Application of surrogate optimization in rotor dynamics design
-
摘要:
旋翼动力学设计传统方法以桨叶固有模态避开气动激励频率为准则，从方法原理和工程实践看难以产生最小化桨毂动载荷的设计效果。为探索改进，开展了Kriging代理优化技术在旋翼动力学设计中的应用研究，针对GJB“直升机旋翼动力学设计要求”，构建了以极小化桨毂动载荷为目标，以固有频率间隔为约束的优化模型；在期望改善（EI）加点准则基础上，利用多目标优化Pareto解集，提出了一种并行加点策略，避免训练样本过于集中。对某实验旋翼开展了动力学优化设计，获得了桨叶剖面刚度与线密度的最优分布，桨毂动载荷相比初始值降低了36%，桨叶疲劳载荷也有不同程度降低。
-
关键词:
- 旋翼动力学 /
- Kriging代理模型 /
- 优化设计 /
- 减振 /
- 剖面特性
Abstract:The traditional method of rotor dynamics design is based on the criterion that the inherent mode of the blade avoids the aerodynamic excitation frequency. But, this method is difficult to minimize the hub dynamic loads. In order to explore methods of improvement, the application of Kriging surrogate optimization technology in rotor dynamics design was studied. According to GJB “helicopter rotor dynamics design requirements”, an optimization model with the goal of minimizing the hub dynamic loads and the constraint of natural frequencies interval was constructed. Based on expected improvement (EI) method, a parallel infill strategy was proposed by using Pareto solutions which can avoid local concentration of training samples. The dynamic optimization design of an experimental rotor was carried out, and the optimal distribution of blade section stiffness and density was obtained. The results showed that, the hub dynamic load was reduced by 36% compared with the initial value, and the blade fatigue loads were also reduced.
-
表 1 旋翼基本参数
Table 1. Basic parameters of the rotor
参数 数值及说明 桨叶片数 5 桨毂形式 球柔式 旋翼半径R/m 2 旋翼转速/（r/min） 1026 前进比 0.2 配平升力系数 0.015 配平阻力系数 0.0003 
下载: 导出CSV
表 2 模态频率对比
Table 2. Comparison of rotor modal frequencies
模态频率 数值 初始 优化 ωβ2 /Ω 2.56 2.47 ωβ3 /Ω 4.31 4.21 ωζ2 /Ω 5.24 5.61
下载: 导出CSV
-
[1] 国防科学技术工业委员会. 直升机旋翼动力学设计要求: GJB 5446-2005[S]. 北京: 中国航空综合技术研究所,2005: 2-4. [2] RAUCH P,GERVAIS M,CRANGA P,et al. Blue edge: the design,development and testing of a new blade concept [C]//Proceedings of the 67th Annual Forum of the American Helicopter Society. Virginia,the U S: American Helicopter Society,2011: 24-30. [3] SKLADANEK Y,HOCQUETTE J,CRANGA P. H160 dynamics development: setting new standards[C]//Proceedings of the Vertical Flight Society 75th Annual Forum. Philadelphia,US: The Vertical Flight Society,2019: 1-13. [4] TARZANIN F,YOUNG D K,PANDA B. Advanced aeroelastic optimization applied to an improved performance,low vibration rotor[C]//Proceedings of AHS 55th Annual Forum. Quebec,Canada: American Helicopter Society,1999: 1-12. [5] STUART M,NICHOLAS G. Structural optimization and aeroelastic tailoring of the BERP Ⅳ demonstrator blade[C]//Proceedings of AHS 65th Annual Forum. Texas,US: American Helicopter Society,2009: 1-20. [6] ROHL P,DORMAN P,SUTTON M,et al. A composite rotor blade structural design environment for aeromechanical assessments in conceptual and preliminary design[C]//Proceedings of AHS 68th Annual Forum. Fort Worth,US: American Helicopter Society,2012: 1-15. [7] DESVIGNE D,COISNON B,MICHEL R,et al. Multi-objective industrial optimization of high-speed helicopter main rotor blades with dynamically-adapted structural properties[C]//Proceedings of 45th European Rotorcraft Forum. Warsaw,Poland: European Rotorcraft Forum,2019: 1-14. [8] SUTTON C,MATALANIS C,MODARRES R. Aeroelastic design of a high speed highly efficient rotor[C]//Proceedings of the Vertical Flight Society 79th Annual Forum. West Palm Beach,US: The Vertical Flight Society,2023: 1-12. [9] ALLEN L,LIM J,HAEHNEL R,et al. Assessment of objective functions for rotor performance in multi-objective rotor blade optimization[C]//Proceedings of the Vertical Flight Society 79th Annual Forum. West Palm Beach,US: The Vertical Flight Society,2023: 1-16. [10] 韩忠华. Kriging模型及代理优化算法研究进展[J]. 航空学报,2016,37(11): 3197-3225. HAN Zhonghua. Kriging surrogate model and its application to design optimization: a review of recent progress[J]. Acta Aeronautica et Astronautica Sinica,2016,37(11): 3197-3225. (in ChineseHAN Zhonghua. Kriging surrogate model and its application to design optimization: a review of recent progress[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(11): 3197-3225. (in Chinese) [11] NITHIN K. Multi-fidelity optimization strategies using genetic algorithms and sequential kriging surrogates[D]. West Lafayette,US: Purdue University,2011. [12] MICHAEL J. Flexibility and efficiency enhancements for constrained global design optimization with kriging approximations[D]. Ann arbor,US: University of Michigan,2002. [13] JEONG S,MURAYAMA M,YAMAMOTO K. Efficient optimization design method using Kriging model[C]//Proceedings of the 42nd AIAA Aerospace Sciences Meeting and Exhibit. Reston,US: AIAA,2004: 1-10. [14] 韩鼎,郑建荣,周骏彦. Kriging 模型并行加点策略的多目标优化方法[J]. 