global topology optimization method in flow problems, this research works on two single-objective optimization problems, where the objective functions are to minimize pressure loss and to maximize heat transfer of flow channels, and the multi-objective optimization problem, which combines these two problems. The shape of flow channels is represented by the level set function, and the pressure loss and the temperature of the channels are evaluated by the Building-Cube Method (BCM), which is a Cartesian-mesh CFD approach. The proposed method resulted in an agreement with previous study in the single-objective problems in its topology, and achieved global exploration of non-dominated solutions in the multi-objective problem.

Original languageEnglish
Title of host publicationComputational Methods in Applied Sciences
PublisherSpringer Netherland
Number of pages16
Publication statusPublished - 2019 Jan 1

Publication series

NameComputational Methods in Applied Sciences
ISSN (Print)1871-3033


  • Building-Cube method
  • Genetic algorithm
  • Kriging model
  • Topology optimization

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Modelling and Simulation
  • Biomedical Engineering
  • Computer Science Applications
  • Fluid Flow and Transfer Processes
  • Computational Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Topology Optimization of Flow Channels with Heat Transfer Using a Genetic Algorithm Assisted by the Kriging Model'. Together they form a unique fingerprint.

Cite this