Optimizing Truss Dynamics: A Multi-Objective Approach to Modify Natural Frequencies and Mode Shapes with Geometric Constraints

Yıl 2024, Cilt: 12 Sayı: 1, 354 - 365, 25.03.2024

Can Ulaş Doğruer Can Barış Toprak Bora Yıldırım

https://doi.org/10.29109/gujsc.1296969

Öz

This article presents a comprehensive optimization approach to dynamically enhance a truss structure. The optimization problem addresses the systematic modification of the truss dynamics, focusing on achieving a specific set of natural frequencies without compromising the geometrical integrity. The truss structure is redesigned through the exploration of diverse cost functions, considering both minimization and maximization strategies for targeted subsets of natural frequencies and mode shape elements but also preserving essential geometric properties including dimensional intervals, symmetry conditions, and adherence to topological constraints. A dual-objective optimization paradigm is adopted; concurrently pursuing the minimization and maximization objectives together with various constraints are introduced to enforce geometric limits on each truss member, providing a holistic solution for effectively tailoring the dynamic characteristics of the truss structure. This study represents a nuanced understanding of dynamic optimization in truss design. The article's main contribution is improving balance between optimizing the dynamic requirements of the truss structure and considering the essential geometry constraints that ensures its practical utility. By doing so, the research not only advances the understanding of truss dynamics but also provides a framework for approaching similar optimization challenges in mechanical engineering.

