Optimal analysis for optimal design of cyclic symmetric structures subject to frequency constraints

• Published in: Structures (Oxford) Vol. 33; pp. 3122 - 3136
• Main Authors: Kaveh, Ali, Biabani Hamedani, Kiarash, Joudaki, Ali, Kamalinejad, Mohammad
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2021
• Subjects: Cyclic symmetry; Eigenvalue decomposition; Free vibration analysis; Frequency constraints; Jaya algorithm; Optimal structural analysis; Set theory; Structural optimization; Jaya algorithm; Free vibration analysis; Structural optimization; Optimal structural analysis; Eigenvalue decomposition; Frequency constraints; Set theory; Cyclic symmetry
• DOI: 10.1016/j.istruc.2021.06.054
• ISSN: 2352-0124

In general, the design optimization of large-scale structural systems is an extremely computationally expensive process. However, optimal structural analysis provides efficient and practical methods of structural analysis which can be employed as powerful tools to reduce significantly the computational time of structural analyses performed in the structural optimization process. In this paper, an efficient eigensolution method for free vibration analysis of rotationally repetitive structures is developed for the optimal design of cyclic symmetric structures subject to frequency constraints. Through the present eigensolution method, the initial free-vibration eigenproblem is decomposed into some sub-eigenproblems with much smaller dimensions using an efficient block-diagonalization technique. The main advantage of the present method is that it requires significantly less computational time and memory than the existing classical eigensolution method. Finally, two large-scale numerical examples are given to demonstrate the efficiency and accuracy of the present eigensolution method and compare it with the existing classical method in both terms of computational time and memory requirements. Numerical results indicate that the present eigensolution method not only guarantees the accuracy of the analysis results but also significantly reduces the computational time and memory required compared to the existing classical method. More importantly, the computational time of the optimization process is also significantly reduced. Optimization is carried out by a set-theoretical-based Jaya algorithm, named ST-JA. The proposed ST-JA aims to enhance the diversification and intensification capabilities of the original JA and strike a fine balance between them. Optimization results confirm that the proposed ST-JA outperforms the original JA and has superior or comparable performance to other state-of-the-art optimization algorithms.