基于GA-BPNN代理模型的混合式黏滞阻尼器构造优化

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (9) : 249-260.

论文

林拥军，陈皓，郭松

作者信息 +

Structural parametric optimization of hybrid viscous damper based onGA-BPNN surrogate model

LIN Yongjun, CHEN Hao, GUO Song

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文章历史 +

摘要

采取CFD（computational fluid dynamics）数值模拟和基于代理模型的优化方法，研究混合式黏滞阻尼器在多参数设计空间内的构造参数优化问题。首先，分析黏滞阻尼器参数对力学性能的影响规律，基于正交试验理论选取间隙宽度、缸体内径、活塞厚度、阻尼孔直径为关键参数，利用优化拉丁超立方设计和CFD数值模拟获得初始样本点耗能性能指标；然后，使用遗传算法(genetic algorithm，GA)和反向传播神经网络(backpropagation neural network，BPNN)的方法构建代理模型，其能够准确反映构造参数耗能性能指标和构造参数之间的繁复关系。进一步地，循环更新GA-BPNN代理模型并结合非支配排序遗传算法(NSGA-II)，以最佳耗能能力为目标，实现在全设计空间内的寻优，确定在典型阻尼介质黏度下双出杆混合式黏滞阻尼器的最优构造参数。最后，探讨了构造参数对黏滞阻尼器流动性能的影响机理。研究结果表明，优化双出杆混合式黏滞阻尼器构造参数在选用基于GA-BPNN代理模型的优化方法时，可有效提高计算效率且具有较高预测精度。从性能指标提升角度，全黏度下最优构造参数阻尼孔直径、活塞厚度、缸体内径和间隙宽度分别为1.31mm、120.78mm、199.87mm、0.5mm，相较于基准构造，最大阻尼力和能量耗材比分别提升33.8%和64.8%。该方法可为黏滞阻尼器构造参数优化及相关研究提供参考。

Abstract

The present investigation aims to explore optimizing design parameters for a hybrid viscous damper employing advanced techniques and methodologies—computational fluid dynamics (CFD) simulation and optimization based on surrogate models. Firstly, the effects of damper parameters on mechanical performance are analyzed. Key parameters, including gap width, cylinder inner diameter, piston thickness, and damping hole diameter, are selected based on orthogonal experiment theory. Initial sample points are obtained through CFD simulation and optimized Latin hypercube design (OPLHS). Secondly, to establish a surrogate model that precisely reflects the intricate correlation between design parameters and energy consumption performance, a genetic algorithm (GA) and a back-propagation neural network (BPNN) approach are employed. Furthermore, the GA-BPNN surrogate model is cyclically updated and combined with the non-dominated sorting genetic algorithm (NSGA-II), aiming at the optimal energy dissipation capacity. The optimization was realized in the whole design space, and the optimal construction parameters of the hybrid viscous damper with a double outlet bar are determined under the viscosity of the typical damping medium. Finally, the influence mechanism of structural parameters on the flow performance of viscous dampers is discussed. The optimal design parameters for the entire viscosity case, including the damper hole diameter, piston thickness, cylinder inner diameter, and gap width, are 1.31mm, 120.78mm, 199.87mm, and 0.5mm, respectively. Compared to the baseline design, the maximum damping force and energy consumption ratio increased by 33.8% and 64.8%, respectively. This method provides a reference for optimizing design parameters and related research for viscous dampers.

导出引用

林拥军，陈皓，郭松. 基于GA-BPNN代理模型的混合式黏滞阻尼器构造优化[J]. 振动与冲击, 2024, 43(9): 249-260

LIN Yongjun, CHEN Hao, GUO Song. Structural parametric optimization of hybrid viscous damper based onGA-BPNN surrogate model[J]. Journal of Vibration and Shock, 2024, 43(9): 249-260

