Accessible Unlicensed Requires Authentication Published by De Gruyter November 30, 2021

Optimal design of aerospace structures using recent meta-heuristic algorithms

Faik Fatih Korkmaz, Mert Subran and Ali Rıza Yıldız
From the journal Materials Testing

Abstract

Most conventional optimization approaches are deterministic and based on the derivative information of a problem’s function. On the other hand, nature-inspired and evolution-based algorithms have a stochastic method for finding the optimal solution. They have become a more popular design and optimization tool, with a continually growing development of novel algorithms and new applications. Flexibility, easy implementation, and the capability to avoid local optima are significant advantages of these algorithms. In this study, shapes, and shape perturbation limits of a bracket part, which is used in aviation, have been set using the hypermorph tool. The objective function of the optimization problem is minimizing the volume, and the constraint is maximum von Mises stress on the structure. The grey wolf optimizer (GWO) and the moth-flame Optimizer (MFO) have been selected as nature-inspired evolution-based optimizers.


Faik Fatih Korkmaz Turkish Aerospace Industry Inc. Uludag University R&D Center

References

1 S. E. De Leon-Aldaco, H. Calleja, J. Aguayo Alquicira: Metaheuristic optimization methods applied to power converters: a review, IEEE Transactions on Power Electronics 30 (2015), No. 12, pp. 6791-6803 DOI:10.1109/TPEL.2015.239731110.1109/TPEL.2015.2397311Search in Google Scholar

2 A. R. Yildiz: Comparison of evolutionary-based optimization algorithms for structural design optimization, Engineering Applications of Artificial Intelligence 26 (2013), No. 1, pp. 327-333 DOI:10.1016/j.engappai.2012.05.01410.1016/j.engappai.2012.05.014Search in Google Scholar

3 B. S. Yildiz, A. R. Yildiz: Comparison of grey wolf, whale, water cycle, ant lion and sine-cosine algorithms for the optimization of a vehicle engine connecting rod, Materials Testing 60 (2018), No. 3, pp. 311-315 DOI:10.3139/120.11115310.3139/120.111153Search in Google Scholar

4 M. Zhou, N. Pagaldipti, H. L. Thomas, Y. K. Shyy: An integrated approach to topology, sizing, and shape optimization, Structural and Multidisciplinary Optimization 26 (2004), No. 5, pp. 308-317 DOI:10.1007/s00158-003-0351-210.1007/s00158-003-0351-2Search in Google Scholar

5 M. Mashayekhi, E. Salajegheh, M. Dehghani: Topology optimization of double and triple layer grid structures using a modified gravitational harmony search algorithm with efficient member grouping strategy, Computers & Structures 172 (2016), pp. 40-58 10.1016/j.compstruc.2016.05.008Search in Google Scholar

6 S. Gholizadeh, H. Barati: Topology optimization of nonlinear single layer domes by a new metaheuristic, Steel and Composite Structures 16 (2014), No. 6, pp. 681-701 DOI:10.12989/scs.2014.16.6.68110.12989/scs.2014.16.6.681Search in Google Scholar

7 G. G. Tejani, V. J. Savsani, V. K. Patel, P. V Savsani: Size, shape, and topology optimization of planar and space trusses using mutation-based improved metaheuristics, Journal of Computational Design and Engineering 5 (2018), No. 2, pp. 198-214 DOI:10.1016/j.jcde.2017.10.00110.1016/j.jcde.2017.10.001Search in Google Scholar

8 S. Gholizadeh, M. Ebadijalal: Performance based discrete topology optimization of steel braced frames by a new metaheuristic, Advances in Engineering Software 123 (2018), pp. 77-92 DOI:10.1016/j.advengsoft.2018.06.00210.1016/j.advengsoft.2018.06.002Search in Google Scholar

9 F. Erdal, E. Dogan, M. P. Saka: Optimum design of cellular beams using harmony search and particle swarm optimizers, Journal of Constructional Steel Research 67 (2011), pp. 237-247 DOI:10.1016/j.jcsr.2010.07.01410.1016/j.jcsr.2010.07.014Search in Google Scholar

