Multidisciplinary design optimization method of overall aircraft power system
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摘要:
针对航空动力系统整机设计优化中存在的计算规模庞大、耦合关系复杂、学科冲突尖锐、实现过程困难等问题，从系统分解、系统建模和系统求解3个方面，发展并应用了高精度代理模型、高效优化策略和智能多目标优化算法等多类先进技术，并建立了基于多学科设计优化(MDO)的航空动力系统整机设计方法。分别以涡轴发动机、涡扇发动机、涡喷发动机、直升机传动系统主减速器主传动链为对象开展了航空动力系统整机MDO工程应用研究，并针对涡喷发动机进行了优化后压气机性能和强度试验验证以及整机试验验证。研究表明：航空动力系统整机MDO能够有效释放设计潜能，显著提高产品综合性能，大幅缩短研制周期，其工程应用前景十分广阔，将变革航空动力系统设计及研发工作。
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关键词:
- 多学科设计优化(MDO) /
- 航空动力系统 /
- 直升机传动系统 /
- 整机优化 /
- 整机验证
Abstract:In view of the difficulties in the design optimization of an overall aircraft power system, such as the considerable calculation scale, complex coupling relationships, sharp discipline conflicts and complicated implementation processes, a variety of advanced technologies such as the high-precision surrogate models, the efficient optimization strategies and the intelligent multi-objective optimization algorithms were developed and applied in three aspects: system decomposition, system modelling and system solution. Finally, the design method of an overall aircraft power system based on multidisciplinary design optimization (MDO) was established. Taking a turboshaft engine, a turbofan engine, a turbojet engine and a main transmission chain of the main reducer of a helicopter transmission system as examples, the engineering application research on MDO of the overall aircraft power system was carried out. Moreover, the performance and strength tests of the optimised compressor, and the optimised overall system test were carried out and verified for the turbojet engine. The research showed that the MDO of the overall aircraft power system could effectively unleash the design potential, significantly improve the comprehensive performance of products and considerably shorten the development cycle. Its engineering application prospect is quite broad, which will change the design and development of an aircraft power system.
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图 4 涡轴发动机整机MDO系统分解示意图
Figure 4. System decomposition diagrams of the overall system MDO of a turboshaft engine
图 5 总体性能与部件性能简化接口
Figure 5. Simplified interface between the overall performance and the component performance
图 8 涡轴发动机整机优化前后主要零部件几何模型
Figure 8. Geometric models of main parts of a turboshaft engine before and after the overall system optimization
图 9 涡扇发动机整机优化前后主要零部件几何模型
Figure 9. Geometric models of main parts of a turbofan engine before and after the overall system optimization
图 10 基于CO方法的涡喷发动机整机MDO过程
Figure 10. MDO process of overall system of a turbojet engine based on the CO method
图 11 涡喷发动机单状态整机优化前后主要零部件几何模型
Figure 11. Geometric models of main parts of a turbojet engine before and after the single-state overall system optimization
图 12 多状态优化前后离心压气机的几何模型
Figure 12. Geometric models of a centrifugal compressor before and after the multi-state optimization
图 13 某2000 kW级直升机传动系统主减速器主传动链的构型示意图
