An Ising machine formulation for design updates in topology optimization of flow channels

Engineering With Computers, vol. 42

Abstract

Abstract Topology optimization is an essential tool in computational engineering, for example, to improve the design and efficiency of flow channels. At the same time, Ising machines, including digital or quantum annealers, have been used as efficient solvers for combinatorial optimization problems. Beyond combinatorial optimization, recent works have demonstrated applicability to other engineering tasks by tailoring corresponding problem formulations. In this study, we present a novel Ising machine formulation for computing design updates during topology optimization with the goal of minimizing dissipation energy in flow channels. We explore the potential of this approach to improve the efficiency and performance of the optimization process. To this end, we conduct experiments to study the impact of various factors within the novel formulation. Additionally, we compare it to a classical method from the literature using the number of optimization steps and the final values of the objective function as indicators of the time intensity of the optimization and the performance of the resulting designs, respectively. Our findings show that the proposed update strategy can accelerate the topology optimization process while producing comparable designs. However, it tends to be less exploratory, which may lead to lower performance of the designs. These results highlight the potential of incorporating Ising formulations for optimization tasks but also show their limitations when used to compute design updates in an iterative optimization process. In conclusion, this work provides an efficient alternative for design updates in topology optimization and enhances the understanding of integrating Ising machine formulations in engineering optimization.

Authors 8

1. Yudai Suzuki

   Keio University

   Affiliation as printed

   Quantum Computing Center, Keio University, Yokohama, Kanagawa, Japan

2. Shiori Aoki

   Keio University

   Affiliation as printed

   Graduate School of Science and Technology, Keio University, Yokohama, Kanagawa, Japan

3. Fabian Key corresponding

   TU Wien

   Affiliation as printed

   Institute of Lightweight Design and Structural Biomechanics (ILSB), TU Wien, Vienna, Austria

4. K. Endo

   National Institute of Advanced Industrial Science and Technology

   Affiliation as printed

   Research Center for Computational Design of Advanced Functional Materials, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan

5. Yoshiki Matsuda

   Waseda University

   Affiliation as printed

   Fixstars Corporation, Tokyo, Japan

   Green Computing System Research Organization, Waseda University, Tokyo, Japan

6. Shu Tanaka

   Waseda University · Keio University

   Affiliation as printed

   Department of Applied Physics and Physico-Informatics, Keio University, Yokohama, Kanagawa, Japan

   Green Computing System Research Organization, Waseda University, Tokyo, Japan

   Human Biology-Microbiome-Quantum Research Center (WPI-Bio2Q), Keio University, Tokyo, Japan

   Keio University Sustainable Quantum Artificial Intelligence Center (KSQAIC), Keio University, Tokyo, Japan

   Quantum Computing Center, Keio University, Yokohama, Kanagawa, Japan

7. Marek Behr Aachen Chair for Computational Analysis of Technical Systems (CATS)

   RWTH Aachen University

   Affiliation as printed

   Chair for Computational Analysis of Technical Systems (CATS), RWTH Aachen University, Aachen, Germany

8. Mayu Muramatsu

   Keio University

   Affiliation as printed

   Department of Mechanical Engineering, Keio University, Yokohama, Kanagawa, Japan

   Keio University Sustainable Quantum Artificial Intelligence Center (KSQAIC), Keio University, Tokyo, Japan

   Quantum Computing Center, Keio University, Yokohama, Kanagawa, Japan