10 A. R. Yildiz, M. U. Erdaş: A new Hybrid Taguchisalp swarm optimization algorithm for the robust design of real-world engineering problems, Materials Testing 63 (2021), pp. 157-162 DOI:10.1515/mt-2020-002210.1515/mt-2020-0022Search in Google Scholar

11 A. Kaveh: Applications of Metaheuristic Optimization Algorithms in Civil Engineering, Springer International Publishing, Basel, Switzerland (2017)Search in Google Scholar

12 L. F. F. Miguel, L. F. F. Miguel: Shape and size optimization of truss structures considering dynamic constraints through modern metaheuristic algorithms, Expert Systems with Applications 39 (2012), No. 10, pp. 9458-9467 DOI:10.1016/j.eswa.2012.02.11310.1016/j.eswa.2012.02.113Search in Google Scholar

13 T. Dede, Y. Ayvaz: Combined size and shape optimization of structures with a new meta-heuristic algorithm, Applied Soft Computing 28 (2015), pp. 250-258 DOI:10.1016/j.asoc.2014.12.00710.1016/j.asoc.2014.12.007Search in Google Scholar

14 Z. Meng, G. Li, X. Wang, S. M. Sait, A. R. Yildiz: A comparative study of metaheuristic algorithms for reliability-based design optimization problems, Archives of Computational Methods in Engineering,28 (2021), pp. 1853-1869 DOI:10.1007/s11831-020-09443-z10.1007/s11831-020-09443-zSearch in Google Scholar

15 H. Abderazek, A. R. Yildiz, S. M. Sait: Optimal design of planetary gear train for automotive transmissions using advanced meta-heuristics, International Journal of Vehicle Design 80 (2019), No. 2-4, pp. 121-136 DOI:10.1504/IJVD.2019.10986210.1504/IJVD.2019.109862Search in Google Scholar

16 A. R. Yildiz, F. Ozturk, Hybrid taguchi-harmony search approach for shape optimization, Recent Advances in Harmony Search Algorithm, Springer, Berlin, (2010), pp. 89-98Search in Google Scholar

17 B.S Yildiz, N. Pholdee, S. Bureerat, A. R. Yildiz, S. M. Sait: Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm, Expert Systems, 38(3)2021,e12666 DOI:10.1111/exsy.1266610.1111/exsy.12666Search in Google Scholar

18 H. Abderazek, F. Hamza, A. R. Yildiz, S. M. Sait: Comparative investigation of the moth-flame algorithm and whale optimization algorithm for optimal spur gear design, Materials Testing, 63 (2021) 3 DOI:10.1515/mt-2020-003910.1515/mt-2020-0039Search in Google Scholar

19 B.S Yildiz, N. Pholdee, S. Bureerat, A. R. Yildiz, S. M. Sait: Comparision of the political optimization algorithm, the Archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry, Materials Testing, 63 (2021) 4, pp. 356-359 DOI:10.1515/mt-2020-00510.1515/mt-2020-005Search in Google Scholar

20 N. Panagan, N. Pholdee; K. Wansasueb, S. Bureerat, A. R. Yildiz; S. M. Sait: Comparison of recent algorithms for many-objective optimisation of an automotive floor-frame, International Journal of Vehicle Design, 80 (2019), No. 2-4, pp. 176-208 DOI:10.1504/IJVD.2019.10986310.1504/IJVD.2019.109863Search in Google Scholar

21 B. S. Yildiz: Robust design of electric vehicle components using a new hybrid salp swarm algorithm and radial basis function-based approach, International Journal of Vehicle Design 83 (2020), No. 1, pp. 38-53 DOI:10.1504/IJVD.2020.11477910.1504/IJVD.2020.114779Search in Google Scholar