Figure 13. Schematic diagram of the main transmission chain of main retarder of a 2000 kW helicopter transmission system
图 14 基于CO方法的2000 kW级直升机传动系统主减速器主传动链MDO过程
Figure 14. MDO process of the main transmission chain of main retarder of a 2000 kW helicopter transmission system based on the CO method
图 15 2000 kW级直升机传动系统主减速器主传动链优化前后主要零件的几何模型
Figure 15. Geometric models of main parts of the main transmission chain of main retarder of a 2000 kW helicopter transmission system before and after optimization
图 16 500 kW级传动系统主减速器主传动链示意图及有限元模型
Figure 16. Schematic diagram and finite element model of the main transmission chain of main retarder of a 500 kW helicopter transmission system
图 17 500 kW级直升机传动系统主减速器主传动链优化前后主要零件的几何模型
Figure 17. Geometric models of main parts of the main transmission chain of main retarder of a 500 kW helicopter transmission system before and after optimization
图 18 组合压气机试验件的构型示意图
Figure 18. Configuration diagrams of the tested combined compressor
图 19 单状态优化前后离心叶轮和径向扩压器实物
Figure 19. Machined centrifugal impeller and radial diffuser before and after the single-state optimization
图 20 单状态优化前后组合压气机的试验特性线
Figure 20. Test characteristic line of the combined compressor before and after the single-state optimization
图 21 多状态优化前后组合压气机的试验特性线
Figure 21. Test characteristic line of the combined compressor before and after the multi-state optimization
图 22 优化前后涡喷发动机试车数据
Figure 22. Test data of a turbojet engine before and after optimization
表 1 涡轴发动机整机优化前后关键性能参数（归一化）
Table 1. Key performance parameters of a turboshaft engine before and after the overall system optimization （normalization）
性能参数 优化前 优化后 变化率/% 耗油率 1.0000 0.9820 −1.80 功率 1.0000 1.0260 2.60 燃烧室出口温度 1.0000 1.0040 0.40 动力涡轮效率 1.0000 1.0003 0.03 发动机总引气量 1.0000 1.0000 0 压气机效率 1.0000 1.0030 0.30 压气机压比 1.0000 1.0017 0.17 燃气涡轮效率 1.0000 1.0040 0.40 燃烧室总压损失 1.0000 0.9990 −0.10 燃烧室效率 1.0000 1.0000 0 
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表 2 涡扇发动机整机优化前后的关键性能参数（归一化）
Table 2. Key performance parameters of a turbofan engine before and after the overall system optimization （normalization）
性能参数 优化前 优化后（Pareto解） 发动机质量 1.0000 0.9661 1.0036 1.0127 1.0219 1.0254 1.0139 轮挡燃油相对变化量 1.0000 0.9981 0.9973 0.9995 1.0032 1.0036 1.0052 NOx排放量特性值 1.0000 1.0029 1.0005 0.9956 1.0041 1.0049 1.0110 噪声声功级（飞越工况） 1.0000 1.0031 0.9994 0.9988 0.9975 0.9988 0.9994 噪声声功级（边线工况） 1.0000 1.0038 0.9994 0.9994 0.9994 0.9987 0.9994 噪声声功级（进场工况） 1.0000 1.0033 0.9993 0.9987 0.9980 0.9987 0.9967 涵道比 1.0000 0.9808 1.0203 1.0198 1.0105 1.0035 0.9967 风扇外涵压比 1.0000 1.0201 0.9953 0.9926 0.9920 0.9913 0.9940 风扇内涵/增压级压比 1.0000 0.9878 0.9783 0.9784 0.9815 0.9947 0.9786 高压压气机压比 1.0000 0.9831 1.0109 1.0109 0.9960 0.9977 0.9961 燃烧室出口总温 1.0000 1.0137 1.0032 1.0005 0.9958 0.9934 0.9927
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表 3 涡喷发动机单状态整机优化前后关键性能参数（归一化）
Table 3. Key performance parameters of a turbojet engine before and after the single-state overall system optimization （normalization）
性能参数 优化前 优化后 变化率/% 耗油率 1.0000 0.9622 −3.78 推重比 1.0000 1.0485 4.85 压气机压比 1.0000 1.0000 0 压气机效率 1.0000 1.0200 2.00 涡轮效率 1.0000 1.0230 2.30 燃烧室总压损失 1.0000 0.9927 −0.73 燃烧室效率 1.0000 1.0000 0
