An Ising Machine Formulation for Design Updates in Topology Optimization of Flow Channels

TL;DR

This paper introduces a novel Ising machine formulation for topology optimization of flow channels, aiming to improve efficiency in design updates while analyzing its impact on optimization speed and design quality.

Contribution

It presents a new Ising machine-based approach for computing design updates in topology optimization, demonstrating its potential to accelerate the process and discussing its limitations.

Findings

Accelerates topology optimization process

Produces comparable designs to classical methods

Less exploratory, potentially lower performance

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 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.