Pusan National University develops novel approach for composite material homogenization
The proposed RBHM method has been shown to accelerate composite material modeling and analysis with reduced basis homogenization, achieving higher accuracy and minimal error.
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RBHM is rapid, yet accurate for calculating homogenized composite material properties. Source | Pusan National University
Estimating composite material properties can be computationally expensive and time-consuming. Researchers from (Busan, South Korea) have proposed a reduced basis homogenization method (RBHM) to enhance homogenization based on a finite element method (FEM). According to the research, in November 2024, RBHM significantly improves computational efficiency while maintaining high accuracy.
When predicting the performance of a composite material in real-world conditions, engineers usually rely on experimental testing or numerical analysis to predict homogenized properties like thermal conductivity and elasticity, but these approaches can be time-consuming or computationally expensive.
The numerical homogenization process approximates composite material properties at a macroscopic scale by solving partial differential equations (PDEs) at a microscopic scale. The team, led by Pusan’s Professor Kyunghoon Lee, proposed RBHM to speed up numerical homogenization. RBHM decreases the computational cost by performing numerical homogenization on a reduced basis space and allows one to easily change fiber and matrix materials to obtain desired composite material properties.
“Through the use of the RBHM, we can rapidly yet accurately generate the solutions of microscale PDEs. We then use these solutions to quickly evaluate the homogenized properties of a periodic composite material as we try various combinations of fiber and matrix materials,” explains Lee. RBHM expedites numerical homogenization, particularly for parametric analyses requiring multiple simulations, while providing prompt evaluations of macroscopic properties with minimal error.
The RBHM method has also achieved computational speeds up to 1,030 times faster than the FEM for evaluating thermal properties and 670 times faster for elastic properties, while maintaining accuracy comparable to that of the FEM. RBHM predictions matched not only the FEM predictions but also the experimental data, providing confidence in the method’s reliability. For instance, RBHM produced homogenized thermal conductivity and Young’s modulus with errors of less than 5% and less than 3%, respectively, compared with experimental results.
“Our method also allows for easy adjustments to fiber and matrix properties, enabling engineers to swiftly explore and test new fiber and matrix combinations,” adds Lee. This feature is crucial for industries where the virtual testing and design of a composite material is needed.
Research shows that RBHM can reduce overall computation time by up to 70%, which not only improves efficiency but also ensures scalability for large-scale industrial applications. Looking to the future, the university researchers aim to expand RBHM to handle even more complex materials and use cases, including nonlinear elastic behavior and thermoelastic coupling, further broadening its applications in material science.