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表 4 多状态优化前后各学科指标变化情况（归一化）
Table 4. Key index parameters of each discipline before and after the multi-state optimization （normalization）
学科指标 优化前 优化后 变化率/% 最大转速状态设计点相对气动效率 1.000 1.036 3.6 巡航状态设计点相对气动效率 1.000 1.040 4.0 最大转速状态设计点相对质量流量 1.000 1.006 0.6 巡航状态设计点相对质量流量 1.000 1.012 1.2 最大转速状态设计点相对压比 1.000 1.014 1.4 巡航状态设计点相对压比 1.000 1.019 1.9 最大转速状态喘振裕度 1.000 0.986 −1.4 巡航状态喘振裕度 1.000 1.164 16.4 最大转速状态最小共振裕度 1.000 0.876 −12.4 巡航状态最小共振裕度 1.000 1.242 24.2 最大转速状态叶片最大径向应力 1.000 1.022 2.2 最大转速状态叶片最大当量应力 1.000 0.828 −17.2
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表 5 基于多种算法优化前后的2000 kW级直升机传动系统主减速器主传动链关键性能参数（归一化）
Table 5. Key performance parameters of the main transmission chain of main retarder of a 2000 kW helicopter transmission system before and after the optimizations based on multiple algorithms （normalization）
性能参数 优化前 优化后 主目标
函数法主目标
函数法固定权系数
加权求和法变权系数
加权求和法基于切比雪夫的
MOEA/Decomposition
算法（Pareto解）目标函数 传动效率 质量 传动效率+质量 传动效率+质量 传动效率+质量 传动效率 1.0000 1.0058 1.0000 0.9975 1.0060 1.0063 1.0059 1.0056 1.0048 质量/kg 1.0000 1.0048 0.8613 0.8146 0.9167 0.9956 0.9461 0.9141 0.8964
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表 6 500 kW级直升机传动系统主减速器主传动链优化前后关键性能参数（归一化）
Table 6. Key performance parameters of the main transmission chain of main retarder of a 500 kW helicopter transmission system before and after optimization （normalization）
性能参数 优化前 优化后 变化率/% 主传动链质量 1.000 0.838 −16.2 太阳轮-行星齿轮最大无量纲油膜压力 1.000 0.896 −10.4 输入齿轮-输出齿轮最大无量纲油膜压力 1.000 0.962 −3.8
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表 7 单状态优化前后组合压气机关键性能参数试验结果
Table 7. Test results of key performance parameters of the combined compressor before and after the single-state optimization
状态 方案 堵点流量/（kg/s） 最高压比 最高效率 最大转速状态 优化前 1.902 5.979 0.740 优化后 1.946 5.854 0.791 变化 2.31% −2.09% 0.051 巡航状态 优化前 1.725 4.781 0.771 优化后 1.641 4.380 0.770 变化 −4.87% −8.39% −0.001
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表 8 优化前后涡喷发动机台架试车数据
Table 8. Test data of a turbojet engine before and after optimization
方案 物理转速/
（r/min）换算
转速换算
推力/
N换算排气
温度/
℃换算耗油率/
（kg/（N∙h））进口空气换算
流量/
（kg/s）换算燃气流量/
（kg/min）压比 优化前
温度为28.8℃，
压力为100637 Pa41998 0.7890 404.0 418.1 0.1354 1.311 0.912 3.261 46799 0.8792 626.8 474.6 0.1186 1.600 1.239 4.165 49396 0.9277 789.1 528.4 0.1175 1.743 1.546 4.729 50180 0.9427 843.5 548.5 0.1168 1.784 1.643 4.920 51220 0.9621 918.2 578.1 0.1175 1.836 1.798 5.166 51989 0.9765 973.3 602.0 0.1180 1.873 1.915 5.353 优化后
温度为28.9℃，
压力为100313 Pa41998 0.7886 408.8 396.6 0.1138 1.364 0.774 3.354 46799 0.8789 629.4 471.6 0.1044 1.647 1.095 4.259 49400 0.9279 791.2 528.7 0.1040 1.791 1.373 4.838 50177 0.9423 842.4 544.5 0.1048 1.830 1.469 5.003 51216 0.9621 907.9 567.7 0.1058 1.875 1.599 5.217 51997 0.9759 944.8 583.4 0.1068 1.895 1.682 5.315
