TY - GEN
T1 - A framework of multi-fidelity topology design and its application to optimum design of flow fields in battery systems
AU - Yaji, Kentaro
AU - Yamasaki, Shintaro
AU - Tsushima, Shohji
AU - Fujita, Kikuo
N1 - Funding Information:
This work was partially supported by JSPS KAKENHI Grand Number 18K13674, and The Mazda Foundation.
Publisher Copyright:
Copyright © 2019 ASME.
PY - 2019
Y1 - 2019
N2 - 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 the basis of a pseudo-topology optimization problem. Meanwhile, the role of high-fidelity design optimization is to obtain a promising initial guess from a dataset comprising topology-optimized design candidates, and subsequently solve a surrogate optimization problem under a restricted design solution space. We apply the proposed framework to a topology optimization problem for the design of flow fields in battery systems, and confirm the efficacy through numerical investigations.
AB - 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 the basis of a pseudo-topology optimization problem. Meanwhile, the role of high-fidelity design optimization is to obtain a promising initial guess from a dataset comprising topology-optimized design candidates, and subsequently solve a surrogate optimization problem under a restricted design solution space. We apply the proposed framework to a topology optimization problem for the design of flow fields in battery systems, and confirm the efficacy through numerical investigations.
UR - http://www.scopus.com/inward/record.url?scp=85076456002&partnerID=8YFLogxK
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U2 - 10.1115/DETC2019-97675
DO - 10.1115/DETC2019-97675
M3 - Conference contribution
AN - SCOPUS:85076456002
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 45th Design Automation Conference
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019
Y2 - 18 August 2019 through 21 August 2019
ER -