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Optimization of gear teeth in the wind turbine drive train with gear contact’s uncertainty using the reliability-based design optimization

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Although gear teeth give lots of advantages, there is a high possibility of failure in gear teeth in each gear stage in the drive train system. In this research, the authors developed proper gear teeth using the basic theorem of gear failure and reliability-based design optimization. A design variable characterized by a probability distribution was applied to the static stress analysis model and the dynamics analysis model to determine an objective function and constraint equations and to solve the reliability-based design optimization. For the optimization, the authors simulated the torsional drive train system which includes rotational coordinates. First, the authors established a static stress analysis model which gives information about endurance limit and bending strength. By expressing gear mesh stiffness in terms of the Fourier series, the equations of motion including the gear mesh models and kinematical relations in the drive train system were acquired in the form of the Lagrange equations and constraint equations. For the numerical analysis, the Newmark Beta method was used to get dynamic responses including gear mesh contact forces. From the results such as the gear mesh contact force, the authors calculated the probability of failure, arranged each probability and gear teeth, and proposed a reasonable and economic design of gear teeth.
Rocznik
Strony
713--728
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
autor
  • Pohang Institute of Metal Industry Advancement, Pohang, Republic of Korea
autor
  • Department of Mechanical Engineering, Yuhan University, Bucheon, Republic of Korea
Bibliografia
  • [1] S. Wang, T. Moan, and Z. Jiang. Influence of variability and uncertainty of wind and waves on fatigue damage of a floating wind turbine drivetrain. Renewable Energy, 181:870–897, 2022. doi: 10.1016/j.renene.2021.09.090.
  • [2] Z. Yu, C. Zhu, J. Tan, C. Song, and Y. Wang. Fully-coupled and decoupled analysis comparisons of dynamic characteristics of floating offshore wind turbine drivetrain. Ocean Engineering, 247:110639, 2022. doi: 10.1016/j.oceaneng.2022.110639.
  • [3] F.K. Moghadam and A.R. Nejad. Online condition monitoring of floating wind turbines drivetrain by means of digital twin. Mechanical Systems and Signal Processing, 162:108087, 2022. doi: 10.1016/j.ymssp.2021.108087.
  • [4] W. Shi, C.W. Kim, C.W. Chung, and H.C. Park. Dynamic modeling and analysis of a wind turbine drivetrain using the torsional dynamic model. International Journal of Precision Engineering and Manufacturing, 14(1):153–159, 2013. doi: 10.1007/s12541-013-0021-2.
  • [5] M. Todorov and G. Vukov. Parametric torsional vibrations of a drive train in horizontal axis wind turbine. In Proceeding of the 1st Conference Franco-Syrian about Renewable Energy, pages 1–17, Damas, 24-28 October, 2010.
  • [6] R.C. Juvinall and K.M. Marshek. Fundamentals of Machine Component Design. John Wiley & Sons, 2020.
  • [7] Q. Zhang, J. Kang, W. Dong, and S. Lyu. A study on tooth modification and radiation noise of a manual transaxle. International Journal of Precision Engineering and Manufacturing, 13(6):1013–1020, 2012. doi: 10.1007/s12541-012-0132-1.
  • [8] B. Shlecht, T. Shulze, and T. Rosenlocher. Simulation of heavy drive trains with multimegawatt transmission power in SimPACK. In: SIMPACK Users Meeting, Baden-Baden, Germany, 21-22 March, 2006.
  • [9] M. Todorov and G. Vukov. Modal properties of drive train in horizontal axis wind turbine. The Romanian Review Precision Mechanics, Optics & Mechatronics, 40:267–275, 2011.
  • [10] D. Lee, D.H. Hodges, and M.J. Patil. Multi‐flexible‐body dynamic analysis of horizontal axis wind turbines. Wind Energy, 5(4):281–300, 2002. doi: 10.1002/we.66.
  • [11] F.L.J. Linden, P.H. Vazques, and S. Silva. Modelling and simulating the efficiency and elasticity of gearboxes, In Proceeding of the 7th Modelica Conference, pages 270–277, Como, 20-22 September, 2009.
  • [12] J. Wang, D. Qin, and Y. Ding. Dynamic behavior of wind turbine by a mixed flexible-rigid multi-body model. Journal of System Design and Dynamics, 3(3):403–419, 2009. doi: 10.1299/jsdd.3.403.
  • [13] A.A. Shabana. Computational Dynamics. John Wiley & Sons. 2009.
  • [14] A.K. Chopra. Dynamics of Structures. Pearson Education India. 2007.
  • [15] Y. Park, H. Park, Z. Ma, J. You, J. and W. Shi. Multibody dynamic analysis of a wind turbine drivetrain in consideration of the shaft bending effect and a variable gear mesh including eccentricity and nacelle movement. Frontiers in Energy Research, 8:604414, 2021. doi: 10.3389/fenrg.2020.604414.
  • [16] S.R. Singiresu. Mechanical Vibrations. Addison Wesley. 1995.
  • [17] R.R. Craig Jr and A.J. Kurdila. Fundamentals of Structural Dynamics. John Wiley & Sons. 2006.
  • [18] K.J. Bathe. Finite Element Procedures. Klaus-Jurgen Bathe. 2006.
  • [19] Y. Kim, C.W. Kim, S. Lee, and H. Park. Dynamic modeling and numerical analysis of a cold rolling mill. International Journal of Precision Engineering and Manufacturing, 14(3):407–413. 2013. doi: 10.1007/s12541-013-0056-4.
  • [20] S.J. Yoon and D.H. Choi. Reliability-based design optimization of slider air bearings. KSME International Journal, 18(10):1722–1729, 2004. doi: 10.1007/BF02984320.
  • [21] H.H. Chun,S.J. Kwon, T. and Tak. Reliability-based design optimization of automotive suspension systems. International Journal of Automotive Technology, 8(6):713–722, 2007.
  • [22] J. Fang, Y. Gao, G. Sun, and Q. Li. Multiobjective reliability-based optimization for design of a vehicledoor. Finite Elements in Analysis and Design, 67:13–21, 2013. doi: 10.1016/j.finel.2012.11.007.
  • [23] Y.L. Young, J.W. Baker, and M.R. Motley. Reliability-based design and optimization of adaptive marine structures. Composite Structures, 92(2):244–253, 2010. doi: 10.1016/j.compstruct.2009.07.024.
  • [24] G. Liu, H. Liu, C. Zhu, T. Mao, and G. Hu. Design optimization of a wind turbine gear transmission based on fatigue reliability sensitivity. Frontiers of Mechanical Engineering, 16(1):61–79, 2021. doi: 10.1007/s11465-020-0611-5.
  • [25] H. Li, H. Cho, H. Sugiyama, K.K. Choi, and N.J. Gaul. Reliability-based design optimization of wind turbine drivetrain with integrated multibody gear dynamics simulation considering wind load uncertainty. Structural and Multidisciplinary Optimization, 56 (1):183–201, 2017. doi: 10.1007/s00158-017-1693-5.
  • [26] C. Luo, B. Keshtegar, S.P. Zhu, O. Taylan, O. and X.P. Niu. Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis. Computer Methods in Applied Mechanics and Engineering, 388:114218. doi: 10.1016/j.cma.2021.114218.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-18c67b42-f4ed-438e-a8ba-7e69b4a85246
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