Shape optimization of the muffler shield with regard to strength properties

• Autorzy: Jarosz Joachim , Długosz Adam

Identyfikatory

DOI

10.24423/EngTrans.3093.20230727

• Języki publikacji: EN

Abstrakty

EN

This paper is devoted to the shape optimization of the muffler shield with regard to strength properties. Three different optimization criteria are defined and numerically implemented concerning the strength properties of the shield, and different variants of optimization tasks are solved using both built-in optimization modules and in-house external algorithms. The effectiveness and efficiency of the optimization methods used are compared and presented.

Słowa kluczowe

EN

muffler shield; evolutionary algorithms; multi-objective optimization; finite element method; optimal design

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Engineering Transactions
• Rocznik: 2023
• Tom: Vol. 71, iss. 3
• Strony: 351--366
• Opis fizyczny: Bibliogr. 24 poz., rys., tab., wykr.

Twórcy

autor

Jarosz Joachim

• Tenneco Automotive Poland sp. z o.o. Rybnik, Poland

autor

Długosz Adam

• Department of Computational Mechanics and Engineering Silesian University of Technology Gliwice, Poland

Bibliografia

• 1. Aminuddin J., Wihantoro, Bilalodin, Sunardi, Rauf N., Designing of muffler part for car exhaust system with low emission and noise using conjugate gradient method, Journal of Physics: Conference Series, 1494(1): 012041, 2020, doi: 10.1088/1742-6596/ 1494/1/012041.
• 2. Barbieri S.G., Giacopini M., Mangeruga V., Mantovani S., A design strategy based on topology optimization techniques for an additive manufactured high performance engine piston, Procedia Manufacturing, 11: 641–649, 2017, doi: 10.1016/j.promfg. 2017.07.162.
• 3. Beluch W., Długosz A., Multiobjective global optimization of mechanical systems with cracks, Journal of Theoretical and Applied Mechanics, 58(2): 553–564, 2020, doi: 10.15632/jtam-pl/119092.
• 4. Burczyński T., Kuś W., Beluch W., Długosz A., Poteralski A., Szczepanik M., Intelligent Computing in Optimal Design, Solid Mechanics and Its Applications, Vol. 261, Springer International Publishing, 2020, doi: 10.1007/978-3-030-34161-9.
• 5. Collette Y., Siarry P., Multiobjective Optimization: Principles and Case Studies, Springer, 2003.
• 6. Deb K., Multi-objective genetic algorithms: problem difficulties and construction of test problems, Evolutionary Computation, 7(3): 205–230, 1999, doi: 10.1162/evco.1999.7.3.205.
• 7. Deb K., Pratap A., Agarwal S., Meyarivan T., A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6(2): 181– 197, 2002, doi: 10.1109/4235.996017. 8. Długosz A., Multiobjective evolutionary optimization of MEMS structures, Computer Assisted Methods in Engineering and Science, 17(1): 41–50, 2010.
• 9. Długosz A., Optimization in multiscale thermoelastic problems, Computer Methods in Materials Science, 14(1): 86–93, 2014.
• 10. Ebrahimi-Nejad S., Kheybari M., Borujerd S.V.N., Multi-objective optimization of a sports car suspension system using simplified quarter-car models, Mechanics & Industry, 21(4): 412, 2020, doi: 10.1051/meca/2020039.
• 11. Fonseca C.M., Fleming P.J., An overview of evolutionary algorithms in multiobjective optimization, Evolutionary Computation, 3(1): 1–16, 1995, doi: 10.1162/evco.1995.3.1.1.
• 12. Gardea A.M., Valenzuela J.C.M., Topological optimization of automotive structures under impact using robust design, Computer-Aided Design and Applications, 12(sup1): 33–47, 2015, doi: 10.1080/16864360.2015.1077073.
• 13. Ikpe A.E., Kelly O.E., Abdulsamad G., Engineering material selection for automotive exhaust systems using CES Software, International Journal of Engineering Technologies, 3(2): 50–60, 2017, doi: 10.19072/ijet.282847.
• 14. Jagdeesh H.K., Manjunatha K., Reddy M., Numerical and experimental investigation on thermal behavior of exhaust heat shield, International Journal of Innovative Science, Engineering & Technology, 2(11): 30–33, 2015.
• 15. Kleiber M., Handbook of Computational Solid Mechanics – Survey and Comparison of Contemporary Methods, Springer-Verlag, Berlin, 2011.
• 16. Knapczyk J., Maniowski M., Optimization of 5-rod car suspension for elastokinematic and dynamic characteristics, Archive of Mechanical Engineering, 57(2): 133–147, 2010, doi: 10.2478/v10180-010-0007-x.
• 17. Kong Y.S., Abdullah S., Omar M.Z., Haris S.M., Topological and topographical optimization of automotive spring lower seat, Latin American Journal of Solids and Structures, 13(7): 1388–1405, 2016, doi 10.1590/1679-78252082.
• 18. Liu X., Deng Y.D., Chen S., Wang W.S., Xu Y., Su C.Q., A case study on compatibility of automotive exhaust thermoelectric generation system, catalytic converter and muffler, Case Studies in Thermal Engineering, 2: 62–66, 2014, doi: 10.1016/j.csite.2014.01.002.
• 19. Lurie A.I., Theory of Elasticity, Springer-Verlag, Berlin, 2005.
• 20. Muhammad A., Ali M.A.H., Shanono I.H., Design optimization of a diesel connecting rod, Materials Today Proceedings, 22(Part 2): 1600–1609, 2020, doi: 10.1016/j.matpr. 2020.02.122.
• 21. Sebastjan P., Kuś W., Hybrid shape optimization of automotive spring seat, International Journal of Automotive Technology, 23(4): 957–965, 2022, doi: 10.1007/s12239-022- 0083-1.
• 22. While L., Hingston P., Barone L., Huband S., A faster algorithm for calculating hypervolume, IEEE Transactions on Evolutionary Computation, 10(1): 29–38, 2006, doi: 10.1109/TEVC.2005.851275.
• 23. Zienkiewicz O.C., Taylor R.L., The Finite Element Method, vol. 1–3, Butterworth, Oxford, 2000.
• 24. Zitzler E., Thiele L., Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation, 3(4): 257–271, 1999, doi: 10.1109/4235.797969.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).