机械科学与技术,2016,35(11): 1715-1720. HAN Ding,ZHENG Jianrong,ZHOU Junyan. A multi-objective optimization method using kriging model and parallel point adding strategy[J]. Mechanical Science and Technology for Aerospace Engineering[J],2016,35(11): 1715-1720. (in ChineseHAN Ding, ZHENG Jianrong, ZHOU Junyan. A multi-objective optimization method using kriging model and parallel point adding strategy[J]. Mechanical Science and Technology for Aerospace Engineering[J], 2016, 35(11): 1715-1720. (in Chinese) [15] SASENA M,PAPALAMBROS P,GOOVAERTS P. Metamodeling sampling criteria in a global optimization framework: AIAA2000-4921 [R]. Reston,US: AIAA,2000. [16] HENRY D. SCHWARTZ,CHUNG H. LEE M. Multifidelity aerodynamics with second order augmented kriging adaptive sampling[C]//Proceedings of AIAA Applied Aerodynamics Conference. Washington DC,US: AIAA,2013: 1-9. [17] PARR J,HOLDEN C,FORRESTER A,et al. Review of efficient surrogate infill sampling criteria with constraint handling[C]//Proceedings of the 2nd International Conference on Engineering Optimization. Lisbon,Portugal: International Conference on Engineering Optimization,2010: 1-10. [18] 马洋,张青斌,韩启龙,等. 二维气动问题中Kriging代理模型精度影响因素[J]. 航空动力学学报,2016,31(11): 2665-2672. MA Yang,ZHANG Qingbing,HAN Qilong,et al. Factors influencing the accuracy of kriging surrogate model in two-dimensional aerodynamic problem[J]. Journal of Aerospace Power,2016,31(11): 2665-2672. (in ChineseMA Yang, ZHANG Qingbing, HAN Qilong, et al. Factors influencing the accuracy of kriging surrogate model in two-dimensional aerodynamic problem[J]. Journal of Aerospace Power, 2016, 31(11): 2665-2672. (in Chinese) [19] 许瑞飞,宋文萍,韩忠华. 改进Kriging模型在翼型气动优化设计中的应用研究[J]. 西北工业大学学报,2010,28(4): 503-510. XU Ruifei,SONG Wenping,HAN Zhonghua. Application of improved kriging-model-based optimization method in airfoil aerodynamic design[J]. Journal of Northwestern Polytechnical University,2010,28(4): 503-510. (in ChineseXU Ruifei, SONG Wenping, HAN Zhonghua. Application of improved kriging-model-based optimization method in airfoil aerodynamic design[J]. Journal of Northwestern Polytechnical University, 2010, 28(4): 503-510. (in Chinese) [20] 韩忠华,许晨舟,乔建领,等. 基于代理模型的高效全局气动优化设计方法研究进展[J]. 航空学报,2020,41(5): 623344. HAN Zhonghua,XU Chenzhou,QIAO Jianling,et al. Recent progress of efficient global aerodynamic shape optimization using surrogate based approach[J]. Acta Aeronautica et Astronautica Sinica,2020,41(5): 623344. (in ChineseHAN Zhonghua, XU Chenzhou, QIAO Jianling, et al. Recent progress of efficient global aerodynamic shape optimization using surrogate based approach[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(5): 623344. (in Chinese) [21] 孙美建,詹浩. Kriging 模型在机翼气动外形优化中的应用[J]. 空气动力学学报,2011,29(6): 759-764. SUN Meijian,ZHAN Hao. Application of kriging surrogate model for aerodynamic shape optimization of wing[J]. ACTA Aerodynamica Sinica,2011,29(6): 759-764. (in ChineseSUN Meijian, ZHAN Hao. Application of kriging surrogate model for aerodynamic shape optimization of wing[J]. ACTA Aerodynamica Sinica, 2011, 29(6): 759-764. (in Chinese) [22] SHALU H,GOVINDAEAJAN B,SRIDHARAN A,et al. Blade shape optimization of rotors using neural networks[C]//Proceedings of the Vertical Flight Society 79th Annual Forum. West Palm Beach,US: The Vertical Flight Society,2023: 1-20. [23] 邓旭东,高乐,邓景辉. 低振动旋翼桨尖代理优化设计[J]. 振动工程学报,2021,34(1): 108-115. DENG Xudong,GAO Le DENG Jinghui. Rotor blade tip shape optimization for vibration reduction based on surrogate model[J]. Journal of Vibration Engineering,2021,34(1): 108-115. (in ChineseDENG Xudong, GAO Le DENG Jinghui. Rotor blade tip shape optimization for vibration reduction based on surrogate model[J]. Journal of Vibration Engineering, 2021, 34(1): 108-115. (in Chinese) [24] GHIRINGHELLI G L,MASARATI P,MANTEGAZZA P,et al. Multi-body analysis of the 1/5 scale wind tunnel model of the V-22 tiltrotor[C]//Proceedings of AHS 55th Annual Forum. Montreal,Canada: American Helicopter Society,1999: 1-10. -
点击查看大图
图(7) / 表(2)
计量
- 文章访问数: 306
- HTML浏览量: 180
- PDF量: 47
- 被引次数: 0