arXiv subjects

Kentaro Yaji

Publications and source records attributed to Kentaro Yaji.

27 records · Page 2Linked to original sources

arXiv2024

Adjoint lattice kinetic scheme for topology optimization in fluid problems

This paper proposes a topology optimization method for non-thermal and thermal fluid problems using the Lattice Kinetic Scheme (LKS).LKS, which is derived from the Lattice Boltzmann Method (LBM), requires only macroscopic values, such as fluid velocity and pressure, whereas LBM requires velocity distribution functions, thereby reducing memory requirements. The proposed method computes design sensitivities based on the adjoint variable method, and the adjoint equation is solved in the same manner as LKS; thus, we refer to it as the Adjoint Lattice Kinetic Scheme (ALKS). A key contribution of this method is the proposed approximate treatment of boundary conditions for the adjoint equation, which is challenging to apply directly due to the characteristics of LKS boundary conditions. We demonstrate numerical examples for steady and unsteady problems involving non-thermal and thermal fluids, and the results are physically meaningful and consistent with previous research, exhibiting similar trends in parameter dependencies, such as the Reynolds number. Furthermore, the proposed method reduces memory usage by up to 75% compared to the conventional LBM in an unsteady thermal fluid problem.

math.NA↗

arXiv2024

Deep Concept Identification for Generative Design

A generative design based on topology optimization provides diverse alternatives as entities in a computational model with a high design degree. However, as the diversity of the generated alternatives increases, the cognitive burden on designers to select the most appropriate alternatives also increases. Whereas the concept identification approach, which finds various categories of entities, is an effective means to structure alternatives, evaluation of their similarities is challenging due to shape diversity. To address this challenge, this study proposes a concept identification framework for generative design using deep learning (DL) techniques. One of the key abilities of DL is the automatic learning of different representations of a specific task. Deep concept identification finds various categories that provide insights into the mapping relationships between geometric properties and structural performance through representation learning using DL. The proposed framework generates diverse alternatives using a generative design technique, clusters the alternatives into several categories using a DL technique, and arranges these categories for design practice using a classification model. This study demonstrates its fundamental capabilities by implementing variational deep embedding, a generative and clustering model based on the DL paradigm, and logistic regression as a classification model. A simplified design problem of a two-dimensional bridge structure is applied as a case study to validate the proposed framework. Although designers are required to determine the viewing aspect level by setting the number of concepts, this implementation presents the identified concepts and their relationships in the form of a decision tree based on a specified level.

cs.LG↗

arXiv2024

Data-driven topology design with persistent homology for enhancing population diversity

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 design variable space and strong nonlinearity of evaluation functions. Motivated by the idea that topology is what characterizes the inherent diversity among material distributions, we employ a topological data analysis method called persistent homology. As a specific operation, a Wasserstein distance sorting between persistence diagrams is introduced into a selection algorithm to maintain the intrinsic population diversity. We apply the proposed selection operation incorporated into DDTD to a stress-based topology optimization problem as a numerical example. The results confirm that topology can be analyzed using persistent homology and that the proposed selection operation significantly enhances the search performance of DDTD.

math.OC↗

arXiv2024

Data-driven multifidelity topology design with multi-channel variational auto-encoder for concurrent optimization of multiple design variable fields

The objective of this study is to establish a gradient-free topology optimization framework that facilitates more global solution searches to avoid entrapping in undesirable local optima, especially in problems with strong non-linearity. The framework utilizes a data-driven multifidelity topology design, where solution candidates resulting from low-fidelity optimization problems are iteratively updated by a variational auto-encoder (VAE) and high-fidelity (HF) evaluation. A key step in the solution update involves constructing HF models by extruding VAE-generated material distributions to a constant thickness (the HF modeling parameter) across all candidates, which limits exploration of the parameter space and requires extensive parametric studies outside the optimization loop. To achieve comprehensive optimization in a single run, we propose a multi-channel image data architecture that stores material distributions and HF modeling parameters in separate channels, allowing simultaneous optimization of the HF parameter space. We demonstrated the efficacy of the proposed framework by solving a maximum stress minimization problem, characterized by strong non-linearity due to its minimax formulation.

math.OC↗

arXiv2024

Data-driven topology design based on principal component analysis for 3D structural design problems

Topology optimization is a structural design methodology widely utilized to address engineering challenges. However, sensitivity-based topology optimization methods struggle to solve optimization problems characterized by strong non-linearity. Leveraging the sensitivity-free nature and high capacity of deep generative models, data-driven topology design (DDTD) methodology is considered an effective solution to this problem. Despite this, the training effectiveness of deep generative models diminishes when input size exceeds a threshold while maintaining high degrees of freedom is crucial for accurately characterizing complex structures. To resolve the conflict between the both, we propose DDTD based on principal component analysis (PCA). Its core idea is to replace the direct training of deep generative models with material distributions by using a principal component score matrix obtained from PCA computation and to obtain the generated material distributions with new features through the restoration process. We apply the proposed PCA-based DDTD to the problem of minimizing the maximum stress in 3D structural mechanics and demonstrate it can effectively address the current challenges faced by DDTD that fail to handle 3D structural design problems. Various experiments are conducted to demonstrate the effectiveness and practicability of the proposed PCA-based DDTD.

