进出口边界压力扰动对多级轴流压气机气动不确定性影响
收稿日期: 2024-01-17
修回日期: 2024-02-03
录用日期: 2024-03-11
网络出版日期: 2024-03-19
基金资助
浙江省自然科学基金(LXR21E060001);国家科技重大专项(J2019-II-00120032);国家自然科学基金(51976183)
Impact of pressure variations at inlet and outlet boundaries on aerodynamic performance of multi-stage axial compressor
Received date: 2024-01-17
Revised date: 2024-02-03
Accepted date: 2024-03-11
Online published: 2024-03-19
Supported by
Zhejiang Provincial Natural Science Foundation of China(LXR21E060001);National Science and Technology Major Project of China(J2019-II-00120032);National Natural Science Foundation of China(51976183)
量化边界流动扰动的不确定性影响是压气机鲁棒性气动设计的主要研究内容之一。基于计算流体力学的多级压气机气动不确定性量化（UQ）非常耗时，高效的气动预测方法有助于提高UQ的计算效率。本文采用时间推进欧拉通流方法（Euler-TFM）和直接蒙特卡洛模拟（MCS）研究了进出口边界压力扰动对某型4.5级轴流压气机的气动不确定性影响。首先，实现了基于Euler-TFM的压气机气动性能预测，并通过与三维数值计算结果的对比验证了Euler-TFM的精度。随后采用MCS量化了不同工况下进口总压、出口背压、两者耦合的不确定性变化对压气机绝热效率、流量和压比的影响大小。基于Sobol灵敏度分析的结果表明：在近失速工况下，进口总压和出口背压扰动对性能变化的影响存在强烈的耦合作用。最后对流场进行统计分析，揭示了边界流动扰动对该压气机流场和性能变化的影响规律，并评估了气动设计的鲁棒性。
李灿灿 , 肖左利 , 罗佳奇 . 进出口边界压力扰动对多级轴流压气机气动不确定性影响[J]. 航空学报, 2024 , 45(19) : 630168 -630168 . DOI: 10.7527/S1000-6893.2024.30168
Flow variations commonly exist in multi-stage turbomachines， strongly impacting the performance of these machines at most times. Quantification of performance impact of flow variations is one of the most attractive topics because it benefits the robust design of turbomachines. Statistical analysis is essential in Uncertainty Quantification （UQ）， yet flow computations of multi-stages are expensive. Apart from the efficient UQ methods， a fast performance calculator is another answer to the computational cost decrease of UQ. This paper presents a method for evaluating the performance changes of a multi-stage axial compressor considering pressure variations at both the inlet and outlet boundaries using the time-marching Euler Throughflow Method （TFM）. First， the time-marching TFM is introduced to performance prediction of a low-speed axial compressor. The results are verified and validated by comparing with those obtained by a three-dimensional computational fluid dynamics method. Then the direct Monte Carlo simulation is employed to evaluate the changes of adiabatic efficiency， mass flow rate and total pressure ratio at different operation conditions， considering the variations of separated inlet total pressure， outlet static pressure， and the simultaneous variations of these two. Moreover， for the UQ case with simultaneous flow variations， Sobol sensitivity analysis indicates that the interactions between the inlet total pressure and outlet static pressure are strong under the near stall operation condition. Finally， flow solutions are statistically analyzed to reveal the impact mechanisms of uncertainties on the flow field variations and the performance changes of the multi-stage axial compressor， through which the potential robust design is suggested.
| 1 | STENNING A H. Inlet distortion effects in axial compressors［J］. Journal of Fluids Engineering， 1980， 102（1）： 7-13. |
| 2 | BRY P， LAVAL P， BILLET G. Distorted flow field in compressor inlet channels［J］. Journal of Engineering for Gas Turbines and Power， 1985， 107（3）： 782-791. |
| 3 | PETERS T， BüRGENER T， FOTTNER L. Effects of rotating inlet distortion on a 5-stage HP-compressor［C］∥ASME Turbo Expo 2001： Power for Land， Sea， and Air. New York： ASME， 2001： V001T03A005. |
| 4 | 郑新前， 王钧莹， 黄维娜， 等. 航空发动机不确定性设计体系探讨［J］. 航空学报， 2023， 44（7）： 027099. |
| ZHENG X Q， WANG J Y， HUANG W N， et al. Uncertainty-based design system for aeroengines［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（7）： 027099 （in Chinese）. | |
| 5 | WU X J， ZHANG W W， SONG S F， et al. Sparse grid-based polynomial chaos expansion for aerodynamics of an airfoil with uncertainties［J］. Chinese Journal of Aeronautics， 2018， 31（5）： 997-1011. |
| 6 | LECERF N， JEANNEL D， LAUDE A. A robust design methodology for high-pressure compressor through?ow optimization［C］∥ASME Turbo Expo 2003： Power for Land， Sea， and Air. New York： ASME， 2003： 1225-1236. |
| 7 | GHISU T， PARKS G T， JARRETT J P， et al. Robust design optimization of gas turbine compression systems［J］. Journal of Propulsion and Power， 2011， 27（2）： 282-295. |
| 8 | ZHAO H， GAO Z H， XU F， et al. Review of robust aerodynamic design optimization for air vehicles［J］. Archives of Computational Methods in Engineering， 2019， 26（3）： 685-732. |
| 9 | GARZON V E， DARMOFAL D L. Impact of geometric variability on axial compressor performance［J］. Journal of Turbomachinery， 2003， 125（4）： 692-703. |
| 10 | XIA Z H， LUO J Q， LIU F. Statistical evaluation of performance impact of flow variations for a transonic compressor rotor blade［J］. Energy， 2019， 189： 116285. |
| 11 | WANG J Y， YANG H L， ZHOU K， et al. Performance dispersion control of a multistage compressor based on precise identification of critical features［J］. Aerospace Science and Technology， 2022， 129： 107845. |
