| Autor: |
Zeng, Qingrong, Liu, Xiaochen, Zhu, Xuefeng, Zhang, Xiangkui, Hu, Ping |
| Předmět: |
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| Zdroj: |
CMES-Computer Modeling in Engineering & Sciences; 2024, Vol. 141 Issue 3, p2065-2085, 21p |
| Abstrakt: |
Traditional topology optimization methods often suffer from the "dimension curse" problem, wherein the computation time increases exponentially with the degrees of freedom in the background grid. Overcoming this challenge, we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty (CGAN-GP). This innovative method allows for nearly instantaneous prediction of optimized structures. Given a specific boundary condition, the network can produce a unique optimized structure in a one-to-one manner. The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization (SIMP) method. Subsequently, we design a conditional generative adversarial network and train it to generate optimized structures. To further enhance the quality of the optimized structures produced by CGAN-GP, we incorporate Pix2pixGAN. This augmentation results in sharper topologies, yielding structures with enhanced clarity, de-blurring, and edge smoothing. Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms, all while maintaining an impressive accuracy rate of up to 85%, as demonstrated through numerical examples. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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