cs.LG↗

arXiv2021

Data-driven topology design using a deep generative model

In this paper, we propose a sensitivity-free and multi-objective structural design methodology called data-driven topology design. It is schemed to obtain high-performance material distributions from initially given material distributions in a given design domain. Its basic idea is to iterate the following processes: (i) selecting material distributions from a dataset of material distributions according to eliteness, (ii) generating new material distributions using a deep generative model trained with the selected elite material distributions, and (iii) merging the generated material distributions with the dataset. Because of the nature of a deep generative model, the generated material distributions are diverse and inherit features of the training data, that is, the elite material distributions. Therefore, it is expected that some of the generated material distributions are superior to the current elite material distributions, and by merging the generated material distributions with the dataset, the performances of the newly selected elite material distributions are improved. The performances are further improved by iterating the above processes. The usefulness of data-driven topology design is demonstrated through numerical examples.

physics.comp-ph↗

arXiv2020

Topology design of two-fluid heat exchange

Heat exchangers are devices that typically transfer heat between two fluids. The performance of a heat exchanger such as heat transfer rate and pressure loss strongly depends on the flow regime in the heat transfer system. In this paper, we present a density-based topology optimization method for a two-fluid heat exchange system, which achieves a maximum heat transfer rate under fixed pressure loss. We propose a representation model accounting for three states, i.e., two fluids and a solid wall between the two fluids, by using a single design variable field. The key aspect of the proposed model is that mixing of the two fluids can be essentially prevented without any penalty scheme. This is because the solid constantly exists between the two fluids due to the use of the single design variable field. We demonstrate the effectiveness of the proposed approach through three-dimensional numerical examples in which an optimized design is compared with a simple reference design, and the effects of design conditions (i.e., Reynolds number, Prandtl number, design domain size, and flow arrangements) are investigated.

cs.CE↗

arXiv2018

Novel flow field design for vanadium redox flow batteries via topology optimization

This paper presents a three-dimensional topology optimization method for the design of flow field in vanadium redox flow batteries (VRFBs). We focus on generating a novel flow field configuration for VRFBs via topology optimization, which has been attracted attention as a powerful design tool based on numerical optimization. An attractive feature of topology optimization is that a topology optimized configuration can be automatically generated without presetting a promising design candidate. In this paper, we formulate the topology optimization problem as a maximization problem of the electrode surface concentration in the negative electrode during the charging process. The aim of this optimization problem is to obtain a topology optimized flow field that enables the improvement of mass transfer effect in a VRFB. We demonstrate that a novel flow field configuration can be obtained through the numerical investigation. To clarify the performance of the topology optimized flow field, we investigate the mass transfer effect through the comparison with reference flow fields---parallel and interdigitated flow fields---and the topology optimized flow field. In addition, we discuss the power loss that takes account of the polarization loss and pumping power, at various operating conditions.

math.OC↗

arXiv2017

Semi-Analytical Model for Wind-Fed Black Hole High-Mass X-ray Binaries -- State Transition Triggered by Magnetic Fields from the Companion Star --

We propose a mechanism of state transition in wind-fed black hole binaries (high-mass X-ray binaries) such as Cyg X-1 and LMC X-1. Modeling a line-driven stellar wind from the companion by two-dimensional hydrodynamical calculations, we investigate the processes of wind capture by and accretion onto the black hole. We assume that the wind acceleration is terminated at the He II ionization front because ions responsible for line-driven acceleration are ionized within the front, i.e. He III region. It is found that the mass accretion rate inferred from the luminosity is remarkably smaller than the capture rate. Considering the difference, we construct a model for the state transition based on the accretion flow being controlled by magneto-rotational instability. The outer flow is torus like, and plays an important role to trigger the transition. The model can explain why state transition does occur in Cyg X-1, while not in LMC X-1. Cyg X-1 exhibits a relatively low luminosity, and then the He II ionization front is located and can move between the companion and black hole, depending on its ionizing photon flux. On the other hand, LMC X-1 exhibits too high luminosity for the front to move considerably; the front is too close to the companion atmosphere. The model also predicts that each state of high-soft or low-hard would last fairly long because the luminosity depends weakly on the wind velocity. In the context of the model, the state transition is triggered by a fluctuation of the magnetic field when its amplitude becomes comparable to the field strength in the torus-like outer flow.

astro-ph.HE↗