Anahtar Kelimeler

Inverse modal analysis, Modal analysis, Optimization, Truss structure

Kaynakça

• [1] Zargham, S., Ward, T. A., Ramli, R., and Badruddin, I. A.: Topology optimization: a review for structural designs under vibration problems, Structural and Multidisciplinary Optimization, 53, 1157–1177, 2016. (Article)
• [2] Pholdee, N. and Bureerat, S.: Comparative performance of meta-heuristic algorithms for mass minimisation of trusses with dynamic constraints, Advances in Engineering Software, 75, 1–13, 2014. (Article)
• [3] Serra, M. and Venini, P.: On some applications of ant colony optimization metaheuristic to plane truss optimization, Structural and Multidisciplinary Optimization, 32, 499–506, 2006. (Article)
• [4] Ho-Huu, V., Nguyen-Thoi, T., Nguyen-Thoi, M., and Le-Anh, L.: An improved constrained differential evolution using discrete variables (D-ICDE) for layout optimization of truss structures, Expert Systems with Applications, 42, 7057–7069, 2015. (Article)
• [5] Ho-Huu, V., Nguyen-Thoi, T., Le-Anh, L., and Nguyen-Trang, T.: An effective reliability-based improved constrained differential evolution for reliability-based design optimization of truss structures, Advances in Engineering Software, 92, 48–56, 2016. (Article)
• [6] Miguel, L. F. F., Lopez, R. H., and Miguel, L. F. F.: Multimodal size, shape, and topology optimisation of truss structures using the Firefly algorithm, Advances in Engineering Software, 56, 23–37, 2013. (Article)
• [7] Xu, T., Zuo,W., Xu, T., Song, G., and Li, R.: An adaptive reanalysis method for genetic algorithm with application to fast truss optimization, Acta Mechanica Sinica, 26, 225–234, 2010. (Article)
• [8] Kaveh, A. and Talatahari, S.: A particle swarm ant colony optimization for truss structures with discrete variables, Journal of Constructional Steel Research, 65, 1558–1568, 2009. (Article)
• [9] Lamberti, L.: An efficient simulated annealing algorithm for design optimization of truss structures, Computers & Structures, 86, 1936–1953, 2008. (Article)
• [10] Jalili, S. and Talatahari, S.: Optimum design of truss structures under frequency constraints using hybrid CSS-MBLS algorithm, KSCE Journal of Civil Engineering, 22, 1840–1853, 2018. (Article)
• [11] Ho-Huu, V., Nguyen-Thoi, T., Truong-Khac, T., Le-Anh, L., and Vo-Duy, T.: An improved differential evolution based on roulette wheel selection for shape and size optimization of truss structures with frequency constraints, Neural computing and applications, 29, 167–185, 2018. (Article)
• [12] Jalili, S. and Hosseinzadeh, Y.: Combining migration and differential evolution strategies for optimum design of truss structures with dynamic constraints, Iranian Journal of Science and Technology, Transactions of Civil Engineering, 43, 289–312, 2019. (Article)
• [13] Lieu, Q. X., Do, D. T., and Lee, J.: An adaptive hybrid evolutionary firefly algorithm for shape and size optimization of truss structures with frequency constraints, Computers & Structures, 195, 99–112, 2018. (Article)
• [14] Miguel, L. F. F. and Miguel, L. F. F.: Shape and size optimization of truss structures considering dynamic constraints through modern metaheuristic algorithms, Expert Systems with Applications, 39, 9458–9467, 2012. (Article)
• [15] Assimi, H. 185 and Jamali, A.: A hybrid algorithm coupling genetic programming and Nelder–Mead for topology and size optimization of trusses with static and dynamic constraints, Expert Systems with Applications, 95, 127–141, 2018. (Article)
• [16] Zuo,W., Xu, T., Zhang, H., and Xu, T.: Fast structural optimization with frequency constraints by genetic algorithm using adaptive eigenvalue reanalysis methods, Structural and Multidisciplinary Optimization, 43, 799–810, 2011. (Article)
• [17] Gholizadeh, S., Salajegheh, E., and Torkzadeh, P.: Structural optimization with frequency constraints by genetic algorithm using wavelet radial basis function neural network, Journal of Sound and Vibration, 312, 316–331, 2008. (Article)
• [18] Kaveh, A. and Mahdavi, V.: A hybrid CBO–PSO algorithm for optimal design of truss structures with dynamic constraints, Applied Soft Computing, 34, 260–273, 2015. (Article)
• [19] Kaveh, A. and Zolghadr, A.: A new PSRO algorithm for frequency constraint truss shape and size optimization, Struct Eng Mech, 52, 445–468, 2014. (Article)
• [20] Tejani, G. G., Savsani, V. J., and Patel, V. K.: Modified sub-population teaching-learning-based optimization for design of truss structures with natural frequency constraints, Mechanics Based Design of Structures and Machines, 44, 495–513, 2016. (Article)
• [21] Tejani, G. G., Savsani, V. J., Bureerat, S., Patel, V. K., and Savsani, P.: Topology optimization of truss subjected to static and dynamic constraints by integrating simulated annealing into passing vehicle search algorithms, Engineering with Computers, 35, 499–517, 2019. (Article)
• [22] Salt, S. J., et al. "Layout optimization of pin-jointed truss structures with minimum frequency constraints." Engineering Optimization 55.8 (2023): 1403-1421.
• [23] Sheng-Xue, He. "Truss optimization with frequency constraints using the medalist learning algorithm." Structures. Vol. 55. Elsevier, 2023.
• [24] Millan-Paramo, Carlos, and João Elias Abdalla Filho. "Size and shape optimization of truss structures with natural frequency constraints using modified simulated annealing algorithm." Arabian Journal for Science and Engineering 45.5 (2020): 3511-3525.
• [25] Lemonge, Afonso CC, et al. "Multi-objective truss structural optimization considering natural frequencies of vibration and global stability." Expert Systems with Applications 165 (2021): 113777.
• [26] Carvalho, Érica CR, et al. "Solving multi-objective truss structural optimization problems considering natural frequencies of vibration and automatic member grouping." Evolutionary Intelligence (2022): 1-26.
• [27] Khodadadi, Nima, and Seyedali Mirjalili. "Truss optimization with natural frequency constraints using generalized normal distribution optimization." Applied Intelligence 52.9 (2022): 10384-10397.
• [28] Fu, Yun-Fei, et al. "Smooth Topological Design of 3D Continuum Structures Using Elemental Volume Fractions." Computers & Structures, 2020.
• [29] Zhu, Jihong, and Tong Gao. Topology optimization in engineering structure design. Elsevier, 2016.
• [30] Li, Jianhongyu, Shenyan Chen, and Hai Huang. "Topology optimization of continuum structure with dynamic constraints using mode identification." Journal of Mechanical Science and Technology 29 (2015): 1407-1412.