Quasi-isotropic metamaterials with negative thermal expansion designed via topology optimization using topological derivative

Designing structures with negative thermal expansion coefficients, which contract when heated, is a significant focus in structural optimization research. While isotropy, meaning uniform properties in all directions, is beneficial for such structures in terms of design flexibility and simulation simplicity, negative thermal expansion structures often exhibit anisotropic, direction-dependent behavior. This study presents a multimaterial topology optimization for structures with a negative quasi-isotropic thermal expansion coefficient. By utilizing a cell structure exhibiting hexagonal symmetry, the approach ensures “quasi-isotropy”, referring to the behavior of a composite material that closely approximates isotropy in certain physical properties. Additionally, the proposed method enhances structural performance through topology optimization employing topological derivatives. Numerical experiments validate the effectiveness of the proposed method, confirming its ability to produce structures with high structural performance and desired thermal expansion properties while maintaining isotropic characteristics. The presented approach offers a novel and promising tool for designing innovative materials and structures with tailored thermal expansion behavior. • Topology optimization method for metamaterials of quasi-isotropic negative thermal expansion. • Optimized through topological derivative-based topology optimization. • Quasi-isotropy is achieved using hexagonal and triangular unit cell.

Authors

Institutions

Publication Details

Journal
Composite Structures
Published
2025-03-29
DOI
https://doi.org/10.1016/j.compstruct.2025.119111
Citations
4
Primary Topic
Topology Optimization in Engineering
Type
article
Field-Weighted Citation Impact
3.81

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quasi-isotropic metamaterials with negative thermal expansion designed via topology optimization using topological derivative

Takayuki Yamada, Masaki Noda
4 citations
Composite Structures
Topology Optimization in Engineering
3.81
article

Quasi-isotropic metamaterials with negative thermal expansion designed via topology optimization using topological derivative

Takayuki Yamada, Masaki Noda
article en
4 citations

Abstract

Designing structures with negative thermal expansion coefficients, which contract when heated, is a significant focus in structural optimization research. While isotropy, meaning uniform properties in all directions, is beneficial for such structures in terms of design flexibility and simulation simplicity, negative thermal expansion structures often exhibit anisotropic, direction-dependent behavior. This study presents a multimaterial topology optimization for structures with a negative quasi-isotropic thermal expansion coefficient. By utilizing a cell structure exhibiting hexagonal symmetry, the approach ensures “quasi-isotropy”, referring to the behavior of a composite material that closely approximates isotropy in certain physical properties. Additionally, the proposed method enhances structural performance through topology optimization employing topological derivatives. Numerical experiments validate the effectiveness of the proposed method, confirming its ability to produce structures with high structural performance and desired thermal expansion properties while maintaining isotropic characteristics. The presented approach offers a novel and promising tool for designing innovative materials and structures with tailored thermal expansion behavior. • Topology optimization method for metamaterials of quasi-isotropic negative thermal expansion. • Optimized through topological derivative-based topology optimization. • Quasi-isotropy is achieved using hexagonal and triangular unit cell.

Composite StructuresVol. 365
The University of Tokyo (JP)
Japan Society for the Promotion of Science
Openalex Percentile: Top 8%
Topology Optimization in Engineering
3.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.