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[1] 尹泽勇, 米栋. 航空发动机多学科设计优化[M]. 北京: 北京航空航天大学出版社, 2015. [2] SOBIESZCZANSKI-SOBIESKI J. A linear decomposition method for large optimization problems-Blueprint for development[R]. NASA-TM-83248, 1982. [3] AIAA Technical Committee for MDO. White paper on current state of the art in multidisciplinary design optimization[R]. Washington: AIAA Technical Committee for MDO, 1991. [4] AIAA Technical Committee for MDO. White paper on industrial experimence with MDO[R]. Washington: AIAA Technical Committee for MDO, 1998. [5] YOUNG J, ANDERSON R, YURKOVICH R. A description of the F/A-18E/F design and design process[R]. AIAA-98-4701, 1998. [6] VAN DER VELDEN A, KOKAN D, KELM R, et al. Application of MDO to large subsonic transport aircraft[R]. AIAA-2000-0844, 2000. [7] RÖHL P, HE B, FINNIGAN P. A collaborative optimization environment for turbine engine development[R]. AIAA-98-4734, 1998. [8] YANG R J , THO C H , GU L. Recent development in multidisciplinary design optimization of vehicle structures[C]//9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Atlanta, Georgia: AIAA, 2002:1-8. [9] YAN J,BROESICKE O A,TONG X,et al. Multidisciplinary design optimization of distributed energy generation systems: the trade-offs between life cycle environmental and economic impacts[J]. Applied Energy,2021,284: 116197.1-116197.9. [10] MARTIN T J,DULIKRAVICH G S. Analysis and multidisciplinary optimization of internal coolant networks in turbine blades[J]. Journal of Propulsion and Power,2002,18(4): 896-906. doi: 10.2514/2.6015 [11] KRÖGER G, SCHNELL R, HUMPHREYS N D. Optimised aero-dynamic design of an OGV with reduced blade count for low noise aircraft engines[C]//Turbo Expo: Power for Land, Sea, and Air. Copenhagen, Denmark: American Society of Mechanical Engineers, 2012: 407-418. [12] SALNIKOV A, DANILOV M. A centrifugal compressor im-peller: a multidisciplinary optimization to improve its mass, strength, and gas-dynamic characteris-tics[C]//Turbo Expo: Power for Land, Sea, and Air. Charlotte, US: American Society of Mechanical Engi-neers, 2017: 1-10. [13] DENG X, GUO F, LIU Y, et al. Aero-mechanical optimiza-tion design of a transonic fan blade[C]//Turbo Expo: Power for Land, Sea, and Air. San Antonio, US: American Society of Mechanical Engineers, 2013: 1-12. [14] POPOV G, GORIACHKIN E, KOLMAKOVA D, et al. Multicriteria optimization of axial low pressure compressor of gas turbine power plant[C]//Turbo Expo: Power for Land, Sea, and Air. Seoul, South Korea: American Society of Mechanical Engineers, 2016: 1-10. [15] AULICH A L, GOERKE D, BLOCHER M, et al. Multidisciplinary automated optimization strategy on a counter rotating fan[C]//Turbo Expo: Power for Land, Sea, and Air. San Antonio, US: American Society of Mechanical Engineers, 2013: 1-14. [16] SEELEY C, TANGIRALA V, MIKA D, et al. Multidisciplinary analysis and optimization of combustion sub-system us-ing a network-centric approach[C]//19th AIAA Applied Aerodynamics Conference. Anaheim, US: AIAA, 2001: 1-12. [17] 王荣桥,贾志刚,杨俊杰,等. 基于多层优化策略的涡轮盘叶设计研究[J]. 