22 B. S. Yildiz: Slime mould algorithm and kriging surrogate model-based approach for enhanced crashworthiness of electric vehicles, International Journal of Vehicle Design 83 (2020), No. 1, pp. 54-68 DOI:10.1504/IJVD.2020.11478610.1504/IJVD.2020.114786Search in Google Scholar

23 B.S Yildiz, V. Patel, N. Pholdee, S. M. Sait, S. Bureerat, A. R. Yildiz,: Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design, Materials Testing, 63 (2021) 4 DOI:10.1515/mt-2020-004910.1515/mt-2020-0049Search in Google Scholar

24 C. M. Aye, N. Pholdee, A. R. Yildiz, S. Bureerat, S. M. Sait: Multi-surrogate-assisted metaheuristics for crashworthiness optimisation, International Journal of Vehicle Design 80 (2019), No. 2-4, pp. 223-240 DOI:10.1504/IJVD.2019.10986610.1504/IJVD.2019.109866Search in Google Scholar

25 R. E. Perez, K. Behdinan: Particle swarm approach for structural design optimization, Computers & Structures 85 (2007), No. 19-20, pp. 1579-1588 10.1016/j.compstruc.2006.10.013Search in Google Scholar

26 C.-Y. Wu, K.-Y. Tseng: Topology optimization of structures using modified binary differential evolution, Structural and Multidisciplinary Optimization 42 (2010), No. 6, pp. 939-953 DOI:10.1007/s00158-010-0523-910.1007/s00158-010-0523-9Search in Google Scholar

27 S. Mirjalili, S. M. Mirjalili, A. Lewis: Grey wolf optimizer, Advances in Engineering Software 69 (2014), pp. 46-61 DOI:10.1016/j.advengsoft.2013.12.00710.1016/j.advengsoft.2013.12.007Search in Google Scholar

28 S. Mirjalili: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowledge-Based Systems 89 (2015), pp. 228-249 DOI:10.1016/j.knosys.2015.07.00610.1016/j.knosys.2015.07.006Search in Google Scholar

29 C. Zhou, H. Zhao, Q. Chang, M. Ji, C. Li: Reliability and global sensitivity analysis for an airplane slat mechanism considering wear degradation Chinese Journal of Aeronautics 34 (2021), No. 1, pp. 163-170 DOI:10.1016/j.cja.2020.09.04810.1016/j.cja.2020.09.048Search in Google Scholar

30 H. Pang, T. Yu, B. Song: Failure mechanism analysis and reliability assessment of an aircraft slat, Engineering Failure Analysis 60 (2016), pp. 261-279 DOI:10.1016/j.engfailanal.2015.11.03210.1016/j.engfailanal.2015.11.032Search in Google Scholar

31 K. Jones, S. Fox, S. Amorosi: Wing Leading Edge Slat System, U. S. Patent No. 20070102587A1, USA (2007)Search in Google Scholar

32 M. J. Abzug, E. E. Larrabee: Airplane Stability and Control – A History of the Technologies that Made Aviation Possible, Cambridge University Press, New York, USA (2005)Search in Google Scholar

33 A. Ceruti, P. Marzocca, A. Liverani, C. Bil: Maintenance in aeronautics in an Industry 4.0 context: the role of augmented reality and additive manufacturing, Journal of Computational Design and Engineering 6 (2019), No. 4, pp. 516-526 DOI:10.1016/j.jcde.2019.02.00110.1016/j.jcde.2019.02.001Search in Google Scholar

34 Altair Engineering HyperMorph10.0, https://blog.altair.co.kr/wp-content/uploads/2011/03/hypermorph.pdf,accessed February 11, 2021Search in Google Scholar

35 Q. He, L. Wang: An effective co-evolutionary particle swarm optimization for constrained engineering design problems, Engineering Applications of Artificial Intelligence 20 (2007), No. 1, pp. 89-99 DOI:10.1016/j.engappai.2006.03.00310.1016/j.engappai.2006.03.003Search in Google Scholar

Published Online: 2021-11-30

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