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- Title
Microstructural design of rigid porous materials using a Bayesian optimization method.
- Authors
Yang, Sung Soo; Jung, Won Gu; Kang, Yeon June
- Abstract
This study presents a methodology for designing the periodic unit cell (PUC) to optimize the sound absorption properties of rigid porous materials using Bayesian optimization (BO). BO is a machine learning algorithm proficient in identifying the global optimum of a 'black-box' objective function with a limited set of observations. This BO methodology was applied in the design of two distinct types of PUCs: a body-centered cubic structure and a Kelvin cell structure. To verify the efficiency and robustness of the proposed approach, the optimization process was executed multiple times, each with varying initial random samples. In the case of the body-centered cubic design, the optimal PUC was ascertained by examining a mere 2.08 % of the total candidate designs; for the Kelvin cell design, this value was 4.33 %. The effectiveness of the BO-driven approach was validated by comparing it with random sampling method.
- Subjects
POROUS materials; BODY centered cubic structure; ABSORPTION of sound; UNIT cell; SET functions; MACHINE learning
- Publication
Journal of Mechanical Science & Technology, 2024, Vol 38, Issue 5, p2265
- ISSN
1738-494X
- Publication type
Article
- DOI
10.1007/s12206-024-0408-2