Kentaro Yaji

Topology optimization is a powerful methodology for generating novel designs with a high degree of design freedom. In exchange for this attractive feature, topology optimization cannot generally avoid multimodality, which often impedes finding a satisfactory solution when dealing with strongly nonlinear optimization problems. In this study, we focus on constructing a framework that aims to indirectly solve such complex topology optimization problems. The framework is based on multifidelity topol

This paper proposes a selection strategy for enhancing population diversity in data-driven topology design (DDTD), a topology optimization framework based on evolutionary algorithms (EAs) using a deep generative model. While population diversity is essential for global search with EAs, conventional selection operators that preserve diverse solutions based on objective values may still lead to a loss of population diversity in topology optimization problems due to the high dimensionality of desig

Particle flow processing is widely employed across various industrial applications and technologies. Due to the complex interactions between particles and fluids, designing effective devices for particle flow processing is challenging. In this study, we propose a topology optimization method to design flow fields that effectively enhance the resistance encountered by particles. Particle flow is simulated using an Eulerian–Eulerian model based on a finite difference method. Automatic differentiat

Abstract We propose a novel framework based on multi-fidelity design optimization for indirectly solving computationally hard topology optimization problems. The primary concept of the proposed framework is to divide an original topology optimization problem into two subproblems, i.e., low- and high-fidelity design optimization problems. Hence, artificial design parameters, referred to as seeding parameters, are incorporated into the low-fidelity design optimization problem that is formulated on