Data-Driven Morphological Exploration and Shape Optimization for Turbulent Pipe Systems

Urata, Kazuya; Tsumoto, Ryo; Yaji, Kentaro et al.

Journal of Mechanical Design, 2025, 147(9), 091704

Number of Access:8（2025-11-20 04:36 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/102927

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Title
Data-Driven Morphological Exploration and Shape Optimization for Turbulent Pipe Systems
Creator
CreatorName
Urata, Kazuya
CreatorName
Tsumoto, Ryo
NRID
1000090779373 1000090779373
ORCID
0000-0002-4309-2043 0000-0002-4309-2043
researchmap permalink
yajiken0609 yajiken0609
Scopus Author ID
55787947500 55787947500
CreatorName
Yaji, Kentaro
NRID
1000010228992 1000010228992
ORCID
0000-0002-2197-1012 0000-0002-2197-1012
researchmap permalink
read0014111 read0014111
Scopus Author ID
7404057902 7404057902
CreatorName
Fujita, Kikuo
Subject
data-driven design
data-driven topology design
deep clustering
design optimization
generative design
multi-objective optimization
shape optimization
structural optimization
topology optimization
turbulence problem
Description
Urata K., Tsumoto R., Yaji K., et al. Data-Driven Morphological Exploration and Shape Optimization for Turbulent Pipe Systems. Journal of Mechanical Design 147, 091704 (2025); https://doi.org/10.1115/1.4068984.
Abstract
Optimal arrangements of turbulent pipe systems strongly depend on branch patterns, and turbulence fields typically cause involved multimodality in the solution space. These features hinder gradient-based structural optimization frameworks from finding promising solutions for turbulent pipe systems. In this article, we propose a multi-stage framework that integrates data-driven morphological exploration and evolutionary shape optimization to address the challenges posed by the complexity of turbulent pipe systems. Our framework begins with data-driven morphological exploration, aiming to find promising morphologies. It results in the shapes for selecting a reasonable number of candidates for the next shape refinement stage. Herein, we employ data-driven topology design, a gradient-free, and multi-objective optimization methodology incorporating a deep generative model and the concept of evolutionary algorithms to generate promising arrangements. Subsequently, a deep clustering strategy extracts representative shapes. The final stage involves refining these shapes through shape optimization using a genetic algorithm. Applying the framework to a two-dimensional turbulent pipe system with a minimax objective shows its effectiveness in delivering high-performance solutions for the turbulent flow optimization problem with branching.
Publisher
American Society of Mechanical Engineers (ASME)
Source Title
Journal of Mechanical Design
Volume (Issue)
147(9)
Page
091704
Date of Issued
2025-07-21
Language
English
Handle URL
https://hdl.handle.net/11094/102927
PISSN
10500472
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1115/1.4068984
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article