航空动力学报,2012,27(6): 1201-1209. doi: 10.13224/j.cnki.jasp.2012.06.025WANG Rongqiao,JIA Zhigang,YANG Junjie,et al. Study on disk and blade design based on multi-layer optimization strategy[J]. Journal of Aerospace Power,2012,27(6): 1201-1209. (in Chinese) doi: 10.13224/j.cnki.jasp.2012.06.025 [18] 申秀丽,龙丹,董晓琳. 航空发动机初步设计阶段涡轮流道多学科优化设计分析方法[J]. 航空动力学报,2014,29(6): 1369-1375. doi: 10.13224/j.cnki.jasp.2014.06.016SHEN Xiuli,LONG Dan,DONG Xiaolin. Multidisciplinary design optimization of aero-engine turbine flow path in preliminary design phase[J]. Journal of Aerospace Power,2014,29(6): 1369-1375. (in Chinese) doi: 10.13224/j.cnki.jasp.2014.06.016 [19] LYTLE J K. The numerical propulsion system simulation: a multidisciplinary design system for aerospace vehicles[R]. NASA/TM-1999-209194, 1999. [20] JONES M J, BRADBROOK S J, NURNEY K. A preliminary engine design process for an affordable capability[R]. United Kingdom: Rolls-Royce Plc Bristol New Projects Engineering, 2003. [21] DONUS F, BRETSCHNEIDER S, SCHABER R, et al. The Architecture and Application of Preliminary Design Systems[R]. ASME Paper GT2011-45614, 2011. [22] PANCHENKO V, MOUSTAPHA H, MAH S, et al. Preliminary multi-disciplinary optimization in turbomachinery de-sign[R]. Longueuil, Canada: Pratt and Whitney Canada Corp, 2003. [23] TRIBES C, TRÉPANIER J Y, PANCHENKO Y, et al. Preliminary multidisciplinary design optimization system: A software solution for early gas turbine conception [C]//ASME Turbo Expo 2005: Power for Land, Sea, and Air. Reno, US: American Society of Mechanical Engineers, 2005: 213-220. [24] FRANCOIS B, STEPHEN M, JASMIN T. Preliminary multi-disciplinary optimization (PMDO) an example at engine level[R]. Montreal: 19th International Symposium on Air Breathing Engines, 2009. [25] 米栋. 航空发动机压气机叶片多学科设计优化技术研究[D]. 北京: 中国航空研究院, 2004.MI Dong. Study on multidisciplinary design optimization technology of aeroengine compressor blade[D]. Beijing: Chinese Aeronautical Establishment, 2004. (in Chinese) [26] 吴立强. 多学科设计优化技术在航空发动机涡轮叶片设计中的应用研究[D]. 北京: 中国航空研究院, 2004.WU Liqiang. Research on application of multidisciplinary design optimization technology in aero-engine turbine blade design[D]. Beijing: Chinese Aeronautical Establishment, 2004. (in Chinese) [27] 肖根升. 基于正交试验设计和响应面模型的多学科设计优化及其应用研究[D]. 北京: 中国航空研究院, 2005.XIAO Gensheng. Research on multidisciplinary design optimization and application based on orthogonal exper-imental design and response surface model [D]. Beijing: Chinese Aeronautical Establishment, 2005. (in Chinese) [28] 刘飞春. 航空发动机涡轮级叶片多学科设计优化研究与应用[D]. 北京: 中国航空研究院, 2006.LIU Feichun. Research and application of multidiscipli-nary design optimization for aeroengine turbine stage blades[D]. Beijing: Chinese Aeronautical Establishment, 2006. (in Chinese) [29] 李立君. 航空发动机总体多学科多目标设计优化[R]. 湖南: 中国航空动力机械研究所, 2007. [30] 张立章. 基于降维技术的航空发动机多学科设计优化方法研究[D]. 北京: 北京航空航天大学, 2019.ZHANG Lizhang. Research on multidisciplinary design optimization method of aeroengine based on dimen-sionality reduction technology[D]. Beijing: Beijing University of Aeronautics and Astronautics, 2019. (in Chinese) [31] 闫成. 航空发动机MDO高精度代理模型及协作优化策略研究[D]. 