参考文献

[1] ASCE 7-05 Minimum Design Loads for Buildings and Other Structures [S]. Reston, VA：2006. [2] GB50011-2010建筑抗震设计规范 [S]. 北京: 中国建筑工业出版社, 2010. GB50011-2010 Code for Seismic Design of Buildings [S]. Beijing: China Architecture and Building Press, 2010. [3] JGJ 297-2013建筑消能减震技术规程 [S]. 北京: 中国建筑工业出版社, 2013. JGJ 297-2013 Technical Specification for Building Energy Dissipation and Shock Absorption[S]. Beijing: China Architecture and Building Press, 2013. [4] Markris N, Constantinou M C. Viscous dampers: testing, modelling, application in vibration and seismic isolation [R]. Buffalo: State University of New York NCEER, 1990 [5] Miyamoto H K, Gilani A S J, Wada A, et al. Limit states and failure mechanisms of viscous dampers and the implications for large earthquakes[J]. Earthquake engineering & structural dynamics, 2010, 39(11): 1279-1297. [6] 黄镇，李爱群. 新型黏滞阻尼器原理与试验研究 [J]. 土木工程学报, 2009, 42(6):61-65. Huang Zhen, Li Aiqun. Principle and Experimental Study of a New Viscous Damper [J]. Journal of Civil Engineering, 2009, 42(6): 61-65. [7] 张敏，汪大洋，耿鹏飞. 黏滞阻尼器在实际工程中的应用研究 [J]. 土木工程学报, 2013, (S1) : 45-50 . Zhang Min, Wang Dayang, Geng Pengfei. Research on the Application of viscous Damper in practical Engineering [J]. Chinese Journal of Civil Engineering, 2013, (S1): 45-50. [8] Esfandiyari R, Nejad S M, Marnani J A, et al. Seismic behaviour of structural and non-structural elements in RC building with bypass viscous dampers[J]. Steel and Composite Structures, 2020, 34(4): 487-497 [9] 吕江，张仲勇，宋腾腾，汪正兴.新型多功能粘滞阻尼器研究及应用 [J]. 世界桥梁, 2020, 48(6):60-63. Lv Jiang, Zhang Zhongyong, Song Tengteng, et al. Research and application of a new multi-function viscous damper [J]. World Bridge, 2020, 48(6)：60-63. [10] Yeh F Y, Chang K C, Chen T W, et al. The dynamic performance of a shear thickening fluid viscous damper[J]. Journal of the Chinese Institute of Engineers, 2014, 37(8): 983-994 [11] Yamamoto M, Minewaki S, Nakahara M, et al. Concept and performance testing of a high-capacity oil damper comprising multiple damper units[J]. Earthquake Engineering & Structural Dynamics, 2016, 45(12): 1919-1933 [12] 冷宙. 双出杆孔隙式油—粉土粘滞阻尼器减震性能研究 [D].重庆：重庆大学, 2016. Leng Zhou. Study on Damping performance of porous oil-silty soil viscous Damper with Double Discharge Rod [D]. Chongqing: Chongqing University, 2016. [13] 王兆勇，郑朝荣，Joshua A M，等.基于代理模型的方形凹角截面超高层建筑气动外形优化[J/OL].土木工程学报,2022. https://doi.org/10.15951/j.tmgcxb.21121203. Wang Zhaoyong, ZHENG Chaorong, Joshua A M, et al. Aerodynamic Shape Optimization of Square Concave Corner Section Super tall Buildings based on Proxy Model [J/OL].Chinese Journal of Civil Engineering,2022. https://doi.org/10.15951/j.tmgcxb.21121203. [14] 周光埛. 流体力学（第二版）上册 [M]. 北京: 高等教育出版社, 2011. Zhou Guangchong. Fluid Mechanics (Second Edition) Volume I [M] Beijing: Higher Education Press, 2011. [15] 黄政. 第三代黏滞阻尼器试验与仿真研究 [D].南京：东南大学, 2018. Huang Zheng. Experimental and Simulation Research on the Third Generation Viscous Damper [D]. Nanjing: Southeast University， 2018. [16] 赵艳男,杜文风,王英奇,等.基于BP神经网络算法的树状结构智能找形研究[J].建筑结构学报,2022,43(04):77-85. ZHAO Yannan, DU Wenfeng, WANG Yingqi, et al. Study on intelligent shape finding for tree-like structures based on BP neural network algorithm [J].Journal of Building Structures,2022,43(04):77-85. [17] Liu F, Xu J, Tan S, et al. Orthogonal Experiments and Neural Networks Analysis of Concrete Performance[J]. Water, 2022, 14(16): 2520. [18] 刘浩洋,户将,李勇锋,等.最优化:建模、算法与理论[M].北京:高等教育出版社,2020. Liu Haoyang, Hu Jiang, Li Yongfeng, et al. Optimization: Modeling, Algorithms and Theory [M]. Beijing: Higher Education Press, 2020. [19] 牛赢,焦锋,赵波,等.基于NSGA-Ⅱ的钛合金纵扭超声铣削多目标参数优化[J].振动与冲击,2020,39(21):241-249. NIU Ying， JIAO Feng， ZHAO Bo, et al. Multi-objective parameter optimization for ultrasonic milling of titanium alloy in longitudinal and torsional directions based on NSGA-II[J]. Journal of Vibration and Shock,2020,39(21):241-249.