| 12 | LOEVEN G J A， WITTEVEEN J A S， BIJL H. Probabilistic collocation： an efficient non-intrusive approach for arbitrarily distributed parametric uncertainties［C］∥45th AIAA Aerospace Sciences Meeting and Exhibit. Reston： AIAA， 2007： 317. |
| 13 | LUO J Q， LIU F. Statistical evaluation of performance impact of manufacturing variability by an adjoint method［J］. Aerospace Science and Technology， 2018， 77： 471-484. |
| 14 | XIU D B， KARNIADAKIS G E. Modeling uncertainty in flow simulations via generalized polynomial chaos［J］. Journal of Computational Physics， 2003， 187（1）： 137-167. |
| 15 | HUANG M， LI Z G， LI J. Robustness analysis on the aerothermal performance of turbine blade squealer tip［J］. Journal of Engineering for Gas Turbines and Power， 2022， 144（7）： 071011. |
| 16 | SPURR A. The prediction of 3D transonic flow in turbomachinery using a combined throughflow and blade-to-blade time marching method［J］. International Journal of Heat and Fluid Flow， 1980， 2（4）： 189-199. |
| 17 | SIMON J F. Contribution to through?ow modelling for axial ?ow turbomachines［D］. Belgium： University De Liege， 2007. |
| 18 | TADDEI S R， LAROCCA F. Euler-based throughflow method for inverse design and optimisation of turbomachinery blades［J］. Progress in Computational Fluid Dynamics， an International Journal， 2014， 14（2）： 71-82. |
| 19 | BROUCKAERT J F， VERSTRAETE T， ALSALIHI Z， et al. Preliminary design and off-design optimization of a single stage axial compressor［C］∥Proceeding of the 22nd International Symposium on Air Breathing Engines. Phoenix： ISABE， 2015： 20102. |
| 20 | SCHNOES M， VO? C， NICKE E. Design optimization of a multi-stage axial compressor using throughflow and a database of optimal airfoils［J］. Journal of the Global Power and Propulsion Society， 2018， 2： W5N91I. |
| 21 | PANIZZA A， RUBINO D T， TAPINASSI L. Efficient uncertainty quantification of centrifugal compressor performance using polynomial chaos［C］∥Proceedings of ASME Turbo Expo 2014： Turbine Technical Conference and Exposition. New York ： ASME， 2014： V02BT45A001. |
| 22 | DRELA M， YOUNGREN H. A user’s guide to MISES 2.53［M］. Massachusetts： Massachusetts Institute of Technology， 1998. |
| 23 | TECZA J， MENEGAY P， KOCH J. Modeling and evaluation of centrifugal compressor performance variations using probabilistic analysis［C］∥Proceedings of ASME Turbo Expo 2005： Power for Land， Sea， and Air. New York： ASME， 2008： 1081-1088. |
| 24 | PANIZZA A， IURISCI G， SASSANELLI G， et al. Performance uncertainty quantification for centrifugal compressors： Part 1—stage performance variation［C］∥ Proceedings of ASME Turbo Expo 2012： Turbine Technical Conference and Exposition. New York ： ASME， 2013： 1863-1872. |
| 25 | BARALON S， ERIKSSON L E， H?LL U. Validation of a through?ow time-marching ?nite volume solver for transonic compressors［C］∥ASME 1998 International Gas Turbine and Aeroengine Congress and Exhibition. New York ： ASME， 1998： V001T01A015. |
| 26 | LI C C， LUO J Q， XIAO Z L. A throughflow-based optimization method for multi-stage axial compressor［J］. AIP Advances， 2021， 11（11）： 115207. |
| 27 | JOHNSEN I A， BULLOCK R O. Aerodynamic design of axial-?ow compressors： NASA SP-36 ［R］. Washington， D.C.： NASA， 1965： 183-252. |
| 28 | CARTER A D S. The low speed performance of related aerofoils in cascades［C］∥Aeronautical Research Council Current Papers. London： His Majesty’s Stationery Office，1950. |
| 29 | KOCH C C， SMITH L H Jr. Loss sources and magnitudes in axial-flow compressors［J］. Journal of Engineering for Power， 1976， 98（3）： 411-424. |
| 30 | CETIN M， UECER A S， HIRSCH C， et al. Application of modi?ed loss and deviation correlations to transonic axial compressors： AGARD-R-745［R］. Neuilly-sur-Seine： AGARD， 1987. |
| 31 | AUNGIER R， FAROKHI S. Axial-flow compressors： A strategy for aerodynamic design and analysis［J］. Applied Mechanics Reviews， 2004， 57（4）： B22. |
| 32 | LUO J Q. Design optimization of the last stage of a 4.5-stage compressor using a POD-based hybrid model［J］. Aerospace Science and Technology， 2018， 76： 303-314. |
| 33 | XIA Z H， LUO J Q， LIU F. Performance impact of flow and geometric variations for a turbine blade using an adaptive NIPC method［J］. Aerospace Science and Technology， 2019， 90： 127-139. |
| 34 | MARSHALL G. Monte Carlo methods for the solution of nonlinear partial differential equations［J］. Computer Physics Communications， 1989， 56（1）： 51-61. |
| 35 | SOBOL’ I M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates［J］. Mathematics and Computers in Simulation， 2001， 55（1-3）： 271-280. |
| 36 | SALTELLI A. Making best use of model evaluations to compute sensitivity indices［J］. Computer Physics Communications， 2002， 145（2）： 280-297. |
| 37 | DENTON J D. Loss mechanisms in turbomachines［J］. Journal of Turbomachinery， 1993， 115（4）： 621-656. |
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