北京: 北京航空航天大学, 2019.YAN Cheng. Research on high-precision surrogate model and cooperative optimization strategy of aero-engine MDO[D]. Beijing: Beijing University of Aeronautics and Astronautics, 2019. (in Chinese) [32] 尹泽勇,米栋,吴立强,等. 航空发动机多学科设计优化技术研究[J]. 中国工程科学,2007,9(6): 1-10. doi: 10.3969/j.issn.1009-1742.2007.06.001YIN Zeyong,MI Dong,WU Liqiang,et al. Study on multidisciplinary design optimization of aero-engine[J]. Engineering Science,2007,9(6): 1-10. (in Chinese) doi: 10.3969/j.issn.1009-1742.2007.06.001 [33] 张立章, 尹泽勇, 米栋, 等 基于改进的BLISS2000优化策略的涡轮级多学科设计优化[J]. 机械强度, 2015, 37(4): 639-645.ZHANG Lizhang, YIN Zeyong, MI Dong, et al. Multidisciplinary design optimization of turbine stage based on improved bi-level integrated system synthesis [J]. Journal of Mechanical Strength, 2015, 37(4): 639-645.（in Chinese） [34] 张立章,尹泽勇,米栋,等. 基于自适应本征正交分解的涡轮级多学科设计优化[J]. 推进技术,2017,38(6): 1249-1257. doi: 10.13675/j.cnki.tjjs.2017.06.007ZHANG Lizhang,YIN Zeyong,MI Dong,et al. Multidisciplinary design optimization for turbine stage based on self-adaptive proper orthogonal decomposition[J]. Journal of Propulsion Technology,2017,38(6): 1249-1257. (in Chinese) doi: 10.13675/j.cnki.tjjs.2017.06.007 [35] 李立君,尹泽勇,乔渭阳. 基于多目标遗传算法的航空发动机总体性能优化设计[J]. 航空动力学报,2006,21(1): 13-18. doi: 10.3969/j.issn.1000-8055.2006.01.003LI Lijun,YIN Zeyong,QIAO Weiyang. Performance optimal design of aircraft engine based on multi-objective genetic algorithms[J]. Journal of Aerospace Power,2006,21(1): 13-18. (in Chinese) doi: 10.3969/j.issn.1000-8055.2006.01.003 [36] YIN Zeyong. Multidisciplinary design optimization technology accepting challenge of advanced green aeroengine design[R]. Italy: Plenary Lecture on the Cira-Crain2 Short Course, 2014. [37] 闫成,尹泽勇,郭福水,等. 基于MDO策略的民用航空发动机概念设计研究[J]. 航空动力学报,2017,32(8): 1911-1921. doi: 10.13224/j.cnki.jasp.2017.08.015YAN Cheng,YIN Zeyong,GUO Fushui,et al. Research on the conceptual design of a civil aero-engine based on MDO strategies[J]. Journal of Aerospace Power,2017,32(8): 1911-1921. (in Chinese) doi: 10.13224/j.cnki.jasp.2017.08.015 [38] YAN C,SHEN X,GUO F. Novel two-stage method for low-order polynomial model[J]. Mathematical Problems in Engineering,2018: 1-13. [39] YAN C,SHEN X,GUO F. An improved support vector regression using least squares method[J]. Structural and Multidisciplinary Optimization,2018,57(6): 2431-2445. doi: 10.1007/s00158-017-1871-5 [40] YAN C,ZHU J,SHEN X,et al. Ensemble of regression-type and interpolation-type metamodels[J]. Energies,2020,13(3): 1-20. [41] YAN C,DU H,KANG E,et al. AVEI-BO: an efficient Bayesian optimization using adaptively varied expected improvement[J]. Structural and Multidisciplinary Optimization,2022,65(6): 1-23. [42] YAN C, YIN Z, GUO F, et al. A newly improved collabora-tive optimization strategy: Application to conceptual multidisciplinary design optimization of a civil aero-engine[C]//Turbo Expo: Power for Land, Sea, and Air. Charlotte, US: American Society of Mechanical Engi-neers, 